＜欠曲＞ ・Tower Fierce（OST3）※グローバル未配信 ・Operation Pine Soot Battle Theme（危機契約#7） ※未配信 ・Operation Barrenland（危機契約#0）

危機契約のメインテーマを集めました。その他の音楽(inst、戦闘BGM)はそれぞれの曲の概要欄から飛べると思います。 危機契約#0 operation barrenlandを除く 危機契約#3 Battleplan Extinguished Sinsを追加しました

＜欠曲＞ ・Tower Fierce（OST3）※グローバル未配信 ・Operation Pine Soot Battle Theme（危機契約#7） ※未配信 ・Operation Barrenland（危機契約#0）

I have classified my study area into six landcover classes (water, urban, barrenland,plantation, nonmangroves forests and mangroves) using Random Forest. I used six bands for classification (red, blue, green, nir, NDVI, NDWI). I now wish to get the NDVI values of all pixels that have been classified as 'mangroves' by my classifier. Can someone show me how to do that? Here is my code: //i) Merge hand-drawn polygons into one FeatureCollection: var FCmerged=water.merge(urban).merge(barrenland).merge(plantation).merge(nonMangrove).merge(mangrove); print(FCmerged.size()); Map.addLayer(FCmerged, {}, 'FCMerged'); //ii) Select bands to be used for classification: var bands = ['B2', 'B3', 'B4','B5','NDWI','NDVI']; print(bands,'bands'); //iii) Using Stratified RandomSampling to generate training points within the polygons: var FCimage=ee.Image().byte().paint(FCmerged, "lc").rename("lc") print(FCimage, 'image'); var stratifiedsample=FCimage.stratifiedSample({ numPoints:1200, classBand:"lc", region:ROI, scale:30, classValues:[0,1,2,3,4,5], classPoints:[200,200,200,200,200,200], geometries:true }) // iv) Collect and map training points: print('Stratified samples',stratifiedsample); print(stratifiedsample.reduceColumns(ee.Reducer.frequencyHistogram(),['lc']).get('histogram','No of points')); //v) Extract pixel values of the training points: var trainingPoints=l8.select(bands).sampleRegions({ collection:stratifiedsample, properties:["lc"], geometries:true, scale:30, }) print(trainingPoints,{},'trainingPoints'); Map.addLayer(trainingPoints, {},'trainingPoints',false); // vi) Split the training points by 70%/30% var sample=trainingPoints.randomColumn(); var split = 0.7; // Roughly 70% training, 30% testing. var training = sample.filter(ee.Filter.lt('random', split)); print(training,'points for classifier'); var test = sample.filter(ee.Filter.gte('random', split)); print(test,'Testing points'); // vii) Build the Random Forest classifier: var RandomForest=ee.Classifier.smileRandomForest(1000).train({ features:training, classProperty:'lc', inputProperties:bands, }); print('Random forest, explained', RandomForest.explain()); //viii) Classify L8 using RandomForest: var L8Classified=l8.select(bands).classify(RandomForest); //here l8 is an imagecomposite of landsat 8 images for 2020. print(L8Classified, 'L8 Classified'); // this output is an image //ix) Visualisation parameters: var lcPalette = [ '0000FF', // water (0) (blue) 'FF00FF', // urban (1) (fuschia) '008080',//barrenland (2) (teal) 'FFFF00',// plantation (3) (yellow) '008000',//nonmangroveforests (4) (green) 'FF0000', // mangroveforest (5) (red) ]; //x) Add Classified landcover onto map: Map.addLayer(L8Classified, {palette:lcPalette, min:0,max:5}, 'LULCC_2020'); // xi) Check the acuracy of the classification using a confusion matrix: var confusionMatrix = RandomForest.confusionMatrix(); print('Classification accuracy: ', confusionMatrix.accuracy()); //xii) Validate the RandomForest Classification using test points: var testEvaluation = test.classify(RandomForest); print('Test Evaluation', testEvaluation); //this output is a Feature Collection // xiii) Get a confusion matrix representing expected accuracy. var testAccuracy = testEvaluation.errorMatrix('lc', 'classification'); print('Validation error matrix: ', testAccuracy); print('Validation overall accuracy: ', testAccuracy.accuracy()); //xiv) Extract NDVI of classified mangrove class to study mangrove health: var mangroveNDVI = L8Classified.updateMask(L8Classified.eq(5)); // mask it print(mangroveNDVI); Map.addLayer(mangroveNDVI,{},'Crops');

I have classified my study area into six landcover classes (water, urban, barrenland,plantation, nonmangroves forests and mangroves) using Random Forest. I used six bands for classification (red, blue, green, nir, NDVI, NDWI). I now wish to get the NDVI values of all pixels that have been classified as 'mangroves' by my classifier. Can someone show me how to do that? Here is my code: //i) Merge hand-drawn polygons into one FeatureCollection: var FCmerged=water.merge(urban).merge(barrenland).merge(plantation).merge(nonMangrove).merge(mangrove); print(FCmerged.size()); Map.addLayer(FCmerged, {}, 'FCMerged'); //ii) Select bands to be used for classification: var bands = ['B2', 'B3', 'B4','B5','NDWI','NDVI']; print(bands,'bands'); //iii) Using Stratified RandomSampling to generate training points within the polygons: var FCimage=ee.Image().byte().paint(FCmerged, "lc").rename("lc") print(FCimage, 'image'); var stratifiedsample=FCimage.stratifiedSample({ numPoints:1200, classBand:"lc", region:ROI, scale:30, classValues:[0,1,2,3,4,5], classPoints:[200,200,200,200,200,200], geometries:true }) // iv) Collect and map training points: print('Stratified samples',stratifiedsample); print(stratifiedsample.reduceColumns(ee.Reducer.frequencyHistogram(),['lc']).get('histogram','No of points')); //v) Extract pixel values of the training points: var trainingPoints=l8.select(bands).sampleRegions({ collection:stratifiedsample, properties:["lc"], geometries:true, scale:30, }) print(trainingPoints,{},'trainingPoints'); Map.addLayer(trainingPoints, {},'trainingPoints',false); // vi) Split the training points by 70%/30% var sample=trainingPoints.randomColumn(); var split = 0.7; // Roughly 70% training, 30% testing. var training = sample.filter(ee.Filter.lt('random', split)); print(training,'points for classifier'); var test = sample.filter(ee.Filter.gte('random', split)); print(test,'Testing points'); // vii) Build the Random Forest classifier: var RandomForest=ee.Classifier.smileRandomForest(1000).train({ features:training, classProperty:'lc', inputProperties:bands, }); print('Random forest, explained', RandomForest.explain()); //viii) Classify L8 using RandomForest: var L8Classified=l8.select(bands).classify(RandomForest); //here l8 is an imagecomposite of landsat 8 images for 2020. print(L8Classified, 'L8 Classified'); // this output is an image //ix) Visualisation parameters: var lcPalette = [ '0000FF', // water (0) (blue) 'FF00FF', // urban (1) (fuschia) '008080',//barrenland (2) (teal) 'FFFF00',// plantation (3) (yellow) '008000',//nonmangroveforests (4) (green) 'FF0000', // mangroveforest (5) (red) ]; //x) Add Classified landcover onto map: Map.addLayer(L8Classified, {palette:lcPalette, min:0,max:5}, 'LULCC_2020'); // xi) Check the acuracy of the classification using a confusion matrix: var confusionMatrix = RandomForest.confusionMatrix(); print('Classification accuracy: ', confusionMatrix.accuracy()); //xii) Validate the RandomForest Classification using test points: var testEvaluation = test.classify(RandomForest); print('Test Evaluation', testEvaluation); //this output is a Feature Collection // xiii) Get a confusion matrix representing expected accuracy. var testAccuracy = testEvaluation.errorMatrix('lc', 'classification'); print('Validation error matrix: ', testAccuracy); print('Validation overall accuracy: ', testAccuracy.accuracy()); //xiv) Extract NDVI of classified mangrove class to study mangrove health: var mangroveNDVI = L8Classified.updateMask(L8Classified.eq(5)); // mask it print(mangroveNDVI); Map.addLayer(mangroveNDVI,{},'Crops');

I have classified my study area into six landcover classes (water, urban, barrenland,plantation, nonmangroves forests and mangroves) using Random Forest. I used six bands for classification (red, blue, green, nir, NDVI, NDWI). I now wish to get the NDVI values of all pixels that have been classified as 'mangroves' by my classifier. Can someone show me how to do that? Here is my code: //i) Merge hand-drawn polygons into one FeatureCollection: var FCmerged=water.merge(urban).merge(barrenland).merge(plantation).merge(nonMangrove).merge(mangrove); print(FCmerged.size()); Map.addLayer(FCmerged, {}, 'FCMerged'); //ii) Select bands to be used for classification: var bands = ['B2', 'B3', 'B4','B5','NDWI','NDVI']; print(bands,'bands'); //iii) Using Stratified RandomSampling to generate training points within the polygons: var FCimage=ee.Image().byte().paint(FCmerged, "lc").rename("lc") print(FCimage, 'image'); var stratifiedsample=FCimage.stratifiedSample({ numPoints:1200, classBand:"lc", region:ROI, scale:30, classValues:[0,1,2,3,4,5], classPoints:[200,200,200,200,200,200], geometries:true }) // iv) Collect and map training points: print('Stratified samples',stratifiedsample); print(stratifiedsample.reduceColumns(ee.Reducer.frequencyHistogram(),['lc']).get('histogram','No of points')); //v) Extract pixel values of the training points: var trainingPoints=l8.select(bands).sampleRegions({ collection:stratifiedsample, properties:["lc"], geometries:true, scale:30, }) print(trainingPoints,{},'trainingPoints'); Map.addLayer(trainingPoints, {},'trainingPoints',false); // vi) Split the training points by 70%/30% var sample=trainingPoints.randomColumn(); var split = 0.7; // Roughly 70% training, 30% testing. var training = sample.filter(ee.Filter.lt('random', split)); print(training,'points for classifier'); var test = sample.filter(ee.Filter.gte('random', split)); print(test,'Testing points'); // vii) Build the Random Forest classifier: var RandomForest=ee.Classifier.smileRandomForest(1000).train({ features:training, classProperty:'lc', inputProperties:bands, }); print('Random forest, explained', RandomForest.explain()); //viii) Classify L8 using RandomForest: var L8Classified=l8.select(bands).classify(RandomForest); //here l8 is an imagecomposite of landsat 8 images for 2020. print(L8Classified, 'L8 Classified'); // this output is an image //ix) Visualisation parameters: var lcPalette = [ '0000FF', // water (0) (blue) 'FF00FF', // urban (1) (fuschia) '008080',//barrenland (2) (teal) 'FFFF00',// plantation (3) (yellow) '008000',//nonmangroveforests (4) (green) 'FF0000', // mangroveforest (5) (red) ]; //x) Add Classified landcover onto map: Map.addLayer(L8Classified, {palette:lcPalette, min:0,max:5}, 'LULCC_2020'); // xi) Check the acuracy of the classification using a confusion matrix: var confusionMatrix = RandomForest.confusionMatrix(); print('Classification accuracy: ', confusionMatrix.accuracy()); //xii) Validate the RandomForest Classification using test points: var testEvaluation = test.classify(RandomForest); print('Test Evaluation', testEvaluation); //this output is a Feature Collection // xiii) Get a confusion matrix representing expected accuracy. var testAccuracy = testEvaluation.errorMatrix('lc', 'classification'); print('Validation error matrix: ', testAccuracy); print('Validation overall accuracy: ', testAccuracy.accuracy()); //xiv) Extract NDVI of classified mangrove class to study mangrove health: var mangroveNDVI = L8Classified.updateMask(L8Classified.eq(5)); // mask it print(mangroveNDVI); Map.addLayer(mangroveNDVI,{},'Crops');

I have classified my study area into six landcover classes (water, urban, barrenland,plantation, nonmangroves forests and mangroves) using Random Forest. I used six bands for classification (red, blue, green, nir, NDVI, NDWI). I now wish to get the NDVI values of all pixels that have been classified as 'mangroves' by my classifier. Can someone show me how to do that? Here is my code: //i) Merge hand-drawn polygons into one FeatureCollection: var FCmerged=water.merge(urban).merge(barrenland).merge(plantation).merge(nonMangrove).merge(mangrove); print(FCmerged.size()); Map.addLayer(FCmerged, {}, 'FCMerged'); //ii) Select bands to be used for classification: var bands = ['B2', 'B3', 'B4','B5','NDWI','NDVI']; print(bands,'bands'); //iii) Using Stratified RandomSampling to generate training points within the polygons: var FCimage=ee.Image().byte().paint(FCmerged, "lc").rename("lc") print(FCimage, 'image'); var stratifiedsample=FCimage.stratifiedSample({ numPoints:1200, classBand:"lc", region:ROI, scale:30, classValues:[0,1,2,3,4,5], classPoints:[200,200,200,200,200,200], geometries:true }) // iv) Collect and map training points: print('Stratified samples',stratifiedsample); print(stratifiedsample.reduceColumns(ee.Reducer.frequencyHistogram(),['lc']).get('histogram','No of points')); //v) Extract pixel values of the training points: var trainingPoints=l8.select(bands).sampleRegions({ collection:stratifiedsample, properties:["lc"], geometries:true, scale:30, }) print(trainingPoints,{},'trainingPoints'); Map.addLayer(trainingPoints, {},'trainingPoints',false); // vi) Split the training points by 70%/30% var sample=trainingPoints.randomColumn(); var split = 0.7; // Roughly 70% training, 30% testing. var training = sample.filter(ee.Filter.lt('random', split)); print(training,'points for classifier'); var test = sample.filter(ee.Filter.gte('random', split)); print(test,'Testing points'); // vii) Build the Random Forest classifier: var RandomForest=ee.Classifier.smileRandomForest(1000).train({ features:training, classProperty:'lc', inputProperties:bands, }); print('Random forest, explained', RandomForest.explain()); //viii) Classify L8 using RandomForest: var L8Classified=l8.select(bands).classify(RandomForest); //here l8 is an imagecomposite of landsat 8 images for 2020. print(L8Classified, 'L8 Classified'); // this output is an image //ix) Visualisation parameters: var lcPalette = [ '0000FF', // water (0) (blue) 'FF00FF', // urban (1) (fuschia) '008080',//barrenland (2) (teal) 'FFFF00',// plantation (3) (yellow) '008000',//nonmangroveforests (4) (green) 'FF0000', // mangroveforest (5) (red) ]; //x) Add Classified landcover onto map: Map.addLayer(L8Classified, {palette:lcPalette, min:0,max:5}, 'LULCC_2020'); // xi) Check the acuracy of the classification using a confusion matrix: var confusionMatrix = RandomForest.confusionMatrix(); print('Classification accuracy: ', confusionMatrix.accuracy()); //xii) Validate the RandomForest Classification using test points: var testEvaluation = test.classify(RandomForest); print('Test Evaluation', testEvaluation); //this output is a Feature Collection // xiii) Get a confusion matrix representing expected accuracy. var testAccuracy = testEvaluation.errorMatrix('lc', 'classification'); print('Validation error matrix: ', testAccuracy); print('Validation overall accuracy: ', testAccuracy.accuracy()); //xiv) Extract NDVI of classified mangrove class to study mangrove health: var mangroveNDVI = L8Classified.updateMask(L8Classified.eq(5)); // mask it print(mangroveNDVI); Map.addLayer(mangroveNDVI,{},'Crops');

I have classified my study area into six landcover classes (water, urban, barrenland,plantation, nonmangroves forests and mangroves) using Random Forest. I used six bands for classification (red, blue, green, nir, NDVI, NDWI). I now wish to get the NDVI…