My GeoServer WFSThis is a description of your Web Feature Server. The GeoServer is a full transactional Web Feature Server, you may wish to limit GeoServer to a Basic service level to prevent modificaiton of your geographic data.WFS, WMS, GEOSERVERhttp://linesam.mruni.eu/geoserver/wfsNONENONEgeonode:CStorageCarbon sequestration calculated using Net primary production (NPP) (average 2000-2013) per eldership (average). NPP data is derived from MODIS/055/MOD17A3 satelite (500 m resolution) Data in kg C/m². Classes were carried out according to Quantile interval.CarbonEPSG:3035geonode:Climate_RegulationClimate regulation calculated using as indicator net primary production (NPP)(average 2000-2013) by forests and woodlands (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest and 3.2.4. Transitional woodland shrub) per eldership (average). NPP data is derived from MODIS/055/MOD17A3 satelite (500 m resolution) and Corine land cover 2018 data from (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018). Data in kg C/m² . Classes were carried out according to Quantile interval.Climate_RegulationEPSG:3035geonode:Crop_DemCrop demand calculated from the average (2000-2017) bread and grain, vegetables, potatoes, fruits and berries consumption per capita at eldership level. The average of Bread and grain, vegetables, potatoes, fruits and berries consumption per capita was multiplied by the number of residents in the different elderships in 2011. Crop consumption per capita and Census 2011 data from Lithuanian Statistical Department (2018). Data in kg/eldership. Classes were carried out according to Quantile interval.CropCEPSG:3035geonode:Crop_munCrop demand calculated from the average (2000-2017) bread and grain, vegetables, potatoes, fruits and berries consumption per capita at municipal level. The average of Bread and grain, vegetables, potatoes, fruits and berries consumption per capita was multiplied by the number of residents in the different municipalities (average 20015-2018). Crop consumption per capita and Census 2011 data from Lithuanian Statistical Department (2018). Data in kg/eldership. Classes were carried out according to Quantile interval.CropCEPSG:3035geonode:CropCTotal average (2014-2018) of cereal and grain production (grain crops, cereals, winter cereals, winter wheat, winter triticale, winter rye, winter barley, spring cereals, spring wheat, spring triticale, spring rye, oats, buckwheat, mixed cereals, grain maize, dried pulses, peas, beans, vetches, mixed dried pulses, lupines, mustard, hemp and sunflower). Data in tons per hectare (t/ha). Cereals and grain data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval.Grain, CerealEPSG:3035geonode:CropFVTotal Average (2013-2017) of field vegetables (all brassicas, carrots, onions, beetroot). Data in tons per hectare (t/ha). Field vegetables data is from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval.VegetablesEPSG:3035geonode:Fruits_vegTotal Average (2013-2017) of fruits and berries (orchards and berries plantations, apples, blackcurrant and strawberries). Data in tons per hectare (t/ha). Crop production (fruits and berries) data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval.Berries, Fruits_vegEPSG:3035geonode:CropGVTotal Average (2013-2017) of vegetables produced in greenhouses (tomatoes and cucumbers). Data in tons per hectare (t/ha). Vegetables under glass data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval.VegetablesEPSG:3035geonode:Cult_NatCultural and natural supply service was calculated by using the point and area data of natural and cultural objects. Data sample included botanical and geological objects, forthills, natural monuments (points), nature reservations, parks, cultural heritage sites (areas). The data was gathered from Department of Cultural Heritage, piliakalniai.lt, Cadastre of protected areas in Lithuania mapped in Google Maps based on data provided in State service for protected areas website.The kernel density estimation was applied to point data, results were reclassified (jenks), overlayed with area data. The zonal statistics was applied to calculate the mean of cultural/natural supply density per eldership.Cultural_Natural, CulturalEPSG:3035geonode:Flood_dem90Flood Regulation Demand 1990flood, MALPESEPSG:3035geonode:Flood_dem00Flood Regulation Demand 2000flood, MALPESEPSG:3035geonode:Flood_dem06Flood Regulation Demand 2006flood, MALPESEPSG:3035geonode:Flood_dem12Flood Regulation Demand 2012flood, MALPESEPSG:3035geonode:Flood_dem18Flood Regulation Demand 2018flood, MALPESEPSG:3035geonode:Flood_sup_90Flood Regulation Supply, 1990flood, MALPESEPSG:3035geonode:Flood_sup_00Flood Regulation Supply, 2000MALPESEPSG:3035geonode:Flood_sup_06Flood Regulation Supply 2006flood, MALPESEPSG:3035geonode:Flood_sup_12Flood Regulation Supply 2012MALPESEPSG:3035geonode:Flood_sup_18Flood Regulation Supply 2018MALPESEPSG:3035geonode:Flood_sup_22Flood Regulation Supply 2022flood, MALPESEPSG:3035geonode:Flood_Demand_Area for flood protection (1.1.1. Continuous urban fabric, 1.1.2. Discontinuous urban fabric, 1.2.1. Industrial and commercial units, 1.2.2. Road and rail networks and associated land, 1.2.3. Port areas, 1.2.4. Airports, 1.3.1. Mineral extraction sites, 1.3.2. Dump sites, 1.3.3. Construction sites and 1.4.2. Sport and leisure facilities) in floodplain risk areas. Floodplain risk areas have the probability to be affected one time per thousand years (returning period). Floodplain and coastal risk areas from Lithuanian Environmental Protection Agency and land use from Corine Land Cover 2018.DemandEPSG:3035geonode:FDFinalBuild up areas (1.1.1. Continuous urban fabric, 1.1.2. Discontinuous urban fabric, 1.2.1. Industrial and commercial units, 1.2.2. Road and rail networks and associated land, 1.2.3. Port areas, 1.2.4. Airports, Construction sites and 1.4.2. Sport and leisure facilities) in floodplain and coastal risky areas at eldership level. Floodplain and coastal risk areas have the probability to be flooded one time per thousand years (returning period). Floodplain and coastal risky areas data from from Lithuanian Environmental Protection Agency and land use data from Corine Land Cover 2018.Flood_area_protectionEPSG:3035geonode:Grass_Supp0Grassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2012 (Copernicus). Data in ha per eldership. Classes were carried out according to Quantile interval. Data in ha per municipality.Grassland_SupplyEPSG:3035geonode:Grass_SuppGrassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2018 (Copernicus). Data in ha per eldership. Classes were carried out according to Quantile interval. Data in ha per municipality.Grassland_SupplyEPSG:4669geonode:Grassland_SupplyGrassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2012 (Copernicus). Data in ha per municipality. Classes were carried out according to Quantile interval. Data in ha per municipality.Grassland_SupplyEPSG:4669geonode:MunGrassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2018 (Copernicus). Data in ha per municipality. Classes were carried out according to Quantile interval. Data in ha per municipality.Grassland_SupplyEPSG:3035geonode:WHD_Average (2012-2016) ground and surface water consumption in households according to the urban fabric (1.1.1. Continuous urban fabric and 1.1.2. Discontinuous urban fabric from Corine land cover 2018) area and the population per municipality in 2018. The index of household water demand was calculated according to the formula of Kroll et al. (2012): (WD_pop X POP)/A_urban =W/A_urban Where, WDpop is the household consumption in m3 per municipality, POP the population of the municipality and A_urban, the urban fabric area at each municipality. Classes were carried out according to Quantile interval.Territorial WatersEPSG:3035geonode:Shape0Average (2012-2016) ground and surface water consumption for non-drinkable purposes (Industry, energy, agricultural and fisheries) at municipal level. Data in m3. Classes were carried out according to Quantile interval.Terrestrial, Territorial Waters, DemandEPSG:3035geonode:Grnd_sup_00Groundwater Recharge Supply 2000MALPES, GroundwaterEPSG:3035geonode:Grnd_sup_06Groundwater Recharge Supply 2006MALPES, GroundwaterEPSG:3035geonode:Grnd_sup_18Groundwater Recharge Supply 2018MALPES, GroundwaterEPSG:3035geonode:Grnd_sup_22Groundwater Recharge Supply 2022MALPES, GroundwaterEPSG:3035geonode:Grnd_sup_12Groundwater Supply 2012MALPES, GroundwaterEPSG:3035geonode:Grnd_h2o_dem90_22No abstract providedMALPES, GroundwaterEPSG:3035geonode:Grnd_h2o_dem00No abstract providedMALPESEPSG:3035geonode:Grnd_h2o_dem06No abstract providedMALPES, GroundwaterEPSG:3035geonode:Grnd_h2o_dem12No abstract providedMALPES, GroundwaterEPSG:3035geonode:Grnd_h2o_dem18No abstract providedMALPES, GroundwaterEPSG:3035geonode:Grnd_h2o_dem22No abstract providedMALPES, GroundwaterEPSG:3035geonode:Grnd_sup_90Groundwater recharge supply for 1990MALPES, GroundwaterEPSG:3035geonode:Her_dem90Heritage Demand 1990-2000MALPES, HeritageEPSG:3035geonode:Her_dem00Heritage Demand 2000-2006MALPES, HeritageEPSG:3035geonode:Her_dem06Heritage Demand 2006-2012MALPES, HeritageEPSG:3035geonode:Her_dem12_18Heritage Demand 2012-2018MALPESEPSG:3035geonode:Her_dem18_22Heritage Demand 2018-2022MALPES, HeritageEPSG:3035geonode:Her_sup90_00Heritage Supply 1990-2000MALPES, HeritageEPSG:3035geonode:Her_sup_00_06Heritage Supply 2000-2006MALPES, HeritageEPSG:3035geonode:Her_sup_06_12Heritage Supply 2006-2012MALPES, HeritageEPSG:3035geonode:Her_sup_12_18Heritage Supply 2012-2018MALPES, HeritageEPSG:3035geonode:Her_sup_18_22Heritage Supply 2018-2022MALPES, HeritageEPSG:3035geonode:Livestock_demandAverage (2005-2016) number of livestock (Cattle, dairy cows, pigs, sheep, goats and horses) per municipality. Livestock data is from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval.Livestock_demand, DemandEPSG:4669geonode:BFor2018_eldPercentage of area occupied by riparian forests and woodland (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. and 3.2.4. Transitional woodland shrub) in a buffer strip of 25 meters at eldership level. Data expressed in percent. Land use data from Corine Land Cover 2018 and water courses from Lithuanian cadastre. Classes were carried out according to Quantile interval.WasteEPSG:3035geonode:Med_wastes_drvchCPercentage of area occupied by riparian forests and woodland (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. and 3.2.4. Transitional woodland shrub) in a buffer strip of 25 meters at eldership level. Data expressed in percent. Land use data from Corine 1990 and 2018, and water courses from Lithuanian cadastre. Data in %.Drivers of Change, MedWastes_drichEPSG:3035geonode:Sand_PotentialThe potential of mineral (sand) extraction was mapped by matching the sediment substrate found in the known wxtraction sites against the seabed sediment distribution in the Lithuanian EEZ. The location of the official sand extraction sites and seabed sediment map was retrieved from the EMODnet Geology “Marine Minerals” and “Seabed Substrates” data products.Marine, Sand_PotentialEPSG:3035geonode:Sand_supplyIn the Lithuanian EEZ, there are two official locations where mineral (sand) is extracted from the seabed. The two areas are located in the northern and southern part of the EEZ at a depth of 30 and 40 meters. The location of the extraction sites was obtained through Emodnet Geology (2019) which acquire the data from the Lithuanian Geological Survey.Marine, Sand_supplyEPSG:3035geonode:PolinationProportion of arable crops demanding insect pollination (2.1.1. Non-irrigated arable land, 2.2.2. Fruit trees and berry plantations, 2.4.2. Complex cultivation patterns and 2.4.3. Land principally occupied by agriculture, with significant areas of natural vegetation). Land use data from Corine Land Cover 2018. Classes were carried out according to Quantile interval.Pollination, DemandEPSG:3035geonode:Flood_Supply_RatioRatio between area between flood protection area supply in flood plain and coastal risky areas (1.4.1. Green urban areas, 2.2.2. Fruit trees and berry plantations 2.3.1. Pastures; 2.4.2. Complex cultivation patterns; 2.4.3. Land principally occupied by agriculture, with significant areas of natural vegetation; 3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. Natural grassland, 3.2.2. Moors and heathland, 3.2.4. Transitional woodland shrub, 3.3.1. Beaches, dunes, and sand plains, 3.3.3. Sparsely vegetated areas, 4.1.1. Inland marshes and 4.1.2. Peatbogs) and the total flooded area at Eldership level. Floodplain risk areas have the probability to be affected one time per thousand years (returning period). Floodplain and coastal risk areas from Lithuanian Environmental Protection Agency and land use from Corine Land Cover 2018.Flood_area_protectionEPSG:3035geonode:Rec_dem90Recreation Demand 1990recreation, MALPESEPSG:3035geonode:Red_dem00Recreation Demand 2000recreation, MALPESEPSG:3035geonode:Rec_dem06Recreation Demand 2006Recreation, MALPESEPSG:3035geonode:Rec_dem12Recreation Demand 2012Recreation, MALPESEPSG:3035geonode:Rec_dem120Recreation Demand 2018Recreation, MALPESEPSG:3035geonode:Rec_dem22Recreation Demand 2022Recreation, MALPESEPSG:3035geonode:Rec_sup90Recreation Supply 1990Recreation, MALPESEPSG:3035geonode:Rec_sup00Recreation Supply 2000Recreation, MALPESEPSG:3035geonode:Rec_sup06Recreation Supply 2006MALPESEPSG:3035geonode:Rec_sup12Recreation Supply 2012Recreation, MALPESEPSG:3035geonode:Rec_sup18Recreation Supply 2018Recreation, MALPESEPSG:3035geonode:Rec_sup22No abstract providedRecreation, MALPESEPSG:3035geonode:RecreationRecreation demand in Lithuania at eldership level. Kernel density was applied to the residential buildings distribution in Lithuania. Subsequently, zonal statistics method was applied in order to calculate the mean building density per eldership. Residential buildings distribution was used as a proxy to estimate recreation demand. Residential buildings data from Lithuanian cadastre. Data in Km/Km2. Classes were carried out according to Quantile interval.RecreationEPSG:3035geonode:Soil_dem_90Soil Erosion Demand 1990MALPES, erosionEPSG:3035geonode:Soil_dem_00Soil Erosion Demand 2000MALPES, erosionEPSG:3035geonode:Soil_dem_06Soil Erosion Demand 2006MALPES, erosionEPSG:3035geonode:Soil_dem_12Soil Erosion Demand 2012MALPES, erosionEPSG:3035geonode:Soil_dem_18Soil Erosion Demand 2018MALPES, erosionEPSG:3035geonode:Soil_dem_22Soil Erosion Demand 2022MALPES, erosionEPSG:3035geonode:Soil_er_90Soil Erosion Regulation Supply 1990MALPES, erosionEPSG:3035geonode:Soil_er_00Soil Erosion Regulation Supply 2000MALPES, erosionEPSG:3035geonode:Soil_er_06Soil Erosion Regulation Supply 2006MALPES, erosionEPSG:3035geonode:Soil_er_12Soil Erosion Regulation Supply 2012MALPESEPSG:3035geonode:Soil_er_18Soil Erosion Regulation Supply 2018MALPES, erosionEPSG:3035geonode:Soil_er_22Soil Erosion Regulation Supply 2022MALPES, erosionEPSG:3035geonode:H2O_dem_90Water demand in Protected Areas 1990MALPESEPSG:3035geonode:H2O_dem_00No abstract provided Water demand in NRPs for 2000MALPESEPSG:3035geonode:H2O_dem_06Water demand in NRPs for 2006MALPESEPSG:3035geonode:H2O_dem_12Water demand in NRPs for 2012MALPESEPSG:3035geonode:H2O_dem_18Water demand in NRPs for 2018MALPESEPSG:3035geonode:H2O_dem_22Water demand in NRPs for 2022MALPESEPSG:3035geonode:H2O_surf_sup90Surface water supply in NRPs for 1990MALPESEPSG:3035geonode:H2O_surf_sup00Surface water supply in NRPs for 2000MALPESEPSG:3035geonode:H2O_surf_sup06Surface water supply in national and regional parks for 2006MALPESEPSG:3035geonode:H2O_surf_sup12Surface water supply in national and regional parks for 2012MALPESEPSG:3035geonode:H2O_surf_sup18Surface water supply in national and regional parks for 2018MALPESEPSG:3035geonode:H2O_surf_sup22Surface water supply in national and regional parks for 2022MALPESEPSG:3035geonode:Timber_demand_GeonodeNumber of companies operating (2018) according to the forest area for exploitation (2018) per municipality. The timber demand index calculated is a modified version of the formulas proposed by Kroll et al.(2012): (WP X NC) / FAE where, WP is the wood production (average 2013-2017), NC is ithe number of companies, and FAE the forest area allowed for exploitation at each municipality. Data in m3 per ha. Classes were carried out according to Quantile interval.Timber, DemandEPSG:4669geonode:ShapeAverage (2012-2016) stand productivity per municipality. Stand productivity is the ratio of wood production (m3) per stand dimension in hectares (ha). Data in m3 per ha. Classes were carried out according to Quantile interval.TimberEPSG:4669geonode:Fish_consumptionWild seafood demand was calculated from the average (2009-2019) of fish consumption per capita at eldership level. The average fish consumption was multiplied by the number of residents in the different elderships in 2013. Fish consumption per capita and Census 2013 data extracted from Lithuanian Official Statistics Portal, 2013 and Official Lithuanian Statistics Department 2019. Data in kg/eldership. Classes were carried out according to Natural jenks interval.Marine, Fish_consumptionEPSG:3035geonode:PotentialWild seafood potential was calculated from the modeled probability of occurrence of prey items of two important fish species in Lithuania: cod and flounder. Prey item composition was extracted from Šiaulys et al. (2012) and the occurrence of prey items extracted from Gogina et al. (2016). To map the probable occurrence of prey items the Maxent model Phillips et al. (2006) was applied for each species individually and then averaged together in the raster calculator tool of ArcGis. The environmental descriptors used in Maxent were extracted from BALANCE MODEL (Al-Hamdani and Reker, 2007; Bendtsen et al., 2007) and IOW Topographic maps from Seifert et al. (2011). Classes were carried out according to Natural jenks interval.Fisheries, MarineEPSG:3035geonode:supply_clipWild seafood supply was calculated from the cumulative average of fishing effort, fishing intensity and fishing ship density in the Lithuanian Exclusive Economic Zone. Fishing intensity and fishing ship density data was extracted from HELCOM (2018) and fishing effort from ICES (2015). Fishing intensity was averaged for the available years and fishing gear types (2006-2016), rasterized and resampled to 20 m resolution using ArcGis. Fishing effort was averaged for the available years and fishing gear types (2009-2013), rasterized and resampled to 20 m resolution using ArcGis. Fishing ship density was averaged for the available years (2006-2016) and resampled to 20 m resolution using ArcGis. Classes were carried out according to Natural jenks interval.Fisheries, MarineEPSG:3035geonode:WEldWind energy was measured using the annual average wind velocity (1971-2000) data from Worldclim (1km resolution). Using Zonal statistics, the index calculated was extracted at eldership level (average). Classes were carried out according to natural breaks interval. Annual average wind velocity data from: http://worldclim.org/version2Energy, WindEPSG:3035geonode:vl_cropProdES_1989_vl_cropProdES_1989No abstract providedfeatures, vl_cropProdES_1989_vl_cropProdES_1989EPSG:3346geonode:vl_cropProdES_2050_s2_ssp1_vl_cropProdES_2050No abstract providedfeatures, vl_cropProdES_2050_s2_ssp1_vl_cropProdES_2050_s2_ssp1EPSG:3346geonode:vl_cropProdES_2050_s2_ssp2_vl_cropProdES_2050No abstract providedfeatures, vl_cropProdES_2050_s2_ssp2_vl_cropProdES_2050_s2_ssp2EPSG:3346geonode:vl_cropProdES_2050_s2_ssp5_vl_cropProdES_2050No abstract providedfeatures, vl_cropProdES_2050_s2_ssp5_vl_cropProdES_2050_s2_ssp5EPSG:3346geonode:vl_microClimRegES_1989_outNo abstract providedfeatures, vl_microClimRegES_1989_outEPSG:3346geonode:vl_microClimRegES_2020_outNo abstract providedfeatures, vl_microClimRegES_2020_outEPSG:3346geonode:vl_microClimRegES_2050_s0_ssp1_outNo abstract providedfeatures, vl_microClimRegES_2050_s0_ssp1_outEPSG:3346geonode:vl_microClimRegES_2050_s0_ssp2_outNo abstract providedfeatures, vl_microClimRegES_2050_s0_ssp2_outEPSG:3346geonode:vl_microClimRegES_2050_s0_ssp5_outNo abstract providedfeatures, vl_microClimRegES_2050_s0_ssp5_outEPSG:3346geonode:vl_microClimRegES_2050_s1_ssp1_outNo abstract providedfeatures, vl_microClimRegES_2050_s1_ssp1_outEPSG:3346geonode:vl_microClimRegES_2050_s1_ssp2_outNo abstract providedfeatures, vl_microClimRegES_2050_s1_ssp2_outEPSG:3346geonode:vl_microClimRegES_2050_s1_ssp5_outNo abstract providedfeatures, vl_microClimRegES_2050_s1_ssp5_outEPSG:3346geonode:vl_microClimRegES_2050_s2_ssp1_outNo abstract providedfeatures, vl_microClimRegES_2050_s2_ssp1_outEPSG:3346geonode:vl_microClimRegES_2050_s2_ssp2_outNo abstract providedfeatures, vl_microClimRegES_2050_s2_ssp2_outEPSG:3346geonode:vl_microClimRegES_2050_s2_ssp5_outNo abstract providedfeatures, vl_microClimRegES_2050_s2_ssp5_outEPSG:3346absabs_2abs_3abs_4acosAddCoveragesAffineAggregateareaarea2AreaGridasinatanatan2attributeCountBandMergeBandSelectBarnesSurfaceBatchDownloadbetweenboundaryboundaryDimensionBoundsbufferBufferFeatureCollectionbufferWithSegmentsCategorizeceilcentroidclassifyClassifyByRangeClipCollectGeometriesCollection_AverageCollection_BoundsCollection_CountCollection_MaxCollection_MedianCollection_MinCollection_NearestCollection_SumCollection_UniqueConcatenatecontainsContourcontrastconvertconvexHullConvolveCoveragecosCountCoverageClassStatsCropCoveragecrossesdarkendateFormatdateParsedensifydesaturatedifferencedimensiondisjointdisjoint3Ddistancedistance3Ddouble2boolDownloadDownloadEstimatorendAngleendPointenvenvelopeEqualIntervalequalsExactequalsExactToleranceequalToexpexteriorRingFeatureFeatureClassStatsfloorgeometrygeometryTypegeomFromWKTgeomLengthGeorectifyCoverageGetFullCoveragegetGeometryNgetXgetYgetzgrayscalegreaterEqualThangreaterThanGridHeatmaphslidIEEEremainderif_then_elseImportinin10in2in3in4in5in6in7in8in9InclusionFeatureCollectionint2bboolint2ddoubleinteriorPointinteriorRingNInterpolateintersectionIntersectionFeatureCollectionintersectsintersects3DisClosedisCoverageisEmptyisInstanceOfisLikeisNullisometricisRingisSimpleisValidisWithinDistanceisWithinDistance3DJenkslabelPointlengthlessEqualThanlessThanlightenlistlistMultiplylogLRSGeocodeLRSMeasureLRSSegmentmaxmax_2max_3max_4minmin_2min_3min_4mincircleminimumdiameterminrectanglemixmoduloMultiplyCoveragesNearestNormalizeCoveragenotnotEqualTonumberFormatnumberFormat2numGeometriesnumInteriorRingnumPointsoctagonalenvelopeoffsetoverlapsPagedUniqueparameterparseBooleanparseDoubleparseIntparseLongpiPointBufferspointNPointStackerPolygonExtractionpolygonizePolyLabellerpowpropertyPropertyExistsQuantileQueryqueryCollectionquerySinglerandomRangeLookupRasterAsPointCollectionRasterZonalStatisticsRasterZonalStatistics2RecodeRectangularCliprelaterelatePatternreprojectReprojectGeometryrescaleToPixelsrintroundround_2roundDoublesaturateScaleCoveragesdo_nnsetCRSshadesimplifysinSnapspinsplitPolygonsqrtStandardDeviationstartAnglestartPointStoreCoveragestrCapitalizestrConcatstrEndsWithstrEqualsIgnoreCasestrIndexOfstringTemplatestrLastIndexOfstrLengthstrMatchesstrPositionstrReplacestrStartsWithstrSubstringstrSubstringStartstrToLowerCasestrToUpperCasestrTrimstrTrim2strURLEncodeStyleCoveragesymDifferencetantinttoDegreestoRadianstouchestoWKTTransformTransparencyFillunionUnionFeatureCollectionUniqueUniqueIntervalVectorToRasterVectorZonalStatisticsverticeswithin