20200726111620001.0ISO 19139 Metadata Implementation Specification GML3.2FALSELSP_Obs_sum_KGZ_pastures_NA_masked_cxq.tiffile://C:\Metadata\RSE\A3\LSP_Obs_sum_KGZ_pastures_NA_masked_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>16FALSELZW1TIFFFALSEcontinuousunsigned integer65535202007281559280020200728155946001500000005000ISO19139 Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321LSP_Obs_sum_KGZ_pastures_NA_masked_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 A3 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 the total number of observations over 17 years used for successful land surface phenology (LSP) fits over pasturelands calculated using Landsat surface reflectance data served to calculate NDVI as a proxy for active green vegetation, and MODIS LST for Accumulated Growing Degree Days (AGDD) as a proxy for insolation thru application of downward-arching convex quadratic (CxQ) function to model LSP from 2001 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 calculate AGDD, we used the MODIS/Terra and MODIS/Aqua Land Surface Temperature/Emissivity products at 1 km spatial resolution (M{O|Y}D11A2 V006), which provide an average 8-day land surface temperature (LST) for all M{O|Y}D11A1 LST pixels collected within the 8-day time frame (Wan et al., 2015). We downloaded two MODIS tiles (h23v04 and h23v05) of MODIS/Terra from 2001 and MODIS/Aqua from 2002 through the end of 2017. We resampled the data to 30 m pixel resolution using bilinear resampling.
For description of AGDD calculation see in Credits of Figure 5 'AGDD_mean_KGZ_pastures_cxq.tif'.
For vegetation information, we worked with the Landsat Collection 1 Tier 1 Level-1 Precision and Terrain (L1TP) corrected product from 2001 to the end of 2017 that is generated from Landsat 8 Operational Land Imager (OLI), Landsat 7 ETM+, and Landsat 5 Thematic Mapper (TM) (USGS EROS, 2017). Surface reflectance NDVI data were obtained by downloading 13,285 images in 33 tiles (WRS-2 Paths 147 to 155 and Rows 30 to 33) from the USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface (https://espa.cr.usgs.gov/). We used an inter-calibration equation to adjust Landsat 7 ETM+ surface NDVI and Landsat 5 TM surface NDVI to the surface Landsat 8 OLI NDVI, which on average has higher values (Roy et al., 2016):
NDVI_OLI = 0.0235 + 0.9723 × {NDVI_TM | NDVI_ETM+}
Roy, D.P., Kovalskyy, V., Zhang, H.K., Vermote, E.F., Yan, L., Kumar, S.S., Egorov, A., 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70.
To model land surface phenology (LSP) metrics, we used a downward-arching convex quadratic (CxQ) function to
model LSP (de Beurs and Henebry, 2004; de Beurs and Henebry, 2010a;Henebry and de Beurs, 2013) as follows:
NDVI = α + β × AGDD + γ × AGDD^2
Here, we provide a layer that is the total number of observations over 17 years used for successful fits.
For each pixel in the study area, we used the fitted parameter coefficients—intercept (α), slope (β), and quadratic (γ)—to calculate phenological metrics (phenometrics) for each year from 2001 to 2017.
Peak Height PH = α − (β^2 / 4γ), the maximum modeled NDVI
Thermal Time to Peak TTP = −β / 2γ, the quantity of AGDD required to reach PH; corresponds to duration of modeled green-up phase
Area Under the Curve up to PH and TTP AUC = Σi=1,t (NDVIt + NDVIt−1) / 2 × (AGDDt − AGDDt−1), numerically integrating by trapezoidal method the NDVI time series as a function of thermal time, serves as a proxy of pasture productivity during the green-up phase. We used threshold NDVI > 0.1 and AGDD > 100 to avoid including non-vegetated or snow-covered pixels.
de Beurs, K.M., Henebry, G.M., 2004. Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens. Environ. 89, 497–509. https://doi.org/10.1016/J.RSE.2003.11.006.
de Beurs, K.M., Henebry, G.M., 2010a. Spatio-temporal statistical methods for modelling land surface phenology. In: Hudson, I.L., Keatley, M.R. (Eds.), Phenological Research.Springer Science+Business Media B.V, Dordrecht, pp. 177–208. https://doi.org/10.1007/978-90-481-3335-2_9.
Henebry, G.M., 2013. Phenologies of North American grasslands and grasses. In:Schwartz, M.D. (Ed.), Phenology: An Integrative Environmental Science. Springer Netherlands, Dordrecht, pp. 197–210. https://doi.org/10.1007/978-94-007-6925-0.Kyrgyzstantotal number of observations for succesful phenology modellingKyrgyzstanFrom 2001 to the end of 2017.30external509923e6175f6463f5ee072c2d914506Geoffrey 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_1356.0000000.00000016total number of observations over 17 years used for successful land surface phenology (LSP) fits over pasturelands calculated using Landsat surface reflectance data served to calculate NDVI as a proxy for active green vegetation, and MODIS LST for Accumulated Growing Degree Days (AGDD) as a proxy for insolation thru application of downward-arching convex quadratic (CxQ) function to model LSP from 2001 to the end of 2017.FalseFalseFalseFalseFalse2020072857579F1B-0F20-4566-98E5-B7B72DD04FE2external509923e6175f6463f5ee072c2d914506Geoffrey 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