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check it in channel description

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// Load the Tamil Nadu boundary var tamilNadu = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level1') .filter(ee.Filter.eq('ADM1_NAME', 'Tamil Nadu')); // Center the map Map.centerObject(tamilNadu, 7); Map.addLayer(tamilNadu, {color: 'red'}, 'Tamil Nadu Boundary'); // Load environmental data // 1. Temperature (MODIS) var temperature = ee.ImageCollection('MODIS/061/MOD11A2') .filterDate('2023-01-01', '2023-12-31') .select('LST_Day_1km') .map(function(image) { return image.multiply(0.02).subtract(273.15) .copyProperties(image, ['system:time_start']); }); var meanTemperature = temperature.mean().clip(tamilNadu); // 2. Precipitation (CHIRPS) var precipitation = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') .filterDate('2023-01-01', '2023-12-31'); var totalPrecipitation = precipitation.sum().clip(tamilNadu); // 3. Vegetation Index (NDVI from Sentinel-2) var sentinel2 = ee.ImageCollection('COPERNICUS/S2') .filterDate('2023-01-01', '2023-12-31') .filterBounds(tamilNadu) .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) .map(function(image) { var ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI'); return image.addBands(ndvi); }); var meanNDVI = sentinel2.select('NDVI').mean().clip(tamilNadu); // 4. Water Bodies (JRC Global Surface Water) var waterOccurrence = ee.ImageCollection('JRC/GSW1_4/MonthlyHistory') .select('water') // Corrected from 'occurrence' to 'water' .mean() .clip(tamilNadu); // Combine the variables into a single multiband image var predictors = meanTemperature.rename('Temperature') .addBands(totalPrecipitation.rename('Precipitation')) .addBands(meanNDVI.rename('NDVI')) .addBands(waterOccurrence.rename('WaterOccurrence')); // Visualize the environmental layers Map.addLayer(meanTemperature, {min: 20, max: 40, palette: ['blue', 'yellow', 'red']}, 'Mean Temperature'); Map.addLayer(totalPrecipitation, {min: 0, max: 1000, palette: ['white', 'blue']}, 'Total Precipitation'); Map.addLayer(meanNDVI, {min: 0, max: 1, palette: ['brown', 'green']}, 'Mean NDVI'); Map.addLayer(waterOccurrence, {min: 0, max: 100, palette: ['white', 'blue']}, 'Water Occurrence'); // Sample the predictors for training data // Add training points based on known malaria cases or hotspots in Tamil Nadu var malariaCases = ee.FeatureCollection([ ee.Feature(ee.Geometry.Point([78.1198, 11.6643]), {'Malaria': 1}), // Example: Salem ee.Feature(ee.Geometry.Point([78.7047, 10.7905]), {'Malaria': 1}), // Example: Trichy ee.Feature(ee.Geometry.Point([79.1325, 12.9716]), {'Malaria': 1}), // Example: Chennai ee.Feature(ee.Geometry.Point([77.1025, 11.2558]), {'Malaria': 0}), // Example: Coimbatore ee.Feature(ee.Geometry.Point([78.7047, 9.9252]), {'Malaria': 0}) // Example: Madurai ]); // Overlay predictors on malaria cases var trainingData = predictors.sampleRegions({ collection: malariaCases, properties: ['Malaria'], scale: 1000 }); // Train a Random Forest Classifier var classifier = ee.Classifier.smileRandomForest(50).train({ features: trainingData, classProperty: 'Malaria', inputProperties: ['Temperature', 'Precipitation', 'NDVI', 'WaterOccurrence'] }); // Classify the region var malariaRisk = predictors.classify(classifier).rename('MalariaRisk'); // Visualize predicted malaria risk Map.addLayer(malariaRisk, {min: 0, max: 1, palette: ['green', 'red']}, 'Malaria Risk'); // Export predicted malaria risk map Export.image.toDrive({ image: malariaRisk, description: 'TamilNadu_Malaria_Risk_Map', scale: 1000, region: tamilNadu, fileFormat: 'GeoTIFF' });

Multi criteria analysis

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I m not currency expert it is out of topic

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SVM works best with enough, well-distributed training points. If your training data is sparse or skewed, errors can occur.

// Load a building footprint dataset (example: Microsoft or OSM via GEE) var buildingFootprints = ee.FeatureCollection("TIGER/2016/Buildings"); // Load DSM and DTM (example using Copernicus) var dsm = ee.Image("COPERNICUS/DEM/GLO30"); var dtm = ee.Image("USGS/NED"); // Calculate building height (DSM - DTM) var buildingHeight = dsm.subtract(dtm).rename('height'); // Add height to building footprints var buildingsWithHeight = buildingFootprints.map(function(feature) { var height = buildingHeight.reduceRegion({ reducer: ee.Reducer.mean(), geometry: feature.geometry(), scale: 30, }).get('height'); return feature.set('height', height); }); // Export to a CSV Export.table.toDrive({ collection: buildingsWithHeight, description: "Building_Height_Export", fileFormat: "CSV" });

Thank you for the GEE Code

I got this error message Collection.loadTable: Collection asset 'TIGER/2016/Buildings' not found. Please help me in downloading building height google building data . Thanks

@@surbhat The error you’re encountering indicates that the asset ID TIGER/2016/Buildings doesn't exist or is inaccessible in Google Earth Engine. This may be due to an incorrect asset ID or the dataset no longer being available.

check it with my video description

sure

yes of course

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In the SafePal app, find your USDT wallet.

// Define the region of interest (Chennai, India) var chennai = ee.FeatureCollection("FAO/GAUL/2015/level2") .filter(ee.Filter.and( ee.Filter.eq('ADM1_NAME', 'Tamil Nadu'), ee.Filter.eq('ADM2_NAME', 'Chennai'), ee.Filter.eq('ADM0_NAME', 'India') )); Map.centerObject(chennai, 10); // Load VIIRS DNB data (Nighttime Lights) for a specific year range (e.g., 2020) var viirsNightLights = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG") .filterBounds(chennai) .filterDate('2020-01-01', '2020-12-31') .select('avg_rad'); // Average radiance // Take the median to reduce noise across multiple months var nightLightsImage = viirsNightLights.median().clip(chennai); // Display the Night Light intensity layer Map.addLayer(nightLightsImage, { min: 0, max: 50, palette: ['black', 'yellow', 'red', 'white'] }, 'Urban Night Lights (2020)'); // Optionally, you can create a time series of night light intensity var startDate = '2015-01-01'; var endDate = '2020-12-31'; var timeSeries = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG") .filterBounds(chennai) .filterDate(startDate, endDate) .select('avg_rad') .map(function(image) { var year = image.date().get('year'); return image.set('year', year); }); // Create a time series chart of night light intensity for Chennai var nightLightChart = ui.Chart.image.seriesByRegion({ imageCollection: timeSeries, band: 'avg_rad', regions: chennai, reducer: ee.Reducer.mean(), scale: 500, seriesProperty: 'year' }) .setOptions({ title: 'Night Light Intensity in Chennai (2015-2020)', vAxis: {title: 'Night Light Intensity (Average Radiance)'}, hAxis: {title: 'Year'}, lineWidth: 2, pointSize: 4 }); print(nightLightChart);

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i unable to understand your language pls comment in english

sure

Most welcome

it is necessary to maintain similar datasets

THANKING Your praise

yes you can get it with sentinental image

You're correct that if we are only detecting existing water, using a single class for water (rather than distinguishing low, medium, or high water levels) simplifies the process. In this case, a single color in the palette for water is appropriate

Gold-rich zones: Areas with high concentrations of gold, usually identified through geological surveys.

Pls Check it with my channel description

Pls Check it with my channel description

Great 👍

you have to configure with srtm data

Pls Check it with my channel description

Pls Check it with my channel description

THANKS FOR Your SUPPORT

Used to express pollution levels in a standardized, interpretable way, typically as part of an Air Quality Index (AQI) or similar indices. It converts raw pollutant concentrations into a normalized value on a scale (e.g., 0-500).

So nice of you

1. Verify Dataset Availability The ESA CCI Soil Moisture dataset in GEE is typically available under a different name or version. To confirm: Go to the Google Earth Engine Data Catalog. Search for "ESA CCI Soil Moisture" to check the correct dataset name and path. Common datasets for soil moisture include: ESA/CCI/SM/DAILY/v04.5 ESA/CCI/SM/DAILY/v06.1 2. Update Your Script If the correct dataset is found, update your script to use the proper dataset path. For example: javascript Copy code // Load the ESA CCI Soil Moisture dataset var soilMoisture = ee.ImageCollection('ESA/CCI/SM/DAILY/v06.1'); // Print to verify print(soilMoisture); 3. Check Access Permissions Some datasets in GEE require you to request access. If ESA/CCI/SM/3_2 is a private or beta dataset, you might need to: Contact the dataset owner. Use an alternative publicly available dataset. 4. Alternative Soil Moisture Datasets If the dataset is not available, you can use these alternatives: SMAP (Soil Moisture Active Passive): NASA_USDA/HSL/SMAP10KM_soil_moisture (10km resolution). GLDAS (Global Land Data Assimilation System): NASA/GLDAS/V021/NOAH/G025/T3H for soil moisture at various depths. ERA5-Land: ECMWF/ERA5_LAND/HOURLY for soil moisture estimates. 5. Verify for "Combined Recharge and Slope" Layer If the issue persists for the Recharge and Slope layer, check the dataset name in a similar way. Datasets in GEE may have been updated, renamed, or replaced.

you can use it global

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pls contact me rajamanickammanoharan24@gmail.com

Consistency in Units (g/m²): NDVI can correlate with biomass density, especially when calibrated with ground-truth data. By establishing a relationship between NDVI values and biomass from sample data, you can extrapolate NDVI values to g/m² using regression or other statistical models

@@techhive.2023 Okay thank you. please make more videos related to ecological work like alpha/beta diversity, landscape metrics, GPP, Canopy height, cover, fires disturbance, historical disturbance etc, if you know, subscribing

​THANKING For Your SUGGESTS TOPICS

Thanka

sure Thanks for watching my video. my email id rajamanickammanoharan24@gmail.com

Thanks for watching my video. my email id rajamanickammanoharan24@gmail.com

It is available from my code just change gps values of Lahore Pakistan

@techhive.2023 sure sir

To prepare a Lineament Density Map from a Digital Elevation Model (DEM) in QGIS, you can follow these steps: Step 1: Load the DEM Open QGIS and load the DEM file by selecting Layer > Add Layer > Add Raster Layer and browsing to your DEM file. Click Open to display the DEM on the map canvas. Step 2: Generate Hillshade Go to Raster > Terrain Analysis > Hillshade. Select your DEM layer as the input and set the Azimuth (angle of the sun) and Altitude (height of the sun). Click Run to create a hillshade layer, which helps to visually enhance the linear features in the landscape. Step 3: Extract Lineaments Using Edge Detection Go to Processing Toolbox and search for Sobel filter or Edge detection (often available under Raster analysis plugins). Apply the filter on the hillshade layer to emphasize linear features, which will help in identifying lineaments. Step 4: Convert Lineaments to Vector Format Use Raster to Vector conversion to convert the highlighted lineaments into vector lines. Go to Raster > Conversion > Contour, choose the edge-detected layer, and set an appropriate interval to generate contour-like lineaments. Alternatively, you can use Digitize Lineament manually if the automatic extraction does not capture all lineaments accurately. Step 5: Create a Lineament Density Map Go to Vector > Analysis Tools > Line Density. Select the vectorized lineament layer as the input, define the search radius, and choose the cell size based on your map scale and desired resolution. Run the tool to create a line density raster layer, showing the density of lineaments across the area. Step 6: Style the Lineament Density Map Open the Layer Styling Panel and select the lineament density raster layer. Apply a color gradient (e.g., Red to Blue or White to Black) to represent low to high lineament densities. Adjust the Color Ramp and Transparency as needed for better visualization. Step 7: Save and Export Once the lineament density map is prepared, save it as a new raster or export it as an image or PDF by going to Project > Import/Export > Export Map. This workflow will give you a Lineament Density Map using QGIS and DEM data.

8668059897

please check it drive.google.com/file/d/1XiA_eSWNwKrvWmrrM3fUe35t8rr9Y-uo/view?usp=sharing