Remote Sensing Applications with ArcGIS Pro

This lecture introduces the ArcGIS Online platform, outlining its key features and differences from traditional desktop GIS software such as ArcGIS Pro. It begins with a discussion on the accessibility and basic functionalities of ArcGIS Online, highlighting its open platform nature and how users can sign in and work without a paid subscription for non-commercial purposes.

The session then explores how ArcGIS Online is designed to support collaboration through web-based GIS, enabling multiple users to work simultaneously on projects within organizational or public accounts. The differences in feature availability between free public accounts and paid subscription accounts are also examined.

The lecture also covers the two available map layouts in ArcGIS Online—the older map layout and the newer layout recently introduced by ESRI—along with a demonstration of preparing a simple map using the platform. Lastly, it introduces the ArcGIS Living Atlas as a valuable resource for geographic data.

Key topics covered in this lecture include:

• ArcGIS Online basics and accessibility

• Differences between ArcGIS Online and ArcGIS Pro desktop software

• Overview of ArcGIS Online map layouts (old and new)

• Account types: public free accounts versus paid subscription accounts

• Collaboration and multiuser capabilities on ArcGIS Online

• Introduction to hosting and sharing features

• Introduction to ArcGIS Living Atlas

Practical value for remote sensing and GIS applications:

• Enables users to access GIS tools without costly software subscriptions

• Facilitates collaboration on spatial data projects across teams

• Allows creation and sharing of interactive web GIS maps

• Provides access to authoritative geographic data through Living Atlas

By the end of this lecture, learners will understand how to navigate and utilize ArcGIS Online, appreciate its distinct advantages and limitations compared to desktop software, and gain foundational skills to create and share simple GIS web maps using this cloud-based platform.

This lecture guides you through the process of signing in and navigating the ArcGIS Online platform. Whether you have a free or subscription-based account, you will learn how to access ArcGIS Online by visiting ArcGIS.com and selecting the sign-in option.

After logging in, you will explore the platform’s Gallery where various public datasets are hosted and available for use in your spatial analyses. The lecture also introduces the ArcGIS Online Map Viewer, focusing on its interface and functionality, which closely resembles ArcMap software for users familiar with it.

This session emphasizes the cloud-based nature of ArcGIS Online, where all data and analyses are stored securely, allowing seamless interaction with other ESRI products like ArcGIS Pro without the need for local file transfers.

Key topics covered in this lecture:

• ArcGIS Online sign-in options for free and subscription accounts

• Using the Gallery to access publicly available datasets

• Opening and navigating the ArcGIS Online Map Viewer classic interface

• Loading and managing basemaps including Imagery Hybrid

• Understanding cloud storage and integration with ArcGIS Pro

• Differences between the classic and new map viewer interfaces

• Organization account features and private data management options

Practical value for remote sensing and GIS users:

• Enables efficient access to and use of spatial data from ArcGIS Online

• Facilitates analysis within a cloud environment, enhancing workflow productivity

• Supports integration of online data and analyses with desktop GIS tools like ArcGIS Pro

• Provides knowledge of different account types and their capabilities

By the end of this lecture, learners will be able to confidently sign in to ArcGIS Online, explore available data sets, use the map viewer's features, and understand how cloud storage supports collaborative and integrated GIS workflows.

This lecture explores the comprehensive map layout and tools available in ArcGIS Online, a cloud-based GIS platform. It focuses on how users can add and manage various geospatial data sets within their maps, enhancing visualization and analysis capabilities.

During the session, you will learn about different ways to incorporate data, including importing CSVs, KMLs, web services, GeoJSON, and more. The lecture also demonstrates the use of layers, Living Atlas, and other datasets available through organizational accounts.

The lesson also covers managing map layers by adjusting parameters such as color, transparency, and blending options, and highlights the advantages of cloud processing where data is handled on the server side to improve efficiency.

Key Topics Covered in This Lecture

• Adding various geospatial datasets like CSVs, KMLs, and web services to maps

• Exploring the Layers panel and organizational data sources

• Using the Living Atlas and ArcGIS Online datasets

• Managing map layers with transparency and blending options

• Understanding cloud-based processing for faster data rendering

• Working with attribute tables from shapefiles and CSV files

• Selecting and switching base maps like terrain slope and Sentinel 2 imagery

Practical Applications for Remote Sensing and GIS

• Efficiently integrating diverse geospatial data into online maps

• Utilizing publicly available and organizational data for spatial analysis

• Customizing map visualization to improve interpretation of spatial information

• Leveraging cloud infrastructure to handle large datasets with limited local computing power

By the end of this lecture, learners will be able to proficiently add and customize multiple data layers in ArcGIS Online, manage map parameters to enhance map usability, and understand the advantages of cloud processing for remote sensing and GIS workflows.

This lecture introduces how to prepare and manage maps using ArcGIS Online, focusing on various key tools and functionalities available within the platform. It guides learners through the process of interacting with data, applying styles, and saving workflows for seamless integration with ArcGIS Pro.

You will explore options such as adding charts, legends, and bookmarks to enhance map visualization. The workflow includes saving your projects in the cloud to ensure accessibility and sharing capabilities for collaboration.

Additionally, the lecture covers customizing map properties, applying processing templates, filtering datasets, and using effects to improve map appearance. It highlights tools for searching locations, sketching, measuring distances, and generating directions, empowering users to create informative and interactive maps.

Key topics covered in this lecture:

• Drawing charts and adding legends to maps

• Creating and managing bookmarks for easy navigation

• Saving workflows and accessing them in ArcGIS Pro

• Customizing map properties including styles and processing templates

• Filtering and applying effects to datasets

• Utilizing search, sketch, measurement, and direction tools

• Exporting high-quality maps for sharing or publication

Practical value for remote sensing and GIS users:

• Efficiently prepare and customize maps for analysis and presentation

• Leverage cloud storage for saving and sharing GIS workflows

• Create interactive web-based maps and applications for diverse uses

• Enhance map readability and communication through styling and effects

By the end of this lesson, learners will understand how to navigate ArcGIS Online’s capabilities for map preparation, styling, and saving, enabling them to streamline their GIS projects and share their results effectively across platforms.

This lecture introduces the ArcGIS Living Atlas of the World, a comprehensive repository provided by ESRI that contains global datasets, applications, and diverse geographic information available to the public. It explains how different user accounts, such as subscription-based, general, or free accounts, can access and visualize these datasets.

Special emphasis is placed on the advantages of having an organization account, which allows importing Living Atlas datasets directly into ArcGIS tools for further analysis. The lecture features practical examples, such as browsing Sentinel-2 data, world imagery, active hurricanes, and thermal hotspots including fire activities, demonstrating the variety of accessible information.

While the ArcGIS Online platform offers a cloud-based environment for data analysis and map preparation with many useful tools, it is clarified that it does not provide the broader analytical capabilities available in ArcGIS Pro. This sets the stage for learners to understand the relationship between these platforms and how Living Atlas serves as a valuable resource.

Key topics covered in this lecture:

• Overview of ArcGIS Living Atlas of the World

• Public availability and access through different account types

• Importing datasets from Living Atlas with organization accounts

• Examples of important datasets: Sentinel-2, world imagery, hurricanes, thermal hotspots

• Comparison of ArcGIS Online and ArcGIS Pro capabilities

Practical value in remote sensing and GIS:

• Access to global, updated datasets for spatial analysis

• Ability to enhance projects by integrating Living Atlas data

• Preparation of quality maps using ArcGIS Online tools

• Understanding platform limitations and when to use ArcGIS Pro

After completing this lecture, learners will understand what the ArcGIS Living Atlas is, how to access its resources through different accounts, and how to use these datasets for mapping and analysis within ArcGIS Online and ArcGIS Pro environments.

This lecture introduces ArcGIS Story Maps, a powerful tool designed for communicating geospatial data through immersive, narrative-driven maps. Story Maps allow users to combine interactive maps, text, and multimedia content to tell compelling stories that are easy to share or present in various formats, such as online or PDF.

We begin with an overview of the purpose and capabilities of ArcGIS Story Maps, highlighting their accessibility for users without extensive GIS knowledge. The lecture explains that full functionality requires a subscription account, and encourages learners to use trial access if needed. You will learn how Story Maps differentiate from traditional GIS mapping software by enabling easy publication and interactive storytelling on cloud platforms.

The course emphasizes the simplicity of creating custom story maps with pre-designed templates, straightforward tools for map integration, and options for personalization. You will explore the concept of digital storytelling and see how Story Maps empower anyone to become an effective storyteller with spatial data, regardless of technical background.

Key topics covered in this lecture:

• Introduction to ArcGIS Story Maps and their purpose

• Requirements for access and account types

• Unique features and benefits of Story Maps vs conventional GIS maps

• Examples of interactive storytelling with maps and multimedia

• Usage of templates for easy and consistent layout

• Customization options and digital storytelling concepts

• Integration with ArcGIS Online and Living Atlas resources

Practical value for remote sensing and GIS practitioners:

• Learn to present spatial data in engaging, accessible formats

• Create interactive maps that enhance communication of analysis results

• Develop professional story maps suitable for research, presentations, or organizational use

• Leverage Esri's cloud resources for sharing and publishing

By the end of this lecture, learners will understand the foundational concepts of ArcGIS Story Maps, be familiar with the workflow for creating story-driven maps, and be prepared to apply these techniques to enhance the presentation of their own spatial data projects.

This lecture explores an example of an ESRI Story Map designed to illustrate human reach and population patterns globally. The story map is carefully crafted to be accessible to everyone, whether or not they have specialized geographic or cartographic knowledge. It visually narrates the evolving human population and settlement patterns with interactive elements and engaging graphics.

Starting with a striking urban cover photo, the story map is divided into sections such as density, urbanization, night earth imagery, and human footprint, each helping users to grasp complex population data through an intuitive interface. The presenter guides the learner through population growth trends, highlighting historical data along with predicted urbanization rates using color coding and animation.

Users can interact with maps that detail population density across various regions, revealing the concentration of people in historically significant areas and the sparseness in harsh climates. Further examples show urban sprawl rates in different countries illustrated by changing color regions over time.

Key topics covered in this lecture:

• Introduction to the structure and sections of an ESRI Story Map

• Visualization of historical and projected human population growth

• Interactive map controls to explore population density data

• Examples of urbanization and human settlement patterns worldwide

• Use of graphics and animations to enhance storytelling

• Insights into population distribution influenced by geographic and climatic factors

• Connection to future lessons on land use science and time series analysis

Practical value in remote sensing and GIS:

• Learning how to communicate spatial data effectively using story maps

• Understanding how to present complex demographic changes through visual tools

• Gaining skills to create compelling narratives for GIS presentations

• Recognizing the importance of interactivity in geographic data storytelling

By the end of this lecture, learners will understand how to analyze and present population and urbanization data through ESRI Story Maps. They will be equipped to use story mapping techniques to visualize and communicate geographic information engagingly and clearly to a broad audience.

This lecture introduces you to creating a Story Map using the ArcGIS Story Map platform. You will learn how to navigate the Story Map website, access existing stories, and begin a new Story Map project. The lesson explains the different template options available for creating stories, including starting from scratch, sidecar, guided map tour, and explorer map tour.

The instructor guides you through the process of starting a Story Map from scratch, showing how to input key elements such as the main title, subheadings, and content blocks. You will become familiar with the interface, including the predefined fonts and sizes for headings, and learn how to add various content types such as text paragraphs and interactive buttons with hyperlinks.

This foundational knowledge will prepare you to design your own narrative stories supported by geographic data, which you will apply in later sections focused on urban sprawl and time series analysis.

Key topics covered in this lecture:

• Accessing and navigating the ArcGIS Story Map website

• Exploring existing Story Map examples and templates

• Starting a new Story Map from scratch and understanding available layouts

• Adding titles, subheadings, and customizable content blocks

• Inserting interactive buttons with hyperlinks

• Understanding server-based operation without extra software installation

Practical value for geographic storytelling and analysis:

• Develop skills to create engaging and interactive Story Maps

• Learn to structure geographic narratives effectively for an audience

• Apply Story Maps to present spatial and temporal analysis results

• Use buttons and multimedia to enhance user experience and navigation

By the end of this lesson, you will understand how to initiate and structure a Story Map project, equipping you with the ability to craft compelling multimedia narratives around spatial data using ArcGIS Story Map.

This lecture is part of the section on ESRI Story Map and focuses on the detailed layout and media integration available in Story Maps. It continues the practical workflow of building interactive story maps by exploring the different content segments and media options you can add to enrich your narrative.

You will learn how to use separators to organize your story into clear sections, and how to embed and customize maps, images, image galleries, videos, audio, and interactive web content. The lecture also covers advanced features like swipe layers and timeline tools to visualize changes and compare spatial data effectively.

In addition, the session explains the final steps to preview and publish your story map, including privacy options and design themes, ensuring your finished project can be shared appropriately and looks polished.

Key topics covered in this lecture:

• Use of separators to divide story map sections

• Inserting and customizing maps linked from ArcGIS Online

• Adding images, image galleries, video, and audio content

• Embedding web apps, dashboards, and social media content via embedded code

• Using swipe tools for comparing map layers

• Creating and configuring timelines for chronological events

• Previewing and publishing story maps with privacy and design settings

Practical value for remote sensing and GIS applications:

• Enables clear organization of complex spatial information into digestible story segments

• Facilitates the integration of multimedia content to create engaging, interactive presentations

• Supports the comparison and temporal analysis of spatial datasets using advanced Story Map features

• Provides options to share work publicly or restrict access to specific organizations or audiences

By the end of this lecture, you will understand how to effectively structure your Story Map layout and incorporate various types of media and interactive elements to present your remote sensing and GIS analyses in a compelling, user-friendly format. You will also be familiar with the preview and publishing process, enabling you to share your spatial stories with the right audience.

This lecture presents a practical example of creating a simple story map using ArcGIS Story Maps. The instructor demonstrates a project developed for a university assignment focused on the NOC Farm, illustrating how to efficiently design and publish a story map by integrating various geospatial data and visual elements.

The workflow covers the structure and layout of the story map, starting with a cover page featuring a background image, title, subtitle, and author details. It then progresses through several pages containing introduction, background context, literature review, and objectives, all presented in a coherent flow. Various types of content like images, maps, and interactive modules based on ArcGIS Online are introduced to enhance engagement.

The lecture emphasizes important details such as inserting captions for images and charts to provide better context and clarity. It also shows the integration of static maps produced in ArcGIS Pro and interactive maps imported from ArcGIS Online, allowing users to zoom, pan, and explore attribute data dynamically within the story map environment.

Key topics covered in this lecture:

• Designing a story map layout with multiple pages

• Using background images, titles, subtitles, and metadata

• Inserting static images and maps with captions

• Integrating interactive GIS maps from ArcGIS Online

• Navigating and interacting with the story map features

• Maintaining logical flow and structure

• Publishing and sharing the story map

Practical value of this lesson in geospatial storytelling:

• Create engaging and informative story maps that combine maps and multimedia content

• Showcase geospatial analysis results using intuitive layout and design

• Leverage ArcGIS Online to add interactive layers and enrich user experience

• Effectively communicate geospatial data and findings to diverse audiences

After completing this lecture, learners will be able to build simple but compelling story maps by combining static and interactive maps, annotations, and descriptions to present geospatial projects effectively and publish them for broad sharing.

Welcome to the first lecture of the Land Use Science section, focusing on the foundational concepts of Land Use Land Cover (LULC) analysis. This lecture establishes the essential understanding of how humans develop and utilize land, alongside the natural surface elements that cover it. You'll explore the distinction between land use—such as cultivated or industrial land—and land cover, which includes natural surfaces like glaciers or artificial ones like roads.

The lecture also delves into the broader land systems, encompassing land use, land cover, and ecosystems, highlighting their dynamic interactions driven by human activities. A key emphasis is on the rapid urbanization process occurring globally and its impact on land transformation.

With a review of research trends over the past 30 years, you will understand the rising scholarly interest in land use science particularly after the availability of Landsat satellite data, which has catalyzed various land use change studies.

Key Topics Covered in This Lecture

• Definition and differentiation of land use and land cover

• Overview of land systems and their components

• The impact of human activities, especially urbanization, on land transformation

• Historical research trends and significance of Landsat data in land use studies

• Challenges and environmental issues linked with land use change

• Introduction to classification methods with a focus on supervised machine learning algorithms

• Literature resources and examples for deeper learning and research

Practical Value for Remote Sensing and GIS Applications

• Prepares learners to interpret and differentiate land use and land cover data effectively

• Provides context for applying classification techniques in ArcGIS Pro

• Supports informed decision-making for land management and environmental monitoring

• Equips learners with research background to optimize analysis methods and algorithm choices

By the end of this lecture, learners will have a solid grasp of the foundational concepts of land use and land cover science, appreciate the importance of satellite data for analysis, and be ready to undertake practical LULC mapping and classification tasks using ArcGIS Pro, setting the stage for more advanced remote sensing applications.

Welcome to this lecture focused on downloading satellite data for land use science applications. You will learn how to access and retrieve satellite images directly from the USGS Earth Explorer website, a key data source for remote sensing projects.

This session guides you through selecting an area of interest, setting date ranges, and applying filters such as cloud cover to find the most relevant satellite images. The lecture also covers how to manually draw polygons or upload shapefiles to define your study area precisely, facilitating targeted data downloads.

After selecting your dataset, you will explore visualizing image footprints and overlays to verify image quality and cloud cover before downloading. The lecture highlights critical criteria for selecting images, including low cloud coverage, absence of radiometric distortions, and completeness of pixel data. Additionally, it explains the necessity of creating a free USGS account to access downloads.

Key Topics Covered

• Accessing and navigating the USGS Earth Explorer website

• Selecting location via map interaction or feature search

• Defining study areas using polygons or shapefile uploads

• Setting date ranges and cloud cover filters for image selection

• Previewing satellite image footprints and browse overlays

• Criteria for selecting quality satellite data

• Creating a USGS account for data download access

Practical Value for Remote Sensing and GIS

• Enable efficient and precise acquisition of satellite imagery tailored to land use analysis

• Develop skills to filter and evaluate data quality before download

• Prepare datasets suitable for subsequent preprocessing and classification in ArcGIS Pro

• Support urban land use and change detection studies with reliable historical satellite data

By the end of this lecture, learners will confidently download high-quality satellite images from USGS tailored to their study areas. They will understand how to prepare this data for further preprocessing and analysis within ArcGIS Pro, laying a foundation for advanced land use and urbanization research.

This lecture is a detailed introduction to the process of importing satellite images and applying preprocessing techniques within ArcGIS Pro, specifically for land use and land cover (LULC) analysis. It builds upon the previous lessons where we selected and downloaded satellite imagery, focusing here on ensuring the image quality and preparing the data for further analysis.

We begin by verifying the suitability of a selected satellite image based on cloud cover, which is crucial for obtaining clear, unobstructed data for urbanization studies. The lecture emphasizes selecting images with minimal clouds to ensure the best representation of the study area, which in this case is Tokyo, Japan. This careful selection impacts the accuracy of subsequent classification and spatial analysis.

Following the download from USGS's Landsat repository, the lecture guides through managing the image files, highlighting how the downloaded data comes compressed and must be decompressed before use. The workflow includes opening ArcGIS Pro, creating a new project, and integrating the uncompressed satellite data folder through the folder connection feature. This hands-on approach ensures that learners understand the environment setup necessary for handling remote sensing data in ArcGIS Pro.

The instructor demonstrates importing auxiliary data such as shapefiles to delineate administrative boundaries of the study area. Specifically, boundary shapefiles for Tokyo are imported from publicly available humanitarian data sources, allowing users to clip and isolate their area of interest effectively. The importance of verifying the spatial alignment of the shapefile with the satellite imagery using visual tools like the Swipe tool is stressed to ensure spatial accuracy before preprocessing.

Then, the lecture covers selecting individual spectral bands from the multispectral imagery. It shows how to load relevant bands such as red, green, blue, near-infrared, and short-wave infrared, which are critical for land cover classification tasks. The flexibility offered by downloading satellite images either as whole bundles or as individual bands through the USGS data portal is explained, providing learners with insights into efficient data handling strategies.

Finally, the lecture closes with practical considerations for preparing the loaded data for subsequent preprocessing steps. These initial steps set the foundation for the detailed classification algorithms and time-series analysis that will follow in the course, making this lecture a critical component for successfully advancing in remote sensing workflows within ArcGIS Pro.

Key Topics Covered in This Lecture

• Selection of high-quality satellite images with minimal cloud cover

• Downloading Landsat imagery from USGS

• Decompressing satellite image files for use in ArcGIS Pro

• Setting up a new project and adding folder connections in ArcGIS Pro

• Importing and using administrative boundary shapefiles for study area delineation

• Verifying spatial alignment of shapefiles with satellite imagery

• Loading individual spectral bands for land cover analysis

• Using the Swipe tool for image comparison and assessment

Practical Value for Remote Sensing and Land Use Analysis

• Mastering the initial steps of remote sensing data preparation within ArcGIS Pro

• Understanding how to manage large satellite image datasets efficiently

• Applying spatial data management skills to isolate and study urban areas

• Learning how to evaluate image quality to improve classification accuracy

• Gaining hands-on experience with real remote sensing datasets

• Developing competence in integrating multiple geospatial data formats

• Setting up workflows that can be adapted for multi-temporal and change detection analysis

By the end of this lecture, learners will be proficient in importing and preparing satellite imagery for detailed analysis in ArcGIS Pro. They will understand the importance of image quality, spatial alignment, and spectral band selection, laying the groundwork for effective land use classification and temporal change detection.

In this lecture, we focus on the essential preprocessing step of importing and preparing satellite data within ArcGIS Pro, specifically for a defined study area. The process begins by isolating the polygon of interest—in this case, the Tokyo boundary—using selection and export features, ensuring that subsequent analysis is focused precisely on the target area. This careful data isolation is crucial for accurate spatial analysis and reduces processing overhead by excluding irrelevant regions.

Once the study area is isolated, the next major task is compositing multiple spectral bands into a single multispectral image. The lecture highlights the practical choices and methods available for compositing, emphasizing the importance of selecting the correct bands for analysis. Here, the focus is on red, green, blue, and near-infrared bands, which are commonly used in land use and land cover studies. The instructor explains how to confirm the band information via external documentation such as the USGS website for Landsat 9, reinforcing the value of understanding the sensor characteristics behind the satellite data.

Two alternative workflows for creating the composite image inside ArcGIS Pro are presented. The preferred approach demonstrated involves directly importing all relevant bands into the composite band geoprocessing tool. This method is shown to be more efficient and user-friendly compared to importing each band individually and then compositing them later from within the map viewer. This practical tip enhances workflow efficiency, especially when handling large datasets.

Following the composite creation, the lecture transitions to managing large datasets by addressing image mosaicing. The concept of mosaicing is explained as the process of combining multiple image tiles into one seamless raster dataset. While mosaicing is not required when working with a single image tile, understanding when and how to mosaic is vital for real-world scenarios involving multiple satellite image tiles covering extensive study areas.

The final preprocessing step covered is clipping the composite raster to the boundary of the study area polygon. The clip operation is carefully demonstrated using the appropriate raster clipping tool within the data management toolbox, ensuring that the satellite image is spatially constrained to Tokyo's limits. The clipped raster allows for focused analysis without extraneous data from outside the area of interest.

The lecture concludes by demonstrating visualization enhancements within ArcGIS Pro such as adjusting band combinations to produce false color composites. These visualization techniques help students better interpret multispectral data and prepare for the classification analysis that follows. Although more advanced preprocessing steps like cloud removal and radiometric corrections are acknowledged, they are reserved for more specialized workflows beyond this introductory segment.

Key topics covered in this lecture

• Selecting and exporting polygons for focused study areas

• Understanding and choosing spectral bands (red, green, blue, near infrared)

• Creating multispectral composites using the Composite Bands tool

• Efficient band importing workflows within ArcGIS Pro

• Concept and considerations of image mosaicing

• Clipping raster data to shapefile boundaries

• Visualizing composite images with band combinations and false color composites

• File management practices in geodatabases versus shapefiles

Practical value within remote sensing and land use analysis

• Data preparation techniques that improve processing efficiency and accuracy

• Hands-on skills to manipulate satellite image bands critical for classification

• Methods to spatially constrain analysis to areas of interest

• Understanding of ArcGIS Pro geoprocessing tools essential for remote sensing workflows

• Ability to customize and interpret multispectral images for better thematic understanding

• Best practices for managing spatial data file formats in GIS projects

• Foundation for subsequent land use classification and change detection analyses

After completing this lecture, learners will confidently manage satellite imagery preprocessing in ArcGIS Pro, from isolating study areas to producing multispectral composites and clipped images. They will be prepared to proceed with advanced classification tasks using clean and focused datasets tailored to their land use science studies.

In this lecture, we explore the process of performing land use classification in ArcGIS Pro, focusing on the practical application within the context of remote sensing. Land use classification is a critical step in interpreting satellite imagery and turning raw data into meaningful spatial information about land cover types.

The lecture begins by introducing the two primary types of classification techniques: unsupervised and supervised classification. Unsupervised classification relies on the software autonomously grouping pixels based on spectral similarities without prior knowledge of the labels, making it suitable when labeled training data is absent. On the other hand, supervised classification uses training data that instructs the algorithm with known examples of land cover types, resulting in more controlled and targeted classification outcomes.

After discussing these foundational concepts, the focus shifts to deciding the classification method most appropriate for the given dataset. The lecture describes the distinction between pixel-based and object-based classification, explaining how object-based techniques typically provide improved accuracy but require high-resolution imagery (less than 5 meters). Since the available Landsat 9 imagery has a moderate 30-meter spatial resolution, pixel-based classification is selected as the feasible option.

Next, the lecture guides learners through practical ArcGIS Pro steps: selecting the clipped raster layer prepared in prior preprocessing, accessing the imagery tab, and opening the classification wizard which is enabled only when a suitable raster layer is active. Visualization tips are offered, such as switching the basemap to imagery hybrid and using the swipe tool to compare the satellite image with the base map, allowing the learner to better understand the patterns of urban areas, vegetation, water bodies, barren land, and other land uses.

The classification wizard itself is thoroughly explained: the selection of classification type (supervised), choosing pixel-based classification, and loading a default classification schema. The schema acts as a template for assigning categories to land cover types, and users are reassured that it can be modified based on the study area's characteristics. The lecture demonstrates how to customize this schema by observing the study area to identify water, urban, barren, and vegetation classes, deleting unnecessary default classes and adjusting class values and colors for better clarity (red for developed land, blue for water, green for vegetation, and appropriate value codes).

Finally, learners are shown how to save their customized classification schema locally for reuse in future projects, ensuring efficient workflows and consistency across classifications.

Key topics covered in this lecture:

• Overview of land use classification types: supervised vs. unsupervised

• Differences between pixel-based and object-based classification

• Considerations for selecting classification methods based on satellite image resolution

• Step-by-step use of ArcGIS Pro classification wizard

• Visualization techniques using imagery basemap and swipe tool

• Customizing classification schema: adding, deleting, and editing classes

• Setting class colors and values for clear land use interpretation

• Saving classification schemas for reuse

Practical value of this lecture in remote sensing and GIS:

• Enables learners to perform accurate land use classification using moderate resolution satellite imagery

• Allows customization of classification categories to match study area specifics

• Builds proficiency in ArcGIS Pro tools critical for raster image analysis

• Facilitates interpretation of urban, vegetation, water, and barren land types

• Introduces effective workflows for preprocessing and classification preparation

• Supports data-driven decisions through meaningful land cover maps

• Prepares learners for advanced remote sensing tasks such as change detection and environmental monitoring

Upon completing this lecture, learners will understand how to effectively apply supervised pixel-based classification techniques in ArcGIS Pro to categorize land cover types within a study area. They will be able to customize classification schemas tailored to their project needs, use visualization tools to interpret satellite imagery, and prepare classified outputs ready for further analysis or presentation.

This lecture focuses on the detailed process of performing land use classification using the Classification Wizard tool in ArcGIS Pro. Building upon the color schema created in previous steps, learners are guided through collecting training and validation samples critical for classifying different land use types within the study area. The instructor demonstrates how to effectively gather samples using tools such as the rectangle selector and explains the importance of considering the proportional distribution of land use classes when deciding the number of samples for each type.

The workflow includes merging individual sample selections into unified classes to streamline the classification process. Emphasis is placed on methodological decisions such as choosing appropriate training samples and maintaining accuracy throughout the classification. This is vital as mislabeling samples can degrade the classifier’s performance and lead to inaccurate results.

Further, learners are introduced to four different classification algorithms available in ArcGIS Pro’s tool: Maximum Likelihood, Random Trees, Support Vector Machine, and K-Nearest Neighbor. The lecture highlights selecting the Random Trees classifier as a practical example, explaining how to run the classification and interpreting the preliminary output. Viewers are taught how to evaluate classification effectiveness visually, including recognizing key land cover categories such as urban areas, water bodies, forest land, and barren land based on color-coded raster images.

Attention is given to troubleshooting and refining results. The instructor advises on modifying sample numbers or classifier parameters, such as maximum tree depths in Random Trees, to improve classification fidelity when initial outcomes are unsatisfactory. This iterative approach ensures learners develop strategies to optimize classification accuracy in real-world GIS projects.

After a successful classification preview, the lecture guides through finalizing the process by naming outputs, running the full classification, and performing optional steps like merging classes or reclassifying specific land use types. This provides learners versatile skills to tailor the classification outcome according to their analysis goals.

The session concludes with a review of the final classified image representing four main land use classes: forest (vegetation), urban (developed areas), barren land, and water, each distinctly visualized. While the accuracy assessment and area estimation features are noted as part of the Classification Wizard, the lecture remains focused on mastering the classification procedure itself.

Overall, this lecture delivers a comprehensive, practical understanding of land use classification in ArcGIS Pro, preparing learners to confidently apply these techniques for environmental, urban planning, and resource management projects using remotely sensed data.

Key topics covered in this lecture:

• Collecting training and validation samples using selection tools

• Merging multiple samples into unified land use classes

• Overview of classification algorithms available in ArcGIS Pro

• Step-by-step classification process using Random Trees

• Evaluating and interpreting classification results visually

• Troubleshooting classification with parameter tuning

• Finalizing classification output and optional class merging

• Visualization of land use classes in a classified raster image

• Understanding classification limitations and preview concepts

Practical value in the domain of Remote Sensing and GIS land use analysis:

• Gain hands-on experience performing supervised land use classification

• Learn how to prepare and use training data effectively for classification

• Understand selection and tuning of classification algorithms in ArcGIS Pro

• Develop skills to interpret classification maps for environmental applications

• Acquire problem-solving approaches to improve classification accuracy

• Learn to customize classification outputs to meet project needs

• Prepare classified data sets usable for further mapping and analysis workflows

By completing this lecture, learners will be able to conduct a land use classification of satellite imagery within ArcGIS Pro, employing appropriate training samples, selecting classifiers, and refining outputs to produce meaningful, visualized land cover maps essential for geospatial analysis and decision-making.

In this lecture, we focus on creating publication-quality land use land cover (LULC) maps using ArcGIS Pro, a critical step in communicating spatial analysis results effectively. Building upon previously prepared LULC raster data, the lesson guides you through the entire map-making workflow from selecting the appropriate layout template to final export, ensuring your maps meet professional and publication standards.

We start by exploring how to initiate a new layout in ArcGIS Pro, highlighting the importance of choosing the right page size and orientation, such as A4 portrait or landscape, based on your study area's characteristics and publication requirements. This ensures that the map will fit well within journal formats or presentation media. The lecture then demonstrates how to insert a map frame into the layout and how to load your prepared LULC data into this frame, essential for framing your study area accurately on the map.

Technical aspects of map composition are discussed in detail. You learn how to add essential cartographic elements such as the north arrow, scale bar, and legend—components that improve map readability and user interpretation. The lecture explains how to move and zoom the map within the frame, providing flexibility to focus on relevant spatial extents. Adding custom grids is also covered, showing how to insert, customize grid lines, and adjust label placements to enhance the geographic referencing of the map without cluttering its visual appearance.

The lecture further emphasizes customization options, such as adjusting grid colors to hide lines if preferred, and aligning coordinate labels for a polished look. Placement of the north arrow and scale bar is covered with practical demonstrations to avoid clutter and maintain balance in the layout. The legend customization process includes turning off unwanted image frames and properly titling the legend to reflect the LULC classification scheme used, which is vital for clear map interpretation.

Adding a descriptive map title concludes the design phase, with tips on inserting and formatting text within ArcGIS Pro. Font sizing and placement are fine-tuned to ensure the title is legible and complements the overall map design without overpowering other elements.

The final segment of the lecture focuses on exporting your map for sharing and publication. The export process involves choosing file formats like PNG or TIFF, with a discussion on file size and quality considerations. You learn how to set resolution (DPI), favoring 300 DPI for high-quality printed outputs and 72 DPI for digital viewing, ensuring the exported map suits your intended medium. The lecture also guides you on color depth settings for further editing in external graphic software, providing flexibility for advanced post-processing.

This comprehensive approach, from layout selection through export, equips you with practical skills to produce visually appealing, accurate, and publication-ready land use land cover maps in ArcGIS Pro. It reinforces the critical role of good cartographic design and technical proficiency in remote sensing applications.

Key topics covered in this lecture:

• Creating new map layouts and selecting appropriate page size and orientation

• Inserting and adjusting map frames within the layout

• Adding and customizing cartographic elements: north arrow, scale bar, and legend

• Implementing and modifying map grids and coordinate label placement

• Adding and formatting descriptive map titles

• Exporting maps with high resolution and suitable file formats for publication

• Adjusting color depth and resolution settings for further editing

Practical value in remote sensing and GIS mapping:

• Produce professional-quality maps suitable for academic publications and presentations

• Ensure accurate visual representation of land use land cover data

• Enhance map readability through effective use of cartographic elements

• Customize grid and label settings for precise geographic referencing

• Optimize maps for various delivery media: print, web, and further graphic editing

• Learn export settings to achieve the best balance between image quality and file size

By completing this lecture, learners will be able to confidently prepare, customize, and export publication-ready land use land cover maps in ArcGIS Pro, reinforcing their capability to convey spatial data effectively for research, reporting, and decision-making.

Welcome to this lecture focused on the foundational concepts of time series analysis, especially as it applies to urban sprawl studies. We begin by discussing the general principles of time series, analyzing data across multiple years to uncover patterns and trends.

Next, the lecture introduces urban sprawl, highlighting how urban areas expand over decades and why monitoring this growth is crucial for resource allocation and urban planning. We also explore the practical use of change detection techniques to assess temporal and spatial changes using satellite imagery.

The lecture workflow includes visualizing data trends with graphs and maps, analyzing multi-year land use and land cover (LULC) datasets, and interpreting spatial patterns from these time series to support urban development studies.

Key topics covered in this lecture:

• Introduction to time series analysis concepts

• Visualization of temporal data through graphs and maps

• Understanding urban sprawl and its significance

• Change detection techniques for multi-year satellite data

• Temporal and spatial pattern analysis in urban growth

• Mapping urban expansion over decades

• Preparing multi-year LULC datasets for time series analysis

Practical value for remote sensing and GIS applications:

• Enables monitoring of urban development trends over time

• Supports decision-making for urban and resource planning departments

• Provides methodologies for preparing and analyzing multi-year satellite data

• Facilitates visualization and communication of findings through maps and story maps

By completing this lecture, learners will understand how to perform time series analysis for urban sprawl, apply change detection techniques, and visualize multi-temporal data to identify urban growth patterns. This foundation prepares you for practical workflows in ArcGIS Pro to support sustainable urban planning using remote sensing data.

In this lecture, we continue the journey of time series analysis for urban sprawl by preparing Land Use Land Cover (LULC) maps for multiple years. Starting from the LULC map of Tokyo for 2022, which was created using a Landsat 8 satellite image in a previous module, the focus shifts to gathering and processing satellite imagery for an earlier year to enable comparative temporal analysis.

The lecture emphasizes the critical importance of selecting multiple years’ satellite imagery for time series analysis. Specifically, the instructor selects the Landsat 5 satellite imagery from the early 1990s, around 1990, as the base layer for long-term urban expansion studies. The steps for filtering and choosing appropriate images are covered in detail, including setting parameters such as sensor type, temporal window (February through May), and cloud coverage tolerance (20-40%). This ensures data consistency and maximum clarity by minimizing cloud interference.

Next, the lecture discusses verifying the path and row information of the satellite images. This ensures that all images precisely cover the same geographic extent; using mismatched path/row coordinates would lead to incomplete or inconsistent coverage of the study area, making time series analysis unreliable. After identifying the proper image with optimal conditions (no cloud cover) and matching path/row alignment, download options are explored, and all available spectral bands are downloaded to support comprehensive processing and analysis in later stages.

Following data acquisition, the instructor demonstrates the familiar preprocessing steps used before, including compositing different spectral bands and clipping the imagery to the defined study area. These preprocessing methods prepare the satellite imagery for further analysis and ensure consistency and comparability between different years.

The lecture then transitions to the land use classification process within ArcGIS Pro. It details entering the Classification Wizard and selecting the imagery for classification. To maintain consistency with previous work, the same classification scheme and pixel-based classification type used in the earlier 2022 classification are applied here as well. The previously developed color schema is loaded to label different land cover classes uniformly, ensuring comparability across datasets.

Training samples for different land cover classes are collected in this lecture. While the reference data sets are deferred to the following lecture for collection, the process stresses the need to gather representative training data for each class, a foundational step for any supervised classification.

For classification, the lecture highlights the importance of using consistent parameters for the classifier in a time series context. The Random Trees classifier is chosen, maintaining the default values for maximum tree depth and sample number, identical to those applied in prior classifications. The classification results preview allows visual inspection to verify key land cover features such as water bodies, urban areas, and barren land, confirming that the classification output is of high quality and consistent.

Finally, the classification results are saved, and while some further processing like merging classes is mentioned, the lecture concludes with the generation of two final raster LULC maps for 1990 and 2020. The instructor reviews basic concepts covered so far in time series analysis including urban sprawl and change detection, setting the stage for the next lecture where learners will estimate the area of each LULC class over time.

Key topics covered in this lecture:

• Selection and filtering of multi-year Landsat satellite imagery

• Verification of path/row coordinates for consistent spatial coverage

• Downloading multispectral band data for analysis

• Preprocessing steps: band compositing and clipping imagery

• Loading consistent classification schema and color scheme

• Collecting training samples for supervised classification

• Use of Random Trees classifier with consistent parameters

• Reviewing and validating classification results

• Saving classified LULC raster maps for different years

• Fundamentals of time series analysis and urban sprawl change detection

Practical value in remote sensing and urban analysis:

• Developing skills to prepare LULC maps from different time periods for temporal studies

• Understanding how to ensure data consistency across multi-year satellite datasets

• Applying preprocessing techniques to clean and standardize imagery

• Using ArcGIS Pro’s classification tools for supervised land cover mapping

• Learning to collect and manage training samples effectively for classification accuracy

• Gaining experience in setting classifier parameters to maintain comparability

• Interpreting and validating classification outputs for reliable urban sprawl monitoring

• Creating foundational datasets to enable change detection and time series quantification

By completing this lecture, learners will be able to confidently acquire, preprocess, classify, and prepare LULC maps across multiple years, a crucial step in conducting robust time series analyses for urban growth and environmental change studies using ArcGIS Pro.

This lecture builds on the previous steps where we performed land use land cover (LULC) classification for the years 1990 and 2022 using Landsat satellite imagery. With two classified raster datasets prepared, the focus now shifts to accurately estimating the area covered by each LULC class for these years. This is a fundamental step for quantitative analysis of changes in land use, especially in the context of urban sprawl studies.

We begin by preparing the 1990 LULC map using the established layout from 2020, adjusting the raster data and legend to reflect the 1990 classifications. Once the maps are visually ready, they are exported to preserve the work. The next crucial task is understanding the importance of raster projection systems. It's vital that the raster layers are in a projected coordinate system, such as UTM, rather than a geographic coordinate system, to ensure area calculations are accurate and units are consistent.

The workflow continues by converting the LULC raster data to vector polygon format. This conversion is necessary because area calculations are more straightforward and precise on vector polygons rather than raster cells. Using the Raster to Polygon tool within ArcGIS Pro, the raster is transformed, and a careful selection of the attribute field consistent with LULC classes is made. Verification of the polygon attribute table confirms the presence of relevant class names such as water, developed, barren, and vegetation.

For area calculation, new fields are added to the attribute table to store results in kilometers squared, alongside shape area already defined in meters squared. The Calculate Geometry feature is employed to populate these fields, converting the area units appropriately. The lecture covers two methods: using the tool to calculate areas directly in kilometers squared, and a simpler manual conversion dividing shape areas in meters squared by 1,000,000. This dual approach ensures accuracy and validation of results.

The same process is repeated for the 2022 land use land cover data, ensuring consistency in methodology across datasets. An important caveat addressed is handling satellite images with mismatched coverage extents, which may require mosaicking to combine images before analysis. However, this step is simplified in the example to maintain focus on the core process.

Finally, learners are guided through transferring area statistics into Excel for visualization and interpretation. Various chart options, especially bar charts, are demonstrated to illustrate changes in LULC classes between 1990 and 2022. Significant trends such as the increase in developed land and decrease in barren land and vegetation are highlighted. This visual analysis lays the foundation for interpreting urban growth patterns in Tokyo, quantifying urban sprawl as an increase of 455 square kilometers over 32 years.

Key topics covered:

• Preparation of LULC maps for different years

• Importance of raster projection systems for area calculations

• Conversion of raster LULC data to vector polygons

• Verification of attribute tables for class consistency

• Adding and calculating area fields in attribute tables

• Two methods for calculating area in square kilometers

• Managing satellite images with different extents using mosaicking

• Exporting data to Excel for charting and analysis

• Interpreting land use changes and urban sprawl metrics

• Basic formula application to quantify urban area increase

Practical value in remote sensing and urban analysis:

• Enables precise quantification of land use class areas over time

• Supports detection and measurement of urban sprawl trends

• Guides users in ensuring proper coordinate systems for accurate spatial analysis

• Demonstrates efficient data conversion workflows between raster and vector formats

• Equips learners with skills to prepare data for statistical and visual reporting

• Facilitates integration of GIS data with spreadsheet tools for advanced analysis

• Helps interpret environmental and urban landscape changes based on satellite data

• Improves ability to communicate spatial change through maps and charts

By completing this lecture, learners will confidently calculate land use land cover areas for different years, understand technical considerations like projection and data type conversions, and translate GIS data into meaningful statistics and visualizations. These skills are essential for robust urban sprawl and environmental monitoring projects using remote sensing and GIS technology.

In this lecture, we dive into the practical process of performing change detection in ArcGIS Pro using land use land cover (LULC) raster images for different years. The demonstration is based on comparing raster data from 1990 and 2022, highlighting key considerations such as handling incomplete satellite imagery and preparing datasets for analysis. You will learn how to manage issues like partial satellite coverage by mosaicking multiple images to create a comprehensive raster for your study area.

The workflow begins by loading both LULC layers into ArcGIS Pro and examining them side by side. This setup is crucial to visually compare changes over time before applying change detection methodologies. We then proceed to use the Change Detection tool available in the Imagery tab, which guides you through selecting appropriate settings for different types of change analysis: categorical change, pixel value change, or time series change. For this lesson, the focus is on the categorical change option, which enables the identification and quantification of changes between two thematic rasters — the LULC maps from 1990 and 2022.

Next, you’ll learn how to set the processing extent to match the area of interest properly, especially when one of the raster datasets may be incomplete. This step ensures the analysis is restricted to consistent spatial boundaries, improving the relevance and accuracy of the results. Additionally, filtering options allow you to focus exclusively on the classes that have changed, which is particularly useful for detecting specific urban expansion dynamics like urban sprawl.

We explore the customization of visual outputs through transition class color methods and smoothing filters. Specifically, applying a median smoothing filter helps reduce noise in the change detection raster, producing clearer, more interpretable results. You’ll be guided through naming and saving the output raster dataset appropriately for further use.

Once the change detection processing finishes, emphasis is placed on mapping and interpreting the results. The lecture shows how to overlay study area boundaries for geographic context and adjust symbology to highlight land cover conversions of interest. The visualization focuses on transitions into urban development, such as water, barren, and vegetated lands transforming into developed areas. This targeted view allows effective monitoring and assessment of urban sprawl patterns over the 32-year period.

Further, you’ll discover the importance of cleaning up the map legend by removing unused classes, sharpening the clarity of the final thematic map. The color coding of changes (e.g., red for barren to developed, green for vegetation to developed, blue for water to developed) enhances legibility and helps communicate your findings effectively.

The lecture also explains how to quantify the spatial extent of detected changes. By converting your raster change detection output to polygons, you can calculate geometry properties such as area. This step mirrors earlier processes used for LULC statistics, enabling detailed area estimations for each change class. Finally, the workflow includes exporting your maps and result charts, for example, saving Excel graphs as PNG images, with a preview of preparing a StoryMap to publish and share your urban sprawl analysis findings in a dynamic and interactive format.

Key topics covered:

• Preparation and handling of multi-year LULC raster datasets for change detection

• Use of ArcGIS Pro Change Detection tool with categorical change method

• Setting processing extent and filtering changes for focused analysis

• Application of smoothing filters and color methods to improve output visualization

• Visualization of land use transitions emphasizing urban sprawl

• Map legend editing to focus on relevant class changes

• Conversion of raster outputs to polygons for area calculation

• Exporting maps and data visualizations for reporting

• Introduction to publishing results via ArcGIS StoryMaps

Practical value in remote sensing and urban studies:

• Acquire hands-on skills conducting comprehensive change detection in ArcGIS Pro

• Understand how to preprocess and prepare multi-temporal satellite imagery for analysis

• Learn techniques for customizing change detection outputs for clarity and focus

• Develop capability to interpret and map urban sprawl based on land cover transformations

• Gain experience calculating spatial statistics and translating raster data into vector formats

• Build proficiency in preparing export-quality maps and charts for presentation

• Explore workflows for disseminating geospatial findings using ESRI StoryMaps

Upon completing this lecture, you will confidently perform change detection workflows in ArcGIS Pro, interpret complex land cover transitions, quantify spatial changes accurately, and effectively communicate your urban sprawl analysis results through maps and interactive story maps. These skills are essential for remote sensing applications in urban planning, environmental monitoring, and geographic research.

This lecture guides you through the process of publishing your urban sprawl analysis findings using ESRI Story Maps.

Starting with the creation of a new story map from scratch, you'll learn how to organize and present your spatial data effectively. The workflow focuses on adding titles, captions, and importing key maps and graphical representations generated during the analysis.

Although building a story map can include adding detailed background information and contextual text, this lesson emphasizes the straightforward import of land use land cover maps from the years 1990 and 2022, statistical graphs showing area estimates, and change detection results to visualize urban growth.

Key topics covered in this lecture:

• Opening and creating a new ESRI Story Map

• Naming the project with relevant study area and timeframe

• Importing previously exported land use land cover maps

• Adding captions to maps for clarity

• Inserting statistical graphs derived from analysis

• Presenting change detection maps showing urban expansion

• Basic workflow tips for structuring your story map

Practical value for remote sensing and GIS applications:

• Learn to effectively communicate spatial analysis results using Story Maps

• Gain hands-on skills in importing and organizing maps within ArcGIS Online

• Understand how to enhance visual storytelling with captions and graphs

• Prepare professional presentations of urban sprawl study findings

By the end of this lecture, you will be able to create a compelling ESRI Story Map that showcases your study area's urban sprawl changes clearly, combining spatial maps and statistical visuals for impactful storytelling.

Welcome to the lecture on the basic concepts of the Urban Heat Island (UHI) effect, the final section of this remote sensing course. This lecture introduces the phenomenon of UHI, a climate change-related issue where urban areas experience higher temperatures than their surrounding rural areas. Using remote sensing techniques and ArcGIS Pro, we aim to quantify and visualize this effect in urban contexts, with a focus on Tokyo.

In this lesson, you will learn about the underlying causes of the UHI effect, including the role of impervious surfaces like asphalt and concrete, reduced vegetation, and anthropogenic heat from vehicles and buildings. We will also cover the concept of surface urban heat, measured as the radiative temperature difference between urban and natural surfaces, and how it varies by time of day and season.

Thermal infrared remote sensing, particularly using Landsat 9 thermal bands, will be introduced as a key tool to measure land surface temperature (LST), which is essential to assessing UHI. You will see examples of temperature trends in cities worldwide and the practical implications of the heat differences between urban centers and their surroundings.

Key topics covered in this lecture:

• Definition and temperature dynamics of the Urban Heat Island effect

• Differences in surface properties causing temperature variations

• Measurement of surface urban heat using thermal infrared bands

• Sources and causes of UHI: impervious surfaces, vegetation loss, and anthropogenic heat

• Examples illustrating temperature rises in urban areas globally

• Introduction to land surface temperature (LST) as a measure of UHI

Practical value for remote sensing and GIS professionals:

• Understanding the application of ArcGIS Pro for UHI quantification and visualization

• Using Landsat 9 thermal bands to assess land surface temperature

• Recognizing urban heat patterns for informed environmental and urban planning

• Interpreting remote sensing data to analyze climate-related urban phenomena

By completing this lecture, you will have a foundational understanding of the Urban Heat Island effect, its causes, and how to apply remote sensing methods to measure and study it. This sets the stage for more advanced analysis and visualization techniques in subsequent lessons of the course.

In this lecture, we focus on evaluating Land Surface Temperature (LST) using thermal infrared data from the Landsat satellite within ArcGIS Pro. We begin by setting up a new project and importing the thermal infrared band, specifically band 10, which is critical for measuring surface temperature. This band captures energy emitted from Earth's surface, allowing us to estimate temperature variations across the landscape.

The process to convert the thermal band data into actionable LST involves several important steps, grounded in radiometric and atmospheric correction principles. We introduce the necessary mathematical formulae, starting with calculating Top of Atmospheric (TOA) spectral radiance using specific parameters like the band-specific multiplicative rescaling factor (ML), additive rescaling factor (AL), and a constant correction (OI). These parameters, retrievable directly from the metadata (MTL file) associated with the Landsat image, guarantee that the conversion is accurate and tailored to the dataset.

After extracting the constants from the MTL text file, we apply the radiometric corrections using the Raster Calculator in ArcGIS Pro. This tool is essential for performing the required image algebra, enabling us to execute the formula defining TOA spectral radiance pixel by pixel. Next, we proceed to convert TOA radiance into brightness temperature using thermal constants (K1 and K2), again sourced from the metadata. This step translates radiance into a temperature metric that reflects the apparent thermal condition of the surface.

In addition to evaluating LST, the lecture emphasizes the importance of calculating the Normalized Difference Vegetation Index (NDVI) which is crucial for emissivity correction. Emissivity reflects the surface's ability to emit thermal radiation and varies depending on the type and density of vegetation or land cover. We demonstrate how to calculate NDVI using bands 5 and 4 of Landsat 9, applying a classic NDVI formula through ArcGIS Pro's Raster Calculator. The resulting NDVI raster helps quantify vegetation cover and provides key parameters such as minimum and maximum NDVI values for further analyses.

Building on NDVI, the lecture explains how to compute the proportion of vegetation or fractional vegetation cover. This is achieved by manipulating NDVI values through image algebra, normalizing the vegetation signal across the study area. This fraction is used later to refine emissivity correction factors, ensuring that LST estimates reflect the actual surface conditions more accurately. Throughout the workflow, clear guidance is offered on handling raster properties, verifying equations, and managing outputs within ArcGIS Pro for reliable reproducibility.

The lecture workflow highlights the technical decisions to ensure data quality and interpretation accuracy, including attention to detail in inputting formula constants and validating outputs. Mistakes in calculation inputs can propagate errors through the analysis, so careful verification is stressed. Visualization using symbology helps interpret the NDVI results, differentiating between water bodies, dense vegetation, and other land covers within the study area.

Finally, this lecture lays the groundwork for further urban climate studies by precisely preparing thermal and vegetation datasets necessary for robust Urban Heat Island effect analysis using remote sensing data.

Key topics covered:

• Setting up ArcGIS Pro project for thermal data processing

• Identifying and loading Landsat thermal infrared band (band 10)

• Extracting radiometric calibration constants from MTL metadata files

• Calculating Top of Atmospheric spectral radiance using Raster Calculator

• Converting radiance to brightness temperature with thermal band constants

• Computing NDVI to assess vegetation cover

• Calculating fractional vegetation for emissivity correction

• Applying image algebra for raster processing

• Verifying and managing raster outputs in ArcGIS Pro

• Visualizing NDVI with meaningful symbology

Practical value in the course domain:

• Enables accurate estimation of land surface temperature from satellite data

• Improves thermal data processing skills within ArcGIS Pro for environmental analysis

• Integrates radiometric correction with practical use of metadata for precise calculation

• Demonstrates NDVI and fractional vegetation computation for emissivity correction

• Provides foundational techniques for Urban Heat Island and climate impact studies

• Enhances capability to interpret and validate remote sensing-derived temperature and vegetation metrics

• Builds proficiency in raster algebra and geospatial data manipulation workflows

• Supports advanced remote sensing applications using Landsat imagery

By completing this lecture, learners will grasp the workflow and technical details necessary to convert raw satellite thermal data into actionable temperature maps. They will also understand how to use vegetation indices to adjust thermal emissions, resulting in more accurate land surface temperature measurements critical for environmental and urban climate assessments.

This lecture focuses on the critical step of calculating land surface emissivity (LSE), a key parameter for accurate estimation of Land Surface Temperature (LST) using thermal satellite imagery. You will learn the formula to evaluate emissivity based on vegetation and soil emissivities as well as surface roughness. The lecture explains practical considerations for these inputs, including default values for vegetation and soil emissivity when exact data is unavailable, helping you make informed assumptions for your analysis.

The process workflow includes implementing the emissivity calculation within ArcGIS Pro's raster calculator, demonstrating step-by-step how to enter the equation and incorporate correction values that are particularly relevant for Landsat 8 and Landsat 9 thermal sensor data. This session also revisits prior calculated parameters such as top-of-atmosphere radiance, brightness temperature, NDVI, and fraction of vegetation, culminating in the integration of emissivity to complete all variables required for LST derivation.

Next, you will apply a map algebra expression based on Bepper's equation to calculate the emissivity corrected Land Surface Temperature. We walk through the formula in detail, clarifying the role of brightness temperature at the sensor, emissivity, and necessary correction factors. This highlights the importance of combining thermal data and emissivity to obtain reliable surface temperature maps.

To finalize, the lecture demonstrates how to clip the derived land surface temperature raster to a specific study area—Tokyo, Japan—using a shapefile to focus analysis on the urban extent. The clipped raster is then styled with appropriate symbology to visually distinguish temperature variations, revealing higher urban heat signatures relative to surrounding vegetated and forested areas. This practical visualization reinforces the real-world application of remote sensing for urban heat island effect studies.

The session emphasizes both the technical GIS operations and the interpretation of thermal patterns across heterogeneous landscapes, providing context on how LST maps can be used in environmental monitoring and urban planning.

Key topics covered:

• Formula and calculation of land surface emissivity (LSE)

• Use of raster calculator for emissivity and LST computations

• Integration of vegetation and soil emissivity values with correction factors

• Bepper's equation for emissivity corrected Land Surface Temperature

• Clipping raster outputs to study area boundaries in ArcGIS Pro

• Applying symbology for effective temperature visualization

• Interpretation of temperature variation in urban vs. natural environments

Practical value for remote sensing analysis:

• Empowers learners to accurately compute emissivity – crucial for thermal remote sensing

• Hands-on use of ArcGIS Pro raster calculator to process satellite thermal bands

• Understanding and applying correction factors specific to Landsat sensors

• Skills to focus analysis on geographic study areas via raster clipping

• Techniques for visualizing and interpreting land surface temperature variations

• Supports urban heat island effect assessments and environmental studies

• Provides essential workflows for preparing datasets for further spatiotemporal analysis

By the end of this lecture, learners will be able to calculate and correct land surface emissivity, integrate it with brightness temperature to produce precise land surface temperature maps, and tailor outputs to specific study areas for clear interpretation of urban thermal dynamics using ArcGIS Pro.

In this lecture, we focus on evaluating the Urban Heat Island (UHI) effect trends by analyzing Land Surface Temperature (LST) using ArcGIS Pro. The UHI effect describes the temperature difference between urban areas and surrounding natural landscapes, often leading to higher temperatures in cities. Evaluating these trends helps us understand the spatial distribution and intensity of heat islands across a study area, which is essential for urban planning and environmental management.

The workflow begins with creating a new shapefile specifically designed to capture the dynamics of UHI along a linear path. This involves defining a feature class of type "line" within the database. The shape file is intended to track temperature changes across a transect running from vegetated or forested zones to densely urbanized city centers, enabling visualization of temperature gradients.

Next, we add a new attribute field in the shapefile to record the land surface temperature values, ensuring the proper data type is set for accurate representation and analysis. Editing the shapefile involves drawing the line over the study area following the urban-to-rural gradient. This manual digitization step is crucial for precisely delineating the zones where temperature changes will be measured.

After establishing the UHI line, we leverage the "Stack Profile" tool in ArcGIS Pro. This tool extracts LST values along the line segment, generating a profile that quantitatively describes the temperature variation through space. The extracted data is saved in a new table named to reflect the UHI evaluation context, facilitating further analysis and visualization.

To interpret these temperature changes clearly, the lecture demonstrates how to create a line chart from the extracted profile table. Using object IDs as the horizontal axis (representing points along the line) and corresponding temperature values as the vertical axis, the chart illustrates the temperature trend across the urban-rural transition. Despite some scaling issues due to predefined constants in the temperature calculation, the graphical trend reveals a marked increase in temperature approaching the city center, highlighting the heat island effect clearly.

The lecture also discusses the importance of calibrating temperature constants, such as emissivity and atmospheric parameters, to obtain more precise LST measurements. The presenter suggests that for rigorous analysis, user-defined constants must replace generic values used in this demonstration to improve accuracy.

Moreover, an alternative approach to evaluating LST trends is introduced by recommending the use of Google Earth Engine's cloud platform. This platform offers powerful JavaScript-based scripting for advanced geospatial processing, including LST extraction, and is covered in AulaGEO's dedicated course on Google Earth Engine.

Key topics covered in this lecture:

• Creating a line shapefile to represent UHI transects

• Adding and configuring land surface temperature data fields

• Digitizing and editing the UHI line feature over the study area

• Using the Stack Profile tool to extract LST values along the UHI path

• Generating line charts to visualize LST trends spatially

• Discussing temperature scale challenges and calibration needs

• Introducing Google Earth Engine as an alternative LST processing tool

Practical value in the remote sensing and urban environment domain:

• Enables quantitative assessment of urban heat island intensity and distribution

• Supports spatial analysis of temperature gradients from natural to urban zones

• Provides hands-on skills with ArcGIS Pro tools for remote sensing data interpretation

• Facilitates visualization of UHI effects to inform urban planning decisions

• Encourages data accuracy through proper parameter calibration in LST analysis

• Offers knowledge of integrating cloud-based platforms for enhanced geospatial analysis

• Prepares learners for advanced environmental data analysis and urban climatology studies

Upon completion of this lesson, learners will understand how to create and manipulate spatial features to analyze urban heat island effects, extract land surface temperature profiles along defined transects, and visualize temperature trends using graphs in ArcGIS Pro. They will also appreciate the importance of precise parameter selection for accurate temperature assessment and will be aware of complementary tools, such as Google Earth Engine, to expand their remote sensing analysis capabilities.

Welcome to the final lecture of this Remote Sensing course using ArcGIS Pro, focused on evaluating Urban Heat Island (UHI) effects through normalized UHI and Urban Thermal Field Variance Index (UTFVI) assessments. Building on previous lessons where we analyzed UHI trends, this session introduces advanced methods to normalize and visualize surface temperature variations associated with urbanization.

The lecture begins by revisiting the concept of UHI and its relationship with land use types. Notably, forested and vegetative areas tend to exhibit significantly lower UHI values compared to highly urbanized impervious surfaces. To quantify this, the lecture teaches the application of a normalized UHI formula, which uses the mean and standard deviation of Land Surface Temperature (LST) within a study area to characterize spatial temperature hotspots more effectively.

In practical workflow terms, you will learn how to employ the Raster Calculator tool in ArcGIS Pro to implement this normalization formula. Key technical steps include extracting statistical properties like mean and standard deviation from the LST raster layer and inputting these values into the raster calculation expression. This process results in a normalized UHI raster highlighting critical thermal hotspots within the study area, exemplified here with Tokyo as a case study.

Once the normalized UHI raster is generated, guidance is provided on customizing layer symbology to facilitate interpretation. You will explore changing the color scheme and stretch types to reflect condition numbers, allowing clear differentiation between hotspot intensities. By overlaying Landsat 9 satellite imagery in false-color composite, you can visually correlate UHI hotspots to specific land cover types, such as water bodies which show minimal UHI, and urban infrastructure including airports which represent the highest UHI zones.

Extending the thermal analysis, the lecture introduces the UTFVI, an alternate index designed to evaluate thermal stress in urban environments. Though mathematically related to the normalized UHI, UTFVI modifies the equation’s division by using the total LST as denominator instead of the standard deviation. You are shown how to calculate and visualize UTFVI in ArcGIS Pro, observing notable differences in spatial differentiation compared to normalized UHI maps.

The comparative evaluation reveals that normalized UHI provides clearer distinctions of thermal gradients across urban landscapes, identifying a wider range of hotspot intensities. Conversely, UTFVI excels in highlighting areas of minimal thermal stress, such as cloud tops, snow cover, and water bodies. These complementary indices provide a nuanced understanding of urban thermal behavior in remote sensing studies.

Finally, the lecture covers exporting your UHI and UTFVI maps for integration into ArcGIS StoryMaps, enabling the creation of professional-grade spatial reports. The workflow includes sharing maps via ArcGIS Online for interactive exploration and combining raster layers for engaging visual storytelling. This comprehensive approach empowers you to present urban thermal environment findings effectively to diverse audiences.

Key topics covered in this lecture:

• Normalized UHI formula and its components (mean, standard deviation of LST)

• Using Raster Calculator in ArcGIS Pro for UHI normalization

• Symbology adjustment to visualize UHI hotspots

• Overlaying Landsat 9 imagery for land cover correlation

• UTFVI calculation and comparison with normalized UHI

• Interpretation of thermal maps for urban and vegetative areas

• Exporting and integrating thermal maps in ArcGIS StoryMaps

• Sharing interactive maps through ArcGIS Online

Practical value for remote sensing and GIS professionals:

• Learn to quantify urban thermal variations using normalized indices

• Gain expertise in ArcGIS Pro raster analysis and calculator tools

• Visualize and interpret complex temperature data with effective symbology

• Integrate multi-source satellite data for comprehensive urban studies

• Create engaging, interactive spatial reports for stakeholders

• Understand the strengths and limitations of UHI and UTFVI indices

• Apply hands-on workflows to real-world metropolitan case studies (Tokyo)

• Enhance remote sensing projects with robust thermal environment assessments

By completing this lecture, you will confidently evaluate urban heat island effects using advanced normalization methods and thermal indices. You will know how to generate, interpret, and communicate UHI and UTFVI maps through ArcGIS Pro and online platforms, equipping you with essential skills for sophisticated remote sensing applications in urban environmental analysis.