20210720
09305900
1.0
FALSE
CalculateField All_r_data_normalized_ESV2020_LULCV2_socioeco_pc KEY_ES_SUM "[TOT_C_HA_1]+ [A_WYIELD_1]+ [SDR_INDE_1]+ [HQ_INDEX_1]+ [POL_ABUND_]+ [PUD_YR_AVG]- [NP_EXP]" VB #
CopyFeatures P:\02_projects\2020_WYSS_LAOS\Data\GIS\InVEST_output_V2020\All_r_data_normalized_ES_LULC_V20_socioeco_pc.shp U:\service_publishing\lukas.wuersch\EcosystemServices\EcosystemServicesPublish\EcosystemServicesPublish.gdb\All_r_data_normalized_ES_LULC_V20_socioeco_pc # # # #
FeatureClassToFeatureClass All_r_data_normalized_ES_LULC_V20_socioeco_pc U:\service_publishing\lukas.wuersch\EcosystemServices\EcosystemServicesPublish\EcosystemServicesPublish.gdb l_es_supply_normalized_2020 # "VILLAGE "Village code" true true false 4 Long 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,VILLAGE,-1,-1;PCODE "Province code" true true false 2 Short 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,PCODE,-1,-1;PNAME "Province name" true true false 50 Text 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,PNAME,0,50;DCODE "District code" true true false 2 Short 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,DCODE,-1,-1;DNAME "District name" true true false 50 Text 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,DNAME,0,50;POLY_AREA "Village size (ha)" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,POLY_AREA,-1,-1;TOT_C_HA_1 "Normalized total carbon" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,TOT_C_HA_1,-1,-1;A_WYIELD_1 "Normalized annual water yield" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,A_WYIELD_1,-1,-1;SWY_QF_H_1 "Normalized quickflow" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,SWY_QF_H_1,-1,-1;RECHARGE_1 "Normalized recharge" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,RECHARGE_1,-1,-1;BASELINE_1 "Normalized baseline" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,BASELINE_1,-1,-1;N_EXP_HA_1 "Normalized Nitrogen" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,N_EXP_HA_1,-1,-1;P_EXP_HA_1 "Normalized Phosphorus" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,P_EXP_HA_1,-1,-1;SDR_INDE_1 "Normalized sediment retention index" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,SDR_INDE_1,-1,-1;HQ_INDEX_1 "Normalized habitat quality" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,HQ_INDEX_1,-1,-1;POL_ABUND_ "Normalized pollination abundance" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,POL_ABUND_,-1,-1;PUD_YR_AVG "Average photo user days per year and village" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,PUD_YR_AVG,-1,-1;NP_EXP "Normalized sum of normalized nitrogen and phosphorus" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,NP_EXP,-1,-1;LLRVAYA "Normalized lowland rice" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,LLRVAYA,-1,-1;ULRVAYA "Normalized upland rice" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,ULRVAYA,-1,-1;SWCVAYA "Normalized sweet corn" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,SWCVAYA,-1,-1;JBTVAYA "Normalized job's tears" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,JBTVAYA,-1,-1;CASVAYA "Normalized cassava" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,CASVAYA,-1,-1;BANVAYA "Normalized banana" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,BANVAYA,-1,-1;MAZVAYA "Normalized maize" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,MAZVAYA,-1,-1;FPNN_SUM "Sum of normalized food production values (seven)" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,FPNN_SUM,-1,-1;KEY_ES_SUM "Sum of key ES" true true false 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,KEY_ES_SUM,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,All_r_data_normalized_ES_LULC_V20_socioeco_pc,Shape_Area,-1,-1" #
UpdateSchema "CIMDATA=<CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><WorkspaceConnectionString>DATABASE=W:\service_publishing\lukas.wuersch\EcosystemServices\EcosystemServicesPublish\EcosystemServicesPublish.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>l_es_supply_normalized_2020</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMStandardDataConnection>" "<operationSequence><workflow><DeleteField><field_name>LLRVAYA</field_name><field_name>ULRVAYA</field_name><field_name>SWCVAYA</field_name><field_name>JBTVAYA</field_name><field_name>CASVAYA</field_name><field_name>BANVAYA</field_name><field_name>MAZVAYA</field_name><field_name>FPNN_SUM</field_name></DeleteField></workflow><workflow><AlterField><field_name>TOT_C_HA_1</field_name><field_alias>Total carbon (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>A_WYIELD_1</field_name><field_alias>Annual water yield (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>SWY_QF_H_1</field_name><field_alias>Quickflow (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>RECHARGE_1</field_name><field_alias>Recharge (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>BASELINE_1</field_name><field_alias>Baseline (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>N_EXP_HA_1</field_name><field_alias>Nitrogen (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>P_EXP_HA_1</field_name><field_alias>Phosphorus (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>SDR_INDE_1</field_name><field_alias>Sediment retention (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>HQ_INDEX_1</field_name><field_alias>Habitat quality (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>POL_ABUND_</field_name><field_alias>Pollination abundance</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>PUD_YR_AVG</field_name><field_alias>Photo user days (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>NP_EXP</field_name><field_alias>Sum of normalized nitrogen and phosphorus (normalized)</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>"
l_es_supply_normalized_2020
002
file://\\nas-cde.unibe.ch\cde-store-01\wyssacademy_geodata\service_publishing\lukas.wuersch\EcosystemServices\EcosystemServicesPublish\EcosystemServicesPublish.gdb
Local Area Network
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
WGS_1984_UTM_Zone_48N
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS["WGS_1984_UTM_Zone_48N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",105.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",32648]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>32648</WKID><LatestWKID>32648</LatestWKID></ProjectedCoordinateSystem>
20230111
08343800
20230111
08343800
Microsoft Windows 10 Version 10.0 (Build 19043) ; Esri ArcGIS 12.9.2.32739
Normalized ecosystem service supply in Laos (2020)
<div style='text-align:Left;'><p style='margin:0 0 0 0;'><span style='font-weight:bold;'>Methods / Lineage:</span></p><p style='margin:0 0 0 0;'><span><span>Ecosystem service supply was modelled with </span></span><a href='https://naturalcapitalproject.stanford.edu:443/software/invest' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>InVEST</span></span></a><span>. The model output was aggregated for each village. For each ecosystem service, outputs per village and hectare were min-max normalized and stretched to a range from zero to one</span><span>. Village tract boundaries were taken from </span><a href='https://www.k4d.la:443/' style='text-decoration:underline;' rel='nofollow ugc'><span>Decide K4D </span></a><span>and do not represent official boundaries.</span></p><p style='margin:0 0 0 0;'><span><span></span></span></p><p style='margin:0 0 0 0;'><span style='font-weight:bold;'><span>Relevant Fields:</span></span></p><ul><li><p><span>Total carbon (normalized)</span></p></li><li><p><span>Nitrogen (normalized)</span></p></li><li><p><span>Phosphorus (normalized)</span></p></li><li><p><span>Sediment retention (normalized)</span></p></li><li><p><span>Annual water yield (normalized)</span></p></li><li><p><span>Quickflow (normalized)</span></p></li><li><p><span>Recharge (normalized)</span></p></li><li><p><span>Baseline flow (normalized)</span></p></li><li><p><span>Habitat quality (normalized)</span></p></li><li><p><span>Pollination abundance (normalized) </span></p></li><li><p><span>Photo user days (normalized): The average number of photos uploaded to the photo-sharing website flickr (per village). Average of the years 2005-2017. Is taken as a proxy to measure recreation / tourism ES</span></p></li><li><p><span>Sum of key ES (normalized): sum of normalized total carbon, annual water yield, nutrient export, sediment retention, habitat quality, pollinator abundance, food production (*MAF Lao PDR Agriculture Census 2010-2011), and recreation and tourism</span></p></li></ul><p style='margin:0 0 0 0;'><span></span></p><p style='margin:1 1 1 0;'><span></span></p><p style='margin:1 1 1 0;'><span style='font-weight:bold;'>Point of Contact:</span></p><p style='margin:1 1 1 0;'>Sandra Eckert (sandra.eckert@cde.unibe.ch)<br /></p><p><span style='font-weight:bold;'>Spatial Reference System:</span></p><p><span>WGS 1984 UTM Zone 48N</span></p><p><span></span></p></div>
Laos
ecosystem service
supply
2015
InVEST
per village
total carbon
nitrogen
phosphorus
sediment
water
quickflow
habitat quality
pollinator
photo user days
This service provides normalized ecosystem service supply values per village in Laos in the year 2020. For each ecosystem service, outputs per village and hectare were min-max normalized and stretched to a range from zero to one. Zero represents the lowest value of an ecosystem service, one its highest value. Normalized values enable comparison between different ecosystem services as well as comparison between different villages.
Centre for Development and Environment (CDE); Wyss Academy for Nature
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). The data can be copied and redistributed as long as the creator (CDE) is quoted. However, the data cannot be used for commercial purposes and may not be redistributed when modified.</SPAN></P></DIV></DIV></DIV>
File Geodatabase Feature Class
dataset
EPSG
6.2(3.0.1)
0
Simple
FALSE
0
TRUE
FALSE
l_es_supply_normalized_2020
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
VILLAGE
Village code
Integer
4
0
0
PCODE
Province code
SmallInteger
2
0
0
PNAME
Province name
String
50
0
0
DCODE
District code
SmallInteger
2
0
0
DNAME
District name
String
50
0
0
POLY_AREA
Village size (ha)
Double
8
0
0
TOT_C_HA_1
Normalized total carbon
Double
8
0
0
A_WYIELD_1
Normalized annual water yield
Double
8
0
0
SWY_QF_H_1
Normalized quickflow
Double
8
0
0
RECHARGE_1
Normalized recharge
Double
8
0
0
BASELINE_1
Normalized baseline
Double
8
0
0
N_EXP_HA_1
Normalized Nitrogen
Double
8
0
0
P_EXP_HA_1
Normalized Phosphorus
Double
8
0
0
SDR_INDE_1
Normalized sediment retention index
Double
8
0
0
HQ_INDEX_1
Normalized habitat quality
Double
8
0
0
POL_ABUND_
Normalized pollination abundance
Double
8
0
0
PUD_YR_AVG
Average photo user days per year and village
Double
8
0
0
NP_EXP
Normalized sum of normalized nitrogen and phosphorus
Double
8
0
0
LLRVAYA
Normalized lowland rice
Double
8
0
0
ULRVAYA
Normalized upland rice
Double
8
0
0
SWCVAYA
Normalized sweet corn
Double
8
0
0
JBTVAYA
Normalized job's tears
Double
8
0
0
CASVAYA
Normalized cassava
Double
8
0
0
BANVAYA
Normalized banana
Double
8
0
0
MAZVAYA
Normalized maize
Double
8
0
0
FPNN_SUM
Sum of normalized food production values (seven)
Double
8
0
0
KEY_ES_SUM
Sum of key ES
Double
8
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20230111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