农业工程学报
農業工程學報
농업공정학보
2013年
1期
126-133
,共8页
吴静%李纯斌%胡自治%张德罡%柳小妮%申正东
吳靜%李純斌%鬍自治%張德罡%柳小妮%申正東
오정%리순빈%호자치%장덕강%류소니%신정동
遥感%水分%温度%草原综合顺序分类法%甘肃省
遙感%水分%溫度%草原綜閤順序分類法%甘肅省
요감%수분%온도%초원종합순서분류법%감숙성
remote sensing%moisture%temperature%comprehensive and sequential classification system of grasslands (CSCS)%Gansu province
为了推进草原综合顺序分类的实用化进程,在草地发生学原理指导下,在草原综合顺序分类中引入甘肃省2008年每日1 km 分辨率的 MODIS 地表温度产品(MYD11A1)和0.5 km 分辨率的 MODIS 地表反照率产品(MYD09GA),反演土壤水分和地表年积温,划分热量级和湿润度级,并对甘肃省草地进行分类,以野外调查数据为相对真值验证了结果,评价了分类精度.结果表明:甘肃省天然草地横跨寒冷-寒温-微温-暖温-暖热5个热量级,极干-干旱-微干-微润-湿润-潮湿6个湿润度级,共26个类,其中暖温干旱暖温带半荒漠类、微温干旱温带半荒漠类和寒温潮湿温性针叶林类是甘肃省最主要的几种草地类型,占全省面积的43.43%;草地类的分布呈现出明显的垂直地带性,类别划分结果符合研究区域的气候、地理位置和地貌特征.研究减少了以往综合顺序分类对气象站点分布和插值方法的依赖性,从数据源的角度解决了综合顺序分类法中站点数据向区域数据转换这一难题,改善了点数据外推的边界模糊问题,拓展了草原综合顺序分类的研究手段和方法,为推进草原综合顺序分类实用化进程提供了新的思路.
為瞭推進草原綜閤順序分類的實用化進程,在草地髮生學原理指導下,在草原綜閤順序分類中引入甘肅省2008年每日1 km 分辨率的 MODIS 地錶溫度產品(MYD11A1)和0.5 km 分辨率的 MODIS 地錶反照率產品(MYD09GA),反縯土壤水分和地錶年積溫,劃分熱量級和濕潤度級,併對甘肅省草地進行分類,以野外調查數據為相對真值驗證瞭結果,評價瞭分類精度.結果錶明:甘肅省天然草地橫跨寒冷-寒溫-微溫-暖溫-暖熱5箇熱量級,極榦-榦旱-微榦-微潤-濕潤-潮濕6箇濕潤度級,共26箇類,其中暖溫榦旱暖溫帶半荒漠類、微溫榦旱溫帶半荒漠類和寒溫潮濕溫性針葉林類是甘肅省最主要的幾種草地類型,佔全省麵積的43.43%;草地類的分佈呈現齣明顯的垂直地帶性,類彆劃分結果符閤研究區域的氣候、地理位置和地貌特徵.研究減少瞭以往綜閤順序分類對氣象站點分佈和插值方法的依賴性,從數據源的角度解決瞭綜閤順序分類法中站點數據嚮區域數據轉換這一難題,改善瞭點數據外推的邊界模糊問題,拓展瞭草原綜閤順序分類的研究手段和方法,為推進草原綜閤順序分類實用化進程提供瞭新的思路.
위료추진초원종합순서분류적실용화진정,재초지발생학원리지도하,재초원종합순서분류중인입감숙성2008년매일1 km 분변솔적 MODIS 지표온도산품(MYD11A1)화0.5 km 분변솔적 MODIS 지표반조솔산품(MYD09GA),반연토양수분화지표년적온,화분열량급화습윤도급,병대감숙성초지진행분류,이야외조사수거위상대진치험증료결과,평개료분류정도.결과표명:감숙성천연초지횡과한랭-한온-미온-난온-난열5개열량급,겁간-간한-미간-미윤-습윤-조습6개습윤도급,공26개류,기중난온간한난온대반황막류、미온간한온대반황막류화한온조습온성침협림류시감숙성최주요적궤충초지류형,점전성면적적43.43%;초지류적분포정현출명현적수직지대성,유별화분결과부합연구구역적기후、지리위치화지모특정.연구감소료이왕종합순서분류대기상참점분포화삽치방법적의뢰성,종수거원적각도해결료종합순서분류법중참점수거향구역수거전환저일난제,개선료점수거외추적변계모호문제,탁전료초원종합순서분류적연구수단화방법,위추진초원종합순서분류실용화진정제공료신적사로.
Grassland classification is a fundamental need of grassland science. It is also a challenge to develop a comprehensive grassland classification system because of the multivariable and multi-functional features of grassland ecosystem. The Comprehensive and Sequential Classification System of Grassland (CSCS), one of well known grassland classification systems, involves a hierarchy of three classification levels (class-subclass-type, class is the basic level) and is advanced in quantification indicators. However, there are at least two aspects need to be improved at the basic classification level of CSCS: 1) the grasslands are grouped into classes according to the data involving annual precipitation and accumulative temperature, which are collected from meteorological stations. These data reflect the near-surface atmosphere hydrothermal conditions instead of the actual habitat of grasses; 2) The data of precipitation and temperature from ground observation can only present the conditions within a small area, but they are used through extrapolation to a larger region. In order to resolve the problems, the areal data of land surface temperature and soil moisture are introduced by quantitative remote sensing as main data sources for the basic classification level of CSCS to replace the parameters of precipitation and atmosphere temperature from ground observation. In this paper, the MODIS land surface temperature product (MYD11A1, daily with 1km resolution) and MODIS land surface reflection product (MYD09GA, daily with 0.5 km resolution) of Gansu province in 2008 were used to invert soil moisture based on Thermal Inertia Model with the help of a Soil Moisture Inversion Platform (SMIP) developped from ENVI/IDL. Then, the annual accumulative land surface temperature (>0℃Σθ) and annual sum of soil moisture were carried out, and then they were fitted with annual accumulative temperature (>0℃Σθ') and precipitation data from meteorological stations respectively. Thermal zones were determined by temperature and humidity zones by K-value (moisture index), and the grassland class was obtained by coupling thermal zones and humidity zones. Finally, the grassland types were verified through the field investigation and the accuracy assessment was tested with confusion matrix. The results showed that: 1) The grassland in Gansu occupies five thermal zones ( frigid, cold temperate, cool temperate, warm temperate, warm subtropical) and six humidity zones ( extrarid, arid, semiarid, subhumid, humid , perhumid); 2) There are 26 possible types present in Gansu province, and three grassland classes that cover the largest area in Gansu are warm temperate-arid warm temperate zonal semidesert ( B11),Ⅳ cool temperate-arid temperate zonal semidesert ( B10) andⅢ cold temperate perhumid taiga forest ( F37), with the total area of the threeⅡ classes is 17.83×106 hm2, accounting for 44.43% of the total grassland area in Gansu; 3) The geographical distribution of grassland type indicates significantly vertical zonality pattern: with the altitude decreasing, frigid series grassland, cold temperate series grassland, cool temperate series grassland, warm temperate series grassland and warm series grassland distribute successively from southwest to northeast, which fit the terrain of Gansu province. 4) Accuracy assessment shows: the overall accuracy of grassland classification is 70.11% and the kappa coefficient is 0.67. The research can solve the problem of transforming from scattered site data to regional polygon data in CSCS and the uncertain borderline in punctate data extrapolation, and provide a new approach to the utilization of CSCS, which can carry forward the practical progress of CSCS.
