Mapping marginal agricultural lands reimagined

Mapping marginal agricultural lands reimagined This is an adaptation of a model from an Accepted Manuscript of an article published by Taylor & Francis in Annals of the American Association of Geographers on 18 January 2018, available online at doi.org/10.1080/24694452.2017.1403877. Reference: Peter, B.G., Messina, J.P. and Snapp, S.S., 2018. A Multiscalar Approach to Mapping Marginal Agricultural Land: Smallholder Agriculture in Malawi. Annals of the American Association of Geographers, 108(4), pp.989-1005. In this version, MODIS NPP (net primary productivity, 1-km spatial resolution) is replaced with MODIS NDVI (normalized difference vegetation index, 250-m spatial resolution). In addition, this revised model also considers time-series slope in characterizing agricultural productivity. Previously devised using ArcPy, this updated version is written in JavaScript for Google Earth Engine. Categories L = low production; M = medium production; H = high production S = stable production; V = variable production I = increasing slope; D = decreasing slope 1 - LSI 2 - LVI 3 - LSD 4 - LVD 5 - MSI 6 - MVI 7 - MSD 8 - MVD 9 - HSI 10 - HVI 11 - HSD 12 - HVD Google Earth Engine Code var mod13q1 = ee.ImageCollection("MODIS/006/MOD13Q1"), lulc = ee.Image("ESA/GLOBCOVER_L4_200901_200912_V2_3"), countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017"); var startYear = 2008; var endYear = 2018; var startMonth = 11; var endMonth = 4; var country = 'Kenya'; var scale = 2500; var years = ee.List.sequence(startYear,endYear); var n = endYear-startYear+1; var ag = lulc.select('landcover').lte(30).eq(1); var region = countries.filter(ee.Filter.eq('country_na',country)); var yearly_mod13q1 = ee.ImageCollection.fromImages( years.map(function (y) { return mod13q1.filter(ee.Filter.calendarRange(y,y,'year')) .filter(ee.Filter.calendarRange(startMonth,endMonth,'month')) .select('NDVI').mean() .clip(region).updateMask(ag) .multiply(0.0001); }) ); var mean_mod13q1 = yearly_mod13q1.mean().rename('mean_mod13q1'); var stddev_mod13q1 = yearly_mod13q1.map(function(img){ return img.subtract(mean_mod13q1).pow(2); }).sum().divide(n).sqrt().rename('stddev_mod13q1'); var level_mod13q1 = mean_mod13q1.reduceRegion({ reducer: ee.Reducer.percentile([33,66]), geometry: region, scale: scale, maxPixels: 1e12 }); var var_mod13q1 = stddev_mod13q1.reduceRegion({ reducer: ee.Reducer.percentile([50]), geometry: region, scale: scale, maxPixels: 1e12 }); var lmh_mod13q1 = mean_mod13q1.gt(ee.Number(level_mod13q1.get('mean_mod13q1_p33'))) .add(mean_mod13q1.gt(ee.Number(level_mod13q1.get('mean_mod13q1_p66')))) .remap([0,1,2],[1,2,3]); var vs_mod13q1 = stddev_mod13q1.gt(ee.Number(var_mod13q1.get('stddev_mod13q1'))).remap([0,1],[10,20]); var productivity_mod13q1 = lmh_mod13q1.add(vs_mod13q1).remap([11,12,13,21,22,23],[1,4,5,2,3,6]).rename('productivity'); // 11 - LS - 1 // 12 - MS - 3 // 13 - HS - 5 // 21 - LV - 2 // 22 - MV - 4 // 23 - HV - 6 var createTimeBand = function(image) { var num = image.get('system:index'); return image.addBands(ee.Image(ee.Number.parse(num)).double()); }; var linearFit = yearly_mod13q1.map(createTimeBand).select(['constant', 'NDVI']).reduce(ee.Reducer.linearFit()).select('scale'); var slope = linearFit.gt(0).remap([0,1],[0,10]); var productivity_slope = productivity_mod13q1.add(slope).remap([1,2,3,4,5,6,11,12,13,14,15,16],[3,4,7,8,11,12,1,2,5,6,9,10]); // 1 - 3 - LSD // 2 - 4 - LVD // 3 - 7 - MSD // 4 - 8 - MVD // 5 - 11 - HSD // 6 - 12 - HVD // 11 - 1 - LSI // 12 - 2 - LVI // 13 - 5 - MSI // 14 - 6 - MVI // 15 - 9 - HSI // 16 - 10 - HVI Map.centerObject(region); Map.setOptions('HYBRID'); var sColors = ['fecc5c','fd8d3c','f03b20','bd0026','a1dab4','41b6c4','2c7fb8','253494','c2e699','78c679','31a354','006837']; Map.addLayer(ee.Image(0),{palette:'black', opacity: 0.75},'dark basemap'); Map.addLayer(productivity_slope,{min:1,max:12,palette:sColors},'productivity slope'); var categories = ['Low-Stable-Increasing', 'Low-Variable-Increasing', 'Low-Stable-Decreasing', 'Low-Variable-Decreasing', 'Medium-Stable-Increasing', 'Medium-Variable-Increasing', 'Medium-Stable-Decreasing', 'Medium-Variable-Decreasing', 'High-Stable-Increasing', 'High-Variable-Increasing', 'High-Stable-Decreasing', 'High-Variable-Decreasing']; var legend = ui.Panel({ style: { position: 'bottom-left', backgroundColor: '202020', padding: '8px 15px', border: '5px solid #333333' } }); var legendTitle = ui.Label({ value: 'Agricultural productivity', style: { fontWeight: 'bold', fontSize: '18px', margin: '3px 0 -1px 8px', backgroundColor: '202020', color: 'dbdbdb', padding: '0' } }); var line = ui.Label({ value: '_________________________________________', style: { fontSize: '10px', margin: '0 0 10px 1px', backgroundColor: '202020', color: 'dbdbdb', padding: '0' } }); legend.add(legendTitle); legend.add(line); var makeRow = function(color, name) { var colorBox = ui.Label({ style: { backgroundColor: '#' + color, padding: '8px', margin: '0 0 4px 0' } }); var description...