Land Use and Land Cover Mapping Methodologies

A Methodology For The Multitemporal Analysis Of Land Cover Changes And Urban Expansion Using Synthetic Aperture Radar (SAR) Imagery - A Case Study Of The Aburrá Valley In Colombia -- https://lnkd.in/gP4ZhhN9 <-- shared paper -- “The Aburrá Valley, NW region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography & dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning & sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics... The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management & environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting & understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation…” #GIS #spatial #mapping #SyntheticApertureRadar #SAR #remotesensing #multitemporalanalysis #landcover #landcoverchange #clustering #kurtosis #fuzzylogic #kernelbasedmethod #machinelearning #spatialanalysis #spatiotemporal #geostatistics #model #modeling #AburráValley #Columbia #urban #urbanexpansion #population #growth #topography #monitoring #satellite #sentinel #valley #landuse #distribution #infrastructure #building #roads #naturalresources #environmental #conservation #monitoring #multitemporal 

🌏 🛰️ Supervised Land Use Classification Using Remote Sensing and Machine Learning 🛰️ 🌏 Land use classification is critical in understanding how human activities shape the environment and guiding sustainable development. In this learning experience, I explored how satellite imagery, field data, and machine learning techniques can be combined to generate detailed land use maps for effective spatial analysis and decision-making. 📊 Methodology ✔️ Field Data Collection- Collected 60 ground truth points across five land use classes (Built-up, Vegetation, Paddy, Bare Land, and Water Bodies) using QField linked with QGIS. ✔️ Satellite Data Processing- Processed Landsat 8 imagery in Google Earth Engine (GEE) to extract spectral signatures for each land use class. ✔️ Classification Model- Applied the Support Vector Machine (SVM) algorithm in Google Colab for supervised classification. ✔️ Accuracy Assessment- Validated the classification using a confusion matrix, user/producer accuracy metrics, and the kappa coefficient from both manually and Google Colab. 📈 Results ▪️ The model achieved an overall classification accuracy of 65% with moderate agreement (Kappa = 0.55). ▪️ Built-up area showed higher classification accuracy (70.59 % producer accuracy, 66.67% user accuracy), while Bare Land and Paddy classes had relatively lower accuracy, highlighting areas for future improvement. 🔎 Why This Matters This study demonstrates how integrating ground truth data, satellite imagery, and open-source tools enables practical, cost-effective land use classification, especially valuable for resource and data-limited areas. These insights can guide urban planning, agriculture management, environmental protection, and disaster risk planning. By applying machine learning in geospatial analysis, this study helps connect data to real-world solutions, making it a valuable approach for building more sustainable and resilient communities. #GIS #RemoteSensing #LandUseClassification #MachineLearning #GoogleEarthEngine #QField #QGIS #SpatialData #GeospatialAnalysis #UrbanPlanning #SustainableDevelopment #DataDrivenPlanning

Following the publication of my paper here https://lnkd.in/eP4AfWty, I’m ready to share my Random Forest-based machine learning scripts for Land Use Land Cover (LULC) classification in Gaborone, Botswana. The model was enhanced using the following indices: NDVI (Vegetation), NDBI (Built-up Index), NDWI (Water Index), BSI (Bare Soil Index). These indices improve class separability, making the classification process more accurate. The scripts, especially for the 2005 classification, also include a scan line error correction function for Landsat 7 images and a pan-sharpening function to enhance radiometric resolution by merging high-resolution panchromatic data with multispectral bands. Access the scripts here: 🔗 https://lnkd.in/eYbQs3Dy #GIS #remotesensing #machinelearning #lulc #geospatialanalysis #gee

🌍 Predicting Future Land Use and Land Cover (LULC) with - MOLUSCE plugin I’ve just published a new YouTube tutorial where I demonstrate how to simulate and validate future Land Use & Land Cover using the MOLUSCE plugin in QGIS. In this video, I walk through the complete workflow: ✅ Data preparation (LULC, DEM, distance from rivers & roads, slope map) ✅ Correlation evaluation (Pearson’s, Cramér’s V, Joint Information Uncertainty) ✅ Area change analysis ✅ Transition potential modeling (Logistic Regression, Multi-layer Perceptron, Weights of Evidence) ✅ Running the simulation ✅ Validation 🔗 Watch the full tutorial on predicting future LULC here: 👉 https://lnkd.in/gR7UykpS 🔗 Link to my YouTube channel: 👉 https://lnkd.in/gttuvzD2 📂 Tutorials to download data from different sources 🔗 How to download DEM from USGS: 👉 https://lnkd.in/gzgbgYZW 🔗 How to download river, road, LULC, and DEM from Diva GIS 👉 https://lnkd.in/gqm5fAWG 🔗 How to download Landsat LULC from USGS: 👉 https://lnkd.in/g6dQ42QW 🔗 LULC Unsupervised classification: 👉 https://lnkd.in/gpyUq7gu 🔗 Use a shapefile to download any topography map from USGS: 👉 https://lnkd.in/gm5HQ2Sg ✨ Whether you’re a student, researcher, or GIS professional, this tutorial will help you understand how to predict and validate land cover. 📢 If you find this useful, please share or repost — it might help someone in your network! 🌟 Let’s learn, grow, and model better together! #QGIS #GIS #RemoteSensing #LULC #Geospatial #MOLUSCE #QGISTutorial #SpatialAnalysis #MachineLearning #WatershedManagement #ArcGIS #Hydrology #WaterResources #FreeCourse #BeramaAcademy #ClimateChange #HydrologicalModeling #Watershed #ANN