GEE代码实例教程详解：植被状况指数（VCI）与干旱监测

简介

在本篇博客中，我们将使用Google Earth Engine (GEE) 进行植被状况指数（Vegetation Condition Index, VCI）的计算和干旱监测。通过MODIS NDVI数据，我们可以评估2001年至2024年间的植被状况和干旱等级。

背景知识

MODIS NDVI数据集

MODIS/061/MOD13A2数据集提供了MODIS的归一化植被指数（NDVI）数据，这些数据可以用来评估植被覆盖和健康状况。

植被状况指数（VCI）

VCI是一种基于NDVI的指标，用于评估植被状况和监测干旱。VCI的计算公式为：
[ VCI = \left(\frac{(NDVI - NDVI_{min})}{(NDVI_{max} - NDVI_{min})}\right) \times 100 ]

完整代码

// 定义研究点坐标
var cor = [52.91994991092, 33.56756085477778];
var point = ee.Geometry.Point(cor);// 加载FAO GAUL数据集并筛选研究区域
var feature = ee.FeatureCollection("FAO/GAUL/2015/level0");
var roi = feature.filterBounds(point);
Map.centerObject(roi);
Map.addLayer(roi);// 定义时间范围
var time_start = '2001', time_end = '2003';// 加载MODIS NDVI数据集
var ndvi = ee.ImageCollection("MODIS/061/MOD13A2").select(['NDVI']).filterDate(time_start, time_end);// 计算VCI
var ndvi_min = ndvi.min().multiply(0.0001);
var ndvi_max = ndvi.max().multiply(0.0001);var vci = ndvi.map(function (img) {var band = img.multiply(0.0001);var index = band.expression('((ndvi - min)/(max - min))*100.0', {'ndvi': band,'min': ndvi_min,'max': ndvi_max}).rename('vci');return index.copyProperties(img, ['system:time_start', 'system:time_end']);
});// 计算VCI中值
var vci_median = vci.median();
Map.addLayer(vci_median.clip(roi), [], 'vci_median', false);// 打印VCI直方图
print(ui.Chart.image.histogram(vci_median, roi, 1000)
);// VCI分类
var cons = ee.Image.constant(0);var extreme = cons.where(vci_median.gte(0).and(vci_median.lt(10)), 1);
var severe = extreme.where(vci_median.gte(10).and(vci_median.lt(20)), 2);
var moderate = severe.where(vci_median.gte(20).and(vci_median.lt(30)), 3);
var light = moderate.where(vci_median.gte(30).gte(vci_median.lt(40)), 4);
var no1 = light.where(vci_median.gte(40).and(vci_median.lt(60)), 5);
var no2 = no1.where(vci_median.gte(60).and(vci_median.lt(80)), 6);
var no3 = no2.where(vci_median.gte(80), 7);Map.addLayer(moderate.clip(roi), {min: 1, max: 7}, 'drought_map', false);// VCI时间序列分类
var time_start = '2001', time_end = '2024';
var ndvi2 = ee.ImageCollection("MODIS/061/MOD13A2").select(['NDVI']).filterDate(time_start, time_end);var ndvi_min2 = ndvi2.min().multiply(0.0001);
var ndvi_max2 = ndvi2.max().multiply(0.0001);var vci2 = ndvi2.map(function (img) {var band = img.multiply(0.0001);var index = band.expression('((ndvi - min)/(max - min))*100.0', {'ndvi': band,'min': ndvi_min2,'max': ndvi_max2}).rename('vci');return index.copyProperties(img, ['system:time_start', 'system:time_end']);
});// 修正：vci_class 应使用 vci2 而不是 vci_median
var vci_class = vci2.map(function (img) {var vci_value = img.select('vci');return img.expression('extreme + severe + moderate + light + no1 + no2 + no3',{'extreme': vci_value.gte(0).and(vci_value.lt(10)).multiply(1),'severe': vci_value.gte(10).and(vci_value.lt(20)).multiply(2),'moderate': vci_value.gte(20).and(vci_value.lt(30)).multiply(3),'light': vci_value.gte(30).and(vci_value.lt(40)).multiply(4),'no1': vci_value.gte(40).and(vci_value.lt(60)).multiply(5),'no2': vci_value.gte(60).and(vci_value.lt(80)).multiply(6),'no3': vci_value.gte(80).multiply(7)}).rename('class');
});var vci_map = vci_class.mode();Map.addLayer(vci_map.clip(roi), {palette: ['black', 'brown', 'red', 'orange', 'yellow', 'lightgreen', 'darkgreen'],min: 1,max: 7
}, 'vci_mode', false);// 导出VCI分类图
Export.image.toDrive({image: vci_map.clip(roi),description: 'vci_map',region: roi,maxPixels: 1e13,crs: 'EPSG:4326',folder: 'drought',scale: 1000
});// 计算干旱面积
var drought_area = (ee.Image.pixelArea().divide(1e6)).addBands(vci_map);print(ui.Chart.image.byClass(drought_area, 'constant',roi, ee.Reducer.sum(), 1000, ['nan', 'extreme drought', 'severe drought', 'moderate drought', 'light drought', 'no drought1', 'nodrought2', 'no drought3'])
);