Study On Comprehensive And Sequential Classification System Of Grasslands Based On3S Technology And Soil Moisture And Temperature Index

Grassland classification is a fundamental need of grassland science. Meanwhile it is alsoa challenge to develop a comprehensive grassland classification system because of themultivariable and multi-functional features of grassland ecosystem.The Comprehensive and Sequential Classification System of Grassland (CSCS), one ofwell known grassland classification systems in China and even over the world, involves ahierarchy of three classification levels(class-subclass-type, class is the basic level) and isadvanced in quantification indicators.However, there are at least two aspects need to be improved at the basic classificationlevel of CSCS:1) the grasslands are grouped into classes according to the data involvingannual precipitation and accumulative temperature, which are collected from meteorologicalstations.These data reflect the near-surface atmosphere hydrothermal conditions instead of theactual habitat of grasses;2) the data of precipitation and temperature from ground observationcan only present the conditions within a small area, but they are used through extrapolation toa larger region.In order to resolve the problems, the areal data of land surface temperature and soilmoisture are introduced by quantitative remote sensing as main data sources for the basicclassification level of CSCS to replace the parameters of precipitation and atmospheretemperature from ground observation. In this paper, the MODIS land surface temperatureproduct (MYD11A1, daily with1km resolution) and MODIS land surface reflection product(MYD09GA, daily with0.5km resolution) of Gansu Province in2008were used to invertsoil moisture based on Thermal Inertia Model with the help of a Soil Moisture InversionPlatform (SMIP) developped from ENVI/IDL. Then, the annual accumulative land surfacetemperature (＞0℃Σθ) and annual sum of soil moisture were carried out, moreover, fit withannual accumulative temperature (＞0℃Σθ′) and precipitation data from meteorologicalstations respectively. Thermal zones were determined by temperature and humidity zones byK-value (moisture index), grassland class was obtained by coupling the thermal zones andhumidity zones. Finally, the grassland types were verified through the field investigation andaccuracy assessment was tested with confusion matrix.The main results are as follows: 1) the grassland in Gansu occupies five thermal zones (Frigid-Cold temperate-Cooltemperate-Warm temperate-Warm-Subtropical) and six humidity zones (Extrarid-Arid-Semiarid-Subhumid-Perhumid);2) there are26possible types present in Gansu Province, and three grassland classes thatcover the largest area in Gansu are Warm temperate-arid warm temperate zonal semidesert(ⅣB11), Cool temperate-arid temperate zonal semidesert (ⅢB10) and Cold temperateperhumid taiga forest (ⅡF37), with the total area of these three is17.83million ha,accounting for44.43%of the total grassland area in Gansu;3) the geographical distribution of grassland type indicates significantly vertical zonalitypattern: with the altitude decreasing, frigid series grassland, cold temperate series grassland,cool temperate series grassland, warm temperate series grassland and warm series grasslanddistribute successively from southwest to northeast, which fit the terrain of Gansu Province;4) accuracy assessment shows: the overall accuracy of grassland classification is70.11%and the kappa coefficient is0.67. The research solved the problem of transforming fromscattered site data to regional polygon data in CSCS and the problem of uncertain borderlinein punctate data extrapolation, and provide a new approach to the utilization of CSCS, whichcould carry forward the practical progress of CSCS.