What are the best ways to use remote sensing data for urban land use changes?

#Availability_of_GIS_Data_is_Dictator I would like to say that before you even strategise the project agenda you should do a prestudy of available Spatial and temporal datasets. It does have core importance, You can be stuck if mishandle this portion because we are dealing with a world where we have limited resources like coverage of satellites, Spatial resolution, temporal resolution, spectral resolution, deadline, budget.etc

Using remote sensing for urban land use changes involves categorizing land cover, analyzing multi-temporal imagery for changes over time, and assessing urban vegetation health. It includes mapping surfaces to quantify urbanization, studying land surface temperatures for heat island identification, and generating 3D models. The approach combines remote sensing with socio-economic data for suitability analysis, monitors urban infrastructure changes, and analyzes land use changes in relation to economic and social factors. Additionally, it assesses flood risk, supports smart city initiatives for efficient urban development, and establishes periodic monitoring using satellite imagery for updates.

Selecting right data also depends on what is the extent of your study area. If the extent is large, you can go for coarse-resolution data like MODIS, But if your study area is small, then you need to go for high resolution data like Landsat series or sentinel series or go for paid ones. The resolution factor is important because then you can see. You need to have an appropriate boundary for the study area especially if you are studying cities, or municipal corporations.

During land use analysis, various factors must be considered, such as season/month, cloud cover, image resolution, elevation, and the number of bands in the image. To achieve better results: Different band combinations can be used to extract or collect samples of different classes. For example, in Landsat/Sentinel series, false color combinations can be considered for vegetation and water. For supervised classification: The number of land use training samples should not deviate significantly. Check all accuracies, including kappa statistics, to gain understanding. Unsupervised and object-based classification can be applied based on the city. Above all, it's essential to be well acquainted with the urban area before doing LU

For continuous change detection over large areas (cities), drones might not be very practical, and satellites, like Landsat, MODIS, or Sentinel 2 are the better choices, still, these optical sensors are hindered by cloud cover, so (IMHO) a blend with SAR (radar) in the urban environment could be a good solution, supported with AI/ML for the statistical analysis of the change (environmental data is noisy by nature). Also, time scales are important, if a yearly composite is sufficient then off-the-shelf urbanasation layers can be used too; Google, ESA, ESRI are producing yearly land cover/land use products. The urban areas in these products could be used as a baseline and then merged with other RS products to give a near-real-time estimate.

The choice of method depends on the nature of the data sources and the research question at hand. For instance, when classifying urban land use types, employing supervised or unsupervised classification methods like maximum likelihood or k-means may be necessary. On the other hand, if the goal is to quantify the scale and significance of urban land use changes, methods such as post-classification comparison or image differencing for change detection could be more appropriate.

type of sensors were used to acquire the data (e.g., satellite, LiDAR, aerial photography)? What spectral and spatial resolutions are available?What is the format of your data? Is it raw imagery, processed raster data, or something else? Image classification: Classifying different features in your data, such as land cover types, vegetation types, or water bodies. Change detection: Identifying changes in your data over time, such as deforestation, urban expansion, or crop growth. Accuracy assessment: Evaluating the accuracy of your results, such as how well your classified maps or change detection products represent the real world. Spatial statistics: Analyzing the spatial patterns of your data, such as clustering, dispersion, or hot spots.

Following the SAR side of the moon, the first thing is to preprocess the data. The preprocessing steps will prepare the data to derive relevant informations later. Regarding this, there are two techniques that can be suitable to map urban landscapes: 1. Backscattering coefficient (mostly Sigma0) 2. Polarimetric Decomposition Those two techniques can be applied in two differente scenes to map the land use changes regarding urban landscapes.

Once you have processed the data, analysed the urban land use changes, you can incorporate these data with buildings shapefile, roads shapefile, any industries in the vicinity. You can even overlay pollution data from sentinel 5p, OCO2 and any other pollution datasets to detect the point source pollution and analyse which landuse coincides with that source. You can also integrate the landuse with population data and then pollution data so you can study what amount of population is like to get affected by landuse changes and pollution.

Integrating remote sensing data with GIS, surveys, and field observations enriches urban land use analysis. For instance, combining satellite imagery with socioeconomic data helps unveil the driving factors behind changes. Overlaying pollution data with land use details identifies sources and potential impacts on populations. This holistic approach ensures a comprehensive understanding of urban dynamics, aiding informed decision-making for sustainable development.

The integration of remote sensing and GIS techniques in land-use mapping creates a powerful synergy that enhances our understanding of the Earth's surface dynamics. Together, they can provide valuable insights into a wide range of applications: Urban planning: Analyzing land use patterns helps urban planners identify areas for commercial, residential, or recreational development, ensuring efficient use of space. Environmental assessment: Monitoring changes in land use helps evaluate the impact of human activities on the environment, enabling effective conservation and sustainable development strategies. Other are Agricultural monitoring and Transportation planning:

Its always important to club the spatial (real time) information with remote sensing data as per clients requirements. The integration should be through but crisp to use. As well the data representation with appropriate indicators can highlights crucial analysis to client.

In my experience, the use of previously mapped urban extensions (in vector formats) can be a useful tool to study the zonal statistics of the urban areas. With a map of the region of interest, it can be extracted zonal statistics (mean, median, quartiles, etc) of the backscattering coeffcients within its limits. This information will reveal a pattern in the SAR response in those regions. In that case, anything that do not foloow this pattern can be considered a change.

Traditional static maps: Perfect for showcasing spatial relationships, land cover patterns, and change detection results. Use clear symbology, legends, and annotations for accessibility. Interactive maps: Enhance engagement with tools for zooming, panning, querying data layers, and overlaying information. Platforms like ArcGIS Online and Leaflet offer great functionality. Charts and Graphs: Time series graphs: Ideal for visualizing trends and variations over time, such as vegetation cover changes or temperature fluctuations. Thematic maps: Use choropleth maps to show variations in a single variable across geographic areas, or scatter plots to explore relationships between multiple variables.

Interactive maps and web-based applications are best way to communicate your urban land use change results as it allows citizens and other stake holders to stay informed on changes and impacts in their own location.

Anything visual, like infographics, will facilitate non-technical stakeholders to understand any complex terms, projects, experiments, and more. Yes, you may make changes according to different stakeholders, but the ground idea is always the same.

Representation of clear indicators with spatial information provides the clear picture of achievements/results/highlights/etc. Also the visualization of data with comparative dates (like time period like 2/5/10/20 years) provides the opportunity to compare LUCL over region effectively.

The visualization method you choose, depends on your results, the scope and the things you want to focus on. Certainly, a map, graphs, reports and presentations can help you with the way you choose to present your results and your scope. For example if you want to raise awareness of land changes and land encroachment you can also have a high quality map and some sentences or a graph to talk about it, but if you want to inform about a specific area for land encroachment a presentation is also a good idea.

Maps and data displays rely heavily on color selection. Colours that are both vibrant and easy on the eyes make it easy to digest data, see trends, and draw conclusions. Careful choice of colors makes things easier to understand and remember.

With the continuous advancements in remote sensing and GIS technologies, the future of land use mapping holds even more promise. Improved spatial resolution, enhanced data processing capabilities, and the integration of artificial intelligence are expected to further enhance the accuracy, efficiency, and usefulness of land use mapping processes. As a result, land managers will have access to more reliable and timely information, allowing for better informed and sustainable land use decisions.

Cross checking of information with spatial locations, local data & coordinate system. It gives edge to visual representation.

The urban changes should also apply for marine areas like port, harbour and specifically land reclamation areas for urbanisation. The use of remote sensing for land changes would help to monitor and supervise for maritime delimitation area issues. It can be a proof and monitoring system to avoid geopolitical escalation between two or more parties. Thus may be used for a huge case of maritime delimitation areas to help international tribunal and even local jurisdictions issues.