20200722100119001.0ISO 19139 Metadata Implementation Specification GML3.2FALSELDoS_mean_rs_pastures_KGZ_reclass_cxq.tiffile://C:\Metadata\RSE\Fig3\LDoS_mean_rs_pastures_KGZ_reclass_cxq.tifLocal Area Network004-1984770.000000-1101330.0000001312980.0000001767060.0000001ProjectedGCS_WGS_1984Linear Unit: Meter (1.000000)Albers_Conic_Equal_Area<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/10.6'><WKT>PROJCS["Albers_Conic_Equal_Area",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",95.0],PARAMETER["standard_parallel_1",15.0],PARAMETER["standard_parallel_2",65.0],PARAMETER["latitude_of_origin",30.0],UNIT["Meter",1.0]]</WKT><XOrigin>-16934700</XOrigin><YOrigin>-8092500</YOrigin><XYScale>265939144.32322362</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></ProjectedCoordinateSystem>32FALSELZW1TIFFFALSEcontinuousfloating point-3.4e+38202007281554410020200728155500001500000005000ISO19139 Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321LDoS_mean_rs_pastures_KGZ_reclass_cxq.tifraster digital data2020-01-31T00:00:00Remote Sensing of Environment2400034-4257https://doi.org/10.1016/j.rse.2020.111675external509923e6175f6463f5ee072c2d914506Geoffrey M Henebry Department of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations (CGCEO), Michigan State UniversityProfessor1405 S. Harrison Road, Manly Miles Building 218East LansingMichigan48823henebryg@msu.eduUSTrueGeoffrey M Henebry externalbf2d0fdaa84020f82abb5f87d71d59ccMonika Anna TomaszewskaMichigan State UniversityResearch Associate1405 S. Harrison Road, Manly Miles Building 218East LansingMI48823monikat@msu.eduUSTrueMonika Anna Tomaszewska168.12650980.72852043.88860038.765312KyrgyzstanThis raster layer has been used to create Figure 3 in Tomaszewska, M.A., Nguyen, L.H., Henebry, G.M., 2020. Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics. Remote Sens. Environ. 240, 111675. https://doi.org/10.1016/j.rse.2020.111675
It presents 17 years mean values of Last Date of Snow (LDoS) - snow metric calculated from the Terra MODIS snow cover product version 6 from 2000 to the end of 2017. Details in credits section.<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN STYLE="font-size:14pt">Many studies have shown that high elevation environments are among very sensitive to climatic changes and where impacts are exacerbated. Across Central Asia, which is especially vulnerable to climate change due to aridity, the ability of global climate projections to capture the complex dynamics of mountainous environments is particularly limited. Over montane Central Asia, agropastoralism constitutes a major portion of the rural economy. Extensive herbaceous vegetation forms the basis of rural economies in Kyrgyzstan. Here we focus on snow cover seasonality and the effects of terrain on phenology in highland pastures using remote sensing data for 2001–2017. First, we describe the thermal regime of growing season using MODerate Resolution Imaging Spectrometer (MODIS) land surface temperature (LST) data, analyzing the modulation by elevation, slope, and aspect. We then characterized the phenology in highland pastures with metrics derived from modeling the land surface phenology using Landsat normalized difference vegetation index (NDVI) time series together with MODIS LST data. Using rank correlations, we then analyzed the influence of four metrics of snow cover seasonality calculated from MODIS snow cover composites—first date of snow, late date of snow, duration of snow season, and the number of snow-covered dates (SCD)—on two key metrics of land surface phenology in the subsequent growing season, specifically, peak height (PH; the maximum modeled NDVI) and thermal time to peak (TTP; the amount of growing degree-days accumulated during modeled green-up phase). We evaluated the role of terrain features in shaping the relationships between snow cover metrics and land surface phenology metrics using exact multinomial tests of equivalence. Key findings include (1) a positive relationship between SCD and PH occurred in over 1664 km2 at p < 0.01 and 5793 km2 at p < 0.05, which account for>8% of 68,881 km2 of the pasturelands analyzed in Kyrgyzstan; (2) more negative than positive correlations were found between snow cover onset and PH, and more positive correlations were observed between snowmelt timing and PH, indicating that a longer snow season can positively influence PH; (3) significant negative correlations between TTP and SCD appeared in 1840 km2 at p < 0.01 and 6208 km2 at p < 0.05, and a comparable but smaller area showed negative correlations between TTP and last date of snow (1538 km2 at p < 0.01 and 5188 km2 at p < 0.05), indicating that under changing climatic conditions toward earlier spring warming, decreased duration of snow cover may lead to lower pasture productivity, thereby threatening the sustainability of montane agropastoralism; and (4) terrain had a stronger influence on the timing of last date of snow cover than on the number of snow-covered dates, with slope being more important than aspect, and the strongest effect appearing from the interaction of aspect and steeper slopes. In this study, we characterized the snow-phenology interactions in highland pastures and revealed strong dependencies of pasture phenology on timing of snowmelt and the number of snow-covered dates.</SPAN></P></DIV></DIV></DIV>From: Tomaszewska, M.A., Nguyen, L.H., Henebry, G.M., 2020. Land surface phenology in the highland pastures of montane Central Asia: Interactions with snow cover seasonality and terrain characteristics. Remote Sens. Environ. 240, 111675. https://doi.org/10.1016/j.rse.2020.111675
To calcutate snow cover metrics, we used the most recent version of the MODIS Terra snow cover 8-
day composites (MOD10A2 V006; Riggs and Hall, 2015) distributed by the National Snow and Ice Data Center (https://nsidc.org/). The nominal spatial resolution is 500 m, and the data are provided in a sinusoidal projection. MOD10A2 product reports the maximum snow cover extent observed during 8-day period by compositing observations from the MODIS/Terra Snow Cover Daily L3 Global 500 m Grid product (MOD10A1 V006), where the snow cover information is derived using the Normalized Difference Snow Index (NDSI). The annual dataset contains 46 8-day composites. We downloaded two MODIS tiles (h23v04 and h23v05) from 2000 to the end of 2017, resampled them to 30 m using nearest neighbors.
We described snow seasonality by generating four temporal metrics for each snow season. We defined our observation window to bound the snow cover season each year by starting on the day of year (DOY) 169 (approximately the summer solstice) and extending to DOY 168 in the following year (DOY169year through DOY168year+1). This approach
enabled us to identify the first and last appearances of snow cover during the cold season. We generated four snow cover metrics: First Date of Snow (FDoS), Last Date of Snow (LDoS), Duration of Snow Season (DoSS), and the number of Snow-Covered Dates (SCD). FDoS is the composite date when the pixel is flagged as snow for the first time each snow cover season. LDoS is, conversely to FDoS, the last composite date with pixel marked as snow in each of the snow season. DoSS is the difference in composite dates between LDoS and FDoS multiplied by 8 to align with DOY [(LDoS – FDoS+1)*8]; SCD is a number of composites with snow cover present between FDoS and LDoS, also multiplied by 8.
Riggs, G.A., Hall, D.K., 2015. MODIS Snow Products Collection 6 User Guide. NSIDC.
https://nsidc.org/sites/nsidc.org/files/files/MODIS-snow-user-guide-C6.pdf.
Rossi, M., Niedrist, G., Asam, S., Tonon, G., Tomelleri, E., Zebisch, M., Rossi, M., Niedrist,Kyrgyzstanmean Last Date of Snow (LDoS)Kyrgyzstanfrom 2000 to the end of 201730external509923e6175f6463f5ee072c2d914506Geoffrey M Henebry Department of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations (CGCEO), Michigan State UniversityProfessor1405 S. Harrison Road, Manly Miles Building 218East LansingMichigan48823henebryg@msu.eduUSTrueGeoffrey M Henebry external509923e6175f6463f5ee072c2d914506Geoffrey M Henebry Department of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations (CGCEO), Michigan State UniversityProfessor1405 S. Harrison Road, Manly Miles Building 218East LansingMichigan48823henebryg@msu.eduUSTrueGeoffrey M Henebry externalbf2d0fdaa84020f82abb5f87d71d59ccMonika Anna TomaszewskaMichigan State UniversityResearch Associate1405 S. Harrison Road, Manly Miles Building 218East LansingMI48823monikat@msu.eduUSTrueMonika Anna Tomaszewskaexternalbf2d0fdaa84020f82abb5f87d71d59ccMonika Anna TomaszewskaMichigan State UniversityResearch Associate1405 S. Harrison Road, Manly Miles Building 218East LansingMI48823monikat@msu.eduUSTrueMonika Anna TomaszewskaRaster DatasetGeoTiffdatasetEPSG210-1984770.000000 1312980.000000-1984770.000000 1767060.000000-1101330.000000 1767060.000000-1101330.000000 1312980.000000-1543050.000000 1540020.0000002944830.0000001513630.000000Band_1361.000000120.0000003217 years mean values of Last Date of Snow (LDoS) - snow metric calculated from the Terra MODIS snow cover product version 6 from 2000 to the end of 2017.FalseFalseFalseFalseFalse202007289D97033D-91E2-4C4E-90C0-C1D5D648FCE6external509923e6175f6463f5ee072c2d914506Geoffrey M Henebry Department of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations (CGCEO), Michigan State UniversityProfessor1405 S. Harrison Road, Manly Miles Building 218East LansingMichigan48823henebryg@msu.eduUSTrueGeoffrey M Henebry external509923e6175f6463f5ee072c2d914506Geoffrey M Henebry Department of Geography, Environment, and Spatial Sciences & Center for Global Change and Earth Observations (CGCEO), Michigan State UniversityProfessor1405 S. Harrison Road, Manly Miles Building 218East LansingMichigan48823henebryg@msu.eduUSTrueGeoffrey M Henebry externalbf2d0fdaa84020f82abb5f87d71d59ccMonika Anna TomaszewskaMichigan State UniversityResearch Associate1405 S. Harrison Road, Manly Miles Building 218East LansingMI48823monikat@msu.eduUSTrueMonika Anna Tomaszewskaexternalbf2d0fdaa84020f82abb5f87d71d59ccMonika Anna TomaszewskaMichigan State UniversityResearch Associate1405 S. Harrison Road, Manly Miles Building 218East LansingMI48823monikat@msu.eduUSTrueMonika Anna Tomaszewska