Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

Soil respiration (R_s) is of great importance to the global carbon balance. Remote sensing of R_s is challenging because of (1) the lack of long-term R_s data for model development and (2) limited knowledge of using satellite-based products to estimate R_s. Using 8-years (2002–2009) of continuous R_s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R_s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R_s and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R_s closely tracked the seasonal patterns of measured R_s and explained 74–92% of the variance in R_s with a root mean square error (RMSE) less than 1.0 g C/m²/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R_s with an overall R² of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R_s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R_s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R_s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R_s.  相似文献   

融合时间序列环境卫星数据与物候特征的水稻种植区提取   总被引：3，自引：0，他引：3  

柳文杰  曾永年  张猛 《遥感学报》2018,22(3):381-391

获取高精度的区域水稻种植面积对于农业规划、配置与决策具有重要意义。区域尺度的水稻面积获取依赖于高时空分辨率影像,但受卫星回访周期和气候影响,难以获取足够时间序列的高时空分辨率影像,从而影响水稻种植面积遥感提取的精度。为此,提出适应于中国南方多雨云天气地区,基于国产环境卫星(HJ-1A/1B)与MODIS融合数据的水稻种植面积提取的新方法。以洞庭湖区为实验区,利用STARFM模型融合环境卫星NDVI数据与MODIS13Q1数据,获取时间序列的环境卫星NDVI数据,利用水稻关键期的NDVI数据结合物候特征参数对水稻种植区域进行提取。结果表明,该方法能有效提取区域水稻种植的面积,水稻种植面积提取的总体精度与Kappa系数分别达到91.71%与0.9024,分类结果明显优于仅采用多光谱影像或NDVI数据。该研究为中国南方多雨云天气地区水稻种植面积提取提供了有效的方法。  相似文献   

内蒙古西辽河平原植被指数时空变化及其影响因素研究          下载免费PDF全文

高萌萌  刘琼  王轶  李小磊  石鹏 《水文地质工程地质》2022,49(1):175-182

西辽河平原位于我国北方农牧交错带,属半干旱气候,发育科尔沁沙地,生态环境极其脆弱,开展植被指数时空变化及其影响因素研究,对于预测土地退化风险意义重大,可为该流域生态环境保护治理及水资源合理开发利用提供技术支撑。利用2000—2019年MODIS NDVI数据,采用一元线性回归趋势法和Mann-Kendall检验分析了近20年来该地区的植被生长变化趋势及突变情况。从影响植被生长的水热条件出发,分析了NDVI值与气象因素（降水、气温）、土壤湿度、地下水埋深等因子的相关关系;结合人类活动,分析了土地利用类型变化对NDVI值的影响。结果表明:（1）2000—2019年生长季NDVI值整体呈上升趋势,不存在显著突变点,最高值0.56,最低值0.41。（2）NDVI值在空间上呈现“东高西低”的分布特征,不同用地类型的NDVI值由大到小依次为耕地>林地>沼泽地>滩地>草地>盐碱地>沙地。（3）92.5%的区域植被呈增长趋势,7.5%的区域植被呈减少趋势。（4）NDVI值与降水、气温、土壤湿度呈正相关关系,相关系数分别为0.86,0.78,0.81,降水对植被影响最大。（5）最适宜天然植被生长的地下水埋深约为3 m,当地下水埋深大于10 m时,NDVI值会随着埋深的增加剧烈减小。（6）人类活动如土地开垦、植树造林是近20年来NDVI值呈增加趋势的主要原因之一,在一定程度上改善了当地生态环境。  相似文献   

MODIS植被指数的美国玉米单产遥感估测     

任建强  陈仲新  周清波  刘佳  唐华俊 《遥感学报》2015,19(4):568-577

针对中国开展的国外农作物产量遥感估测大多依靠中低分辨率耕地信息、省级(州级)或国家级作物产量统计数据的现状,本文以美国玉米为例,探讨利用多年中高分辨率作物分布信息、时序遥感植被指数和县级作物产量统计数据开展国外重点地区作物单产遥感估测技术研究,以期进一步提高中国对国外农作物产量监测精度和精细化水平。首先,利用美国农业部国家农业统计局(NASS/USDA)生产的作物分布数据(CDL)获得多个年份玉米空间分布图,并对相应年份250 m分辨率16天合成的MODIS-NDVI时序数据进行掩膜处理,统计获得每年各县域内玉米主要生育期NDVI均值;其次,以各州为估产区,以多年县级玉米统计单产和县域内玉米主要生育期NDVI均值为基础,建立各州玉米主要生育期NDVI与玉米单产间关系模型;然后,通过主要生育期玉米单产和玉米植被指数间拟合程度,筛选确定各州玉米最佳估产期和最佳估产模型。最终,利用最佳估产模型实现美国各州玉米单产估测和全国玉米单产推算。其中,建模数据覆盖时间为2007年—2010年,验证数据为2011年。结果表明,应用最佳估产模型的2011年美国各州玉米单产估测相对误差在-4.16%—4.92%,均方根误差在148.75—820.93 kg/ha,各州估测结果计算获得全国玉米单产的相对误差仅为2.12%,均方根误差为285.57 kg/ha。可见,本研究的作物单产遥感估测技术方法具有一定可行性,可准确估测全球重点地区作物单产信息。  相似文献   

Semi-automatic delineation of badlands using contrast in vegetation activity: a case study in the lower Chambal valley,India     

V. Ranga  A. Van Rompaey  J. Poesen  S.N. Mohapatra  P. Pani 《国际地球制图》2015,30(8):919-936

Population growth worldwide leads to an increasing pressure on the land. Recent studies reported that many areas covered by badlands are decreasing because parts of badlands are being levelled and converted into arable land. It is important to monitor these changes for environmental planning. This paper proposes a remote-sensing-based detection method which allows mapping of badland dynamics based on seasonal vegetation changes in the lower Chambal valley, India. Supervised classification was applied on three Landsat (Thematic Mapper) images, from 3 different seasons; January (winter), April (summer) and October (post-monsoon). Different band selection methods were applied to get the best classification. Validation was done by ground referencing and a GeoEye-1 satellite image. The image from January performed best with overall accuracy of 87% and 0.69 of kappa. This method opens the possibilities of using semi-automatic classification for the Chambal badlands which is so far mapped with manual interpretations only.  相似文献   

Detection of foreclosure-related landscape management changes using Landsat     

《Applied geography (Sevenoaks, England)》2015

The volume of properties affected by foreclosure over the past decade suggests the potential for dramatic change in vegetation cover due to changes in management. Yet, the specific pathology of each foreclosure, the temporal asynchrony among foreclosures, and differences in the area available for vegetation growth across properties presents challenges to observing and measuring change. This paper develops and tests a difference in deviations approach that compares the parcel NDVI to a neighborhood norm before and after foreclosure. The difference in deviations approach addresses the challenges of separating parcel-level change corresponding to foreclosure and identifies changes on both small and large parcels. The method relies on a time series of Landsat Normalized Difference Vegetation Index (NDVI) data, individual home foreclosure records and property tax assessment data for Maricopa County, Arizona from 2002 to 2012. To establish the level of difference associated with observable landscape change, we use a probit regression model, coding Google Earth images for properties across the range of observed deviations of difference. The basic assumption underlying the approach is that if foreclosure coincides with a change in management, it will lead to changes in vegetation structure and thus, NDVI values. We estimate that 13% of home foreclosures in Maricopa County over the period from 2002 to 2012 resulted in declines in vegetation whereas 6.5% resulted in vegetation increases. Future uses of this method for understanding landscape management in residential landscapes are discussed.  相似文献   

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure     

《Applied geography (Sevenoaks, England)》2015

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.  相似文献   

基于LSWI和NDVI时间序列的水田信息提取研究   总被引：5，自引：0，他引：5  

刘元亮  李艳  吴剑亮 《地理与地理信息科学》2015,31(3):32-37,2

利用多时相的Landsat TM/ETM+和DEM数据,分别采用直方图匹配(HM)和准不变特征点(PIFs)方法对影像序列进行相对辐射校正,减少了影像的光照和大气条件在时间上的不确定性,提高了归一化植被指数(NDVI)和地表水分指数(LSWI)的计算精度。根据水稻在不同生育期表现出的生理特征,基于LSWI和NDVI时间序列及高程特征,采用二叉树方法提取了浙江省金华市水田信息。经过验证,在空间上水田信息的提取精度达到92.3%,在县域尺度上提取面积与统计年鉴具有0.97的相关度。  相似文献   

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle     

《International Journal of Applied Earth Observation and Geoinformation》2015

Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.  相似文献   

三套NDVI长时间序列植被指数的对比——以玛曲为例     

沙莎  郭铌  李耀辉  韩涛 《干旱气象》2013,(4):657-665

NDVI／MODIS、NDVI／GIMMS和NDVI／NSMC是时间长度不同、空间分辨率相差甚远的3套ND—VI数据集,如何集成应用这些不同时间长度、不同分辨率的数据进行相关研究,数据集间的比较是最基础的工作。本文以甘肃省甘南州玛曲县为例,用直方图、相关分析、趋势分析等方法研究了这3套NDVI产品数据集的相互关系。结果表明：1）NDVI／NSMC与NDVI/MODIS的直方图具有类似的图像分布特征,但是NDVI／MODIS数据分布范围更大;2）3套NDVI在数值上表现为NDVI／MODIS〉NDVI／GIMMS〉NDVI／NSMC;3）3套数据集空间图像特征一致,两两间均具有十分显著的空间相关性,其中1月份相对最弱,5、10月份最强,三者相比NDVIfNSMC与NDVI／MODIS的空间相关性更强;4）1—3月、5—8月及年均的NDVI／GIMMS与NDVI／NSMC值存在显著的时间相关性,但两者逐年变化趋势存在较大差别,两者气候倾向率相差最大的高达5倍之多。NDVIfNSMC数据集在处理过程中可能未进行大气订正及交叉定标,这是造成共同源的NDVIfGIMMS与NDVI／NSMC差异较大的重要原因。  相似文献