百度不收录网站内页,注册公司需要什么条件吗,如何免费建立个人网站,重钢建设公司官方网站基于GEE实现物种分布模型之随机森林法 1.物种分布数据2.研究区绘制3.预测因子选择 1.物种分布数据
根据研究目的和需要导入物种数据#xff1a;
// Load presence data
var Data ee.FeatureCollection(users/************736/Distribution);
print(Original da… 基于GEE实现物种分布模型之随机森林法 1.物种分布数据2.研究区绘制3.预测因子选择 1.物种分布数据
根据研究目的和需要导入物种数据
// Load presence data
var Data ee.FeatureCollection(users/************736/Distribution);
print(Original data size:, Data.size());// Define spatial resolution to work with (m)
var GrainSize 1000;function RemoveDuplicates(data){var randomraster ee.Image.random().reproject(EPSG:4326, null, GrainSize);var randpointvals randomraster.sampleRegions({collection:ee.FeatureCollection(data), scale: 15000, geometries: true});return randpointvals.distinct(random);
}var Data_Remove RemoveDuplicates(Data);
print(Final data size:, Data_Remove.size());
print(Final data size:, Data_Remove);// Add two maps to the screen.
var left ui.Map();
var right ui.Map();
ui.root.clear();
ui.root.add(left);
ui.root.add(right);// Link maps, so when you drag one map, the other will be moved in sync.
ui.Map.Linker([left, right], change-bounds);// Visualize presence points on the map
right.addLayer(Data_Remove, {color:red}, RemoveDuplicates-Presence, 1);
left.addLayer(Data, {color:blue}, Presence, 1);
此处使用两个模块来展示数据图像
2.研究区绘制
这里要注意使用USDOS/LSIB_SIMPLE/2017数据时他给出的中国地图有错误如没有台湾省以及新疆和西藏接壤处存在问题。
// Define the AOI
// var AOI Data.geometry().bounds().buffer({distance:50000, maxError:1000});
var AOI ee.FeatureCollection(USDOS/LSIB_SIMPLE/2017).filter(ee.Filter.eq(country_co, CH));
// var AOI ee.FeatureCollection(users/******736/ChinaMap);
var AOI_polygon AOI.geometry().bounds();// Add border of study area to the map
var outline ee.Image().byte().paint({featureCollection: AOI, color: 1, width: 3});
right.addLayer(outline, {palette: 0000FF}, Final Study Area);
left.addLayer(outline, {palette: FF0000}, Original Study Area);// Center each map to the area of interest
right.centerObject(AOI, 3); //Number indicates the zoom level
left.centerObject(AOI, 3); //Number indicates the zoom level
3.预测因子选择
常见的因子包括worldclim中的生物气候因子、地形因子、植被因子等这里使用了以上三者作为分析当然你还可以添加土壤因子和人为因子等因素进去。
// Load WorldClim BIO Variables (a multiband image) from the data catalog
var BIO ee.Image(WORLDCLIM/V1/BIO);// Load elevation data from the data catalog
// and calculate slope, aspect, and a simple hillshade from the terrain Digital Elevation Model.
var Terrain ee.Algorithms.Terrain(ee.Image(USGS/SRTMGL1_003)); // 30m year:2000// Load NDVI 250 m collection and estimate median annual tree cover value per pixel
var MODIS ee.ImageCollection(MODIS/006/MOD44B);
var MedianPTC MODIS.filterDate(2000-01-01, 2020-12-31).select([Percent_Tree_Cover]).median();// Combine bands into a single multi-band image
var predictors BIO.addBands(Terrain).addBands(MedianPTC);
print(Predictors values:, predictors)var watermask Terrain.select(elevation).gt(0); //Create a water mask
var predictors predictors.updateMask(watermask).clip(AOI);// Select subset of bands to keep for habitat suitability modeling
// bio04,bio05,bio06,bio12,elevation,Percent_Tree_Cover
var bands [bio04,bio05,bio06,bio12,elevation,Percent_Tree_Cover];
var predictors predictors.select(bands);// Display layers on the map
right.addLayer(predictors, {bands:[elevation], min: 0, max: 8000, palette: [000000,006600, 009900,33CC00,996600,CC9900,CC9966,FFFFFF,]}, Elevation (m), 0);
right.addLayer(predictors, {bands:[bio05], min: 100, max: 490, palette:white,red}, Max temperature of warmest month, 0);
right.addLayer(predictors, {bands:[bio12], min: 0, max: 4000, palette:white,blue}, Annual precipitation (mm), 0);
right.addLayer(predictors, {bands:[Percent_Tree_Cover], min: 0, max: 70, palette:white,green, red}, Percent_Tree_Cover, 0);
海拔
最大温度
年降水量
森林植被覆盖率
