网站开发分几种类型,砍柴网wordpress,如何把自己做的网站连上网,id设计本期将带来NDVI的变化趋势分析#xff0c;一个地区究竟是变绿了#xff0c;还是植被退化了#xff0c;如何进行量化呢#xff1f;小编给出了代码#xff0c;后期将带来详细的解释#xff0c;大家可以先尝试着分析一下代码。
上代码#xff01;
var regionee.FeatureCo…本期将带来NDVI的变化趋势分析一个地区究竟是变绿了还是植被退化了如何进行量化呢小编给出了代码后期将带来详细的解释大家可以先尝试着分析一下代码。
上代码
var regionee.FeatureCollection(users/hesuixinya511/South_China);
var empty ee.Image().toByte();
var outline ee.Image().toByte().paint({featureCollection:region,color:0,width:2});
Map.addLayer(outline, {palette: black}, outline);
Map.centerObject(region,6);var mod13 ee.ImageCollection(MODIS/006/MOD13Q1);
var mod13Summer mod13.filterDate(2000-01-01, 2020-12-31).filter(ee.Filter.calendarRange(2000, 2020, year)).map(function(img) {return img.set(year, img.date().get(year));});
print(mod13Summer);
//按年份jion数据
var mod13SummerAnnualJoin ee.Join.saveAll(same_year).apply({primary: mod13Summer.distinct(year),secondary: mod13Summer,condition: ee.Filter.equals({leftField: year, rightField: year})
});
print(mod13SummerAnnualJoin);//添加年份作为新的波段
var summerStats ee.ImageCollection(mod13SummerAnnualJoin.map(function(img) {var year img.get(year);var yearCol ee.ImageCollection.fromImages(img.get(same_year));var mean yearCol.select(NDVI).mean();var yr ee.Image.constant(ee.Number(year)).toShort();return ee.Image.cat(yr, mean).rename([year, mean]).set(year, year);
}));
print(summerStats,summerStats);//lineChart
var NDVI_mean summerStats.select(mean). map(function(image){return image.multiply(0.0001).set(image.toDictionary(image.propertyNames()));});
var Yearly_chart ui.Chart.image.series({imageCollection: NDVI_mean.select(mean),region: region,reducer: ee.Reducer.mean(),scale: 500,xProperty: year,}).setOptions({interpolateNulls: true,lineWidth: 2,title: NDVI Yearly Seires,vAxis: {title: NDVI},hAxis: {title: Date},trendlines: { 0: {title: NDVI_trend,type:linear, showR2: true, color:red, visibleInLegend: true}}});
print(Yearly_chart);//sensSlope
var sens summerStats.reduce(ee.Reducer.sensSlope());
print(sens,sens);
//linearFit
var fitsummerStats.reduce(ee.Reducer.linearFit());
print(fit,fit);
//slop 可视化
var visParams {opacity: 1,bands: [slope],min: -55,max: 55,palette:[8c510a, d8b365, f6e8c3, f5f5f5, d9f0d3, 7fbf7b, 1b7837]
};
Map.addLayer(sens.clip(region), visParams, Sen\s slope);
var vis {min: -55, max: 55, palette: [8c510a, d8b365, f6e8c3, f5f5f5, d9f0d3, 7fbf7b, 1b7837]};
var palettes require(users/gena/packages:palettes);
function makeColorBarParams(palette) {return {bbox: [0, 0, 1, 0.1],dimensions: 300x10,format: png,min: 0,max: 1,palette: palette,};
}
var type((sens.select(slope).gt(0)).multiply(1))
.add((sens.select(slope).lte(0)).multiply(2))
Map.addLayer(type.clip(region),{min:1,max:2,palette:[#3CB371,#B22222]},G_or_B,false)
Export.image.toDrive({image:type.clip(region),description:Drive,fileNamePrefix:Graph,folder:Graph1,scale:500,region:region,crs:EPSG:4326,maxPixels:1e13
})
var colorBar ui.Thumbnail({image: ee.Image.pixelLonLat().select(0),params: makeColorBarParams([8c510a, d8b365, f6e8c3, f5f5f5, d9f0d3, 7fbf7b, 1b7837]),style: {stretch: horizontal, margin: 0px 8px, maxHeight: 40px},
});
var legendLabels ui.Panel({widgets: [ui.Label(vis.min, {margin: 4px 8px}),ui.Label((vis.max / 2),{margin: 4px 8px, textAlign: center, stretch: horizontal}),ui.Label(vis.max, {margin: 4px 8px})],layout: ui.Panel.Layout.flow(horizontal)
});
var legendTitle ui.Label({value: Annual growing season NDVI trend,style: {fontWeight: bold}
});
var legendPanel ui.Panel([legendTitle, colorBar, legendLabels]);
Map.add(legendPanel);
function getHistogram(Img, geometry) {var hist Img.select(slope).reduceRegion({reducer: ee.Reducer.autoHistogram(),geometry: geometry,scale: 250,maxPixels: 1e13,});var histArray ee.Array(hist.get(slope));var binBottom histArray.slice(1, 0, 1);var nPixels histArray.slice(1, 1, null);var histColumnFromArray ui.Chart.array.values({array: nPixels, axis: 0, xLabels: binBottom}).setChartType(LineChart).setOptions({title: vegetation condition trend histogram,hAxis: {title: Slope},vAxis: {title: Pixel count},pointSize: 0,lineSize: 2,colors: [1b7837],legend: {position: none}});return histColumnFromArray;
}
var histogram(getHistogram(sens.select(slope)
.updateMask(((sens.select(slope)).gt(-200)).and ((sens.select(slope)).lt(200))), region));
var Greening_Area((type.eq(1)).updateMask(type.eq(1)).multiply(ee.Image.pixelArea())).reduceRegion({reducer:ee.Reducer.sum(),geometry:region,scale :250,maxPixels:1e13});
print(Greening area (km2),ee.Number(Greening_Area.get(slope)).divide(1000000))
var Browning_Area((type.eq(2)).updateMask(type.eq(2)).multiply(ee.Image.pixelArea())).reduceRegion({reducer:ee.Reducer.sum(),geometry:region,scale :250,maxPixels:1e13});
print(Browning area (km2),ee.Number(Browning_Area.get(slope)).divide(1000000))
var dict ee.Image.pixelArea() .addBands(type.int()) .reduceRegion({ reducer:ee.Reducer.sum().group({ groupField:1, groupName:type, }), geometry:region, scale:250, maxPixels:1e13 });
var groups ee.List(dict.get(groups));
var typeNames ee.List([Greening,Browning]);
var featureList groups.map(function(group){ group ee.Dictionary(group); var area ee.Number(group.get(sum)); area area.divide(1000000); //km2var type ee.Number(group.get(type)); var f ee.Feature(null, { type: type, area: area, name: typeNames.get(type.subtract(1))}); return f;
});
var areaFCol ee.FeatureCollection(featureList);
var totalArea ee.Number(areaFCol.aggregate_sum(area));
areaFCol areaFCol.map(function(f){ var typearea ee.Number(f.get(area)); f f.set(type_area, typearea); f f.set(percent,typearea.divide(totalArea).multiply(100));return f;
});
print(areaFCol ,areaFCol);
var piechart ui.Chart.feature.byFeature(areaFCol).setChartType(PieChart).setOptions({xProperty: name,yProperties: percent,title: Area Percent of Greening and Browning pixles,legend: {position:none},//top-rightwidth: 200,height: 200,is3D: true,colors: [#3CB371,#B22222],});
var panel2 ui.Panel();
panel2.style().set({width: 350px,position: top-right
});
var Area_table(ui.Chart.feature.byFeature(areaFCol.select([name,type_area]),name).setChartType(Table));
var legendTitle ui.Label({value: Area statistic of greening and browning area (Km2),style: {fontWeight: bold}
});
panel2.add(histogram).add(piechart).add(legendTitle).add(Area_table);
Map.add(panel2);
最后你可以看到华南地区NDVI随时间的变化趋势 也可以看到绿了还是退化了的面积统计 当然也可以看到可视化的地图哦 今天的分享到这里就结束了有不懂可以私聊小编哦更多内容欢迎大家关注小编的公众号“梧桐GIS”谢谢大家的支持
