基于ERDAS平台的NDVI植被覆盖变化检测

介绍了利用ERDAS IMAGINE软件对多时相遥感影像进行地表植被覆盖变化检测的方法及过程,采用归一化植被指数差值法,得到包含多时相遥感影像变化信息的差异图像,依据差异图像的灰度直方图分布情况,结合经验公式设置变化阈值来分离变化信息和非变化信息。     

Estimating evapotranspiration fraction by modeling two-dimensional space of NDVI/albedo and day-night land surface temperature difference: A comparative study

Three simple models estimating the evapotranspiration fraction (EF) were tested, validated and compared with available data from NDVI/albedo and day-night land surface temperature difference (DT). They were analyzed using ground based measurements collected by the energy balance Bowen ratio system at the 11 enhanced facilities located at the Southern Great Planes of the USA between April 2001 and May 2005. One model was superior in predicting the EF. It used an extension of the Priestly-Taylor equation and a relation between the normalized difference vegetation index (NDVI) and DT.     

Evolution of vegetation activity on vegetated,eroded, and erosion risk areas in the central Spanish Pyrenees,using multitemporal Landsat imagery

The temporal evolution of vegetation activity on various land cover classes in the Spanish Pyrenees was analyzed. Two time series of the normalized difference vegetation index (NDVI) were used, corresponding to March (early spring) and August (the end of summer). The series were generated from Landsat TM and Landsat ETM+ images for the period 1984–2007. An increase in the NDVI in March was found for vegetated areas, and the opposite trend was found in both March and August for degraded areas (badlands and erosion risk areas). The rise in minimum temperature and the time variation of the cloud cover during the study period appears to be the most important factors explaining increased NDVI in the vegetated areas. In degraded areas, no climatic or topographic variable was associated with the negative NDVI trend, which may be related to erosion processes taking place in these regions. Copyright © 2010 John Wiley & Sons, Ltd.     

京津风沙源区生态环境时空变化的遥感监测分析

本文利用由遥感影像解译获得的土地利用数据和MODIS NDVI产品,采用空间转移矩阵、最大值合成法、均值法和一元线性回归模拟等方法,分析了2000年以来京津风沙源区生态环境变化的时空特征.结果表明:(1)2000-2005年京津风沙源区耕地开垦、林草地减少的趋势有所减缓,但区域气候变化趋于干旱,水域面积明显减少,草地退...     

1982—1999年珠江流域归一化植被指数与降水年际变化分析

利用1982—1999年月平均归一化植被指数(normalized difference vegetation index,NDVI)和降水资料,采用经验正交函数(EOF)方法分别研究了珠江流域的NDVI和降水在年和年际尺度上的异常关系,并分析了NDVI与降水及其他一些气象因子的相关关系。研究发现流域NDVI和降水在年内异常上具有较好的空间一致性,在时间上具有1—2个月的滞后;年际尺度上两者异常空间差异明显,流域东部(下游)异常为负相关,西部(上游)异常为正相关。NDVI和各气候因子的相关关系存在明显的空间和季节差异:流域东部NDVI和降水负相关明显,和温度及太阳短波辐射正相关明显;流域西部NDVI和降水滞后正相关明显,和温度相关不明显;NDVI在夏季和降水呈显著负相关,在春、秋季节滞后于降水,呈明显正相关,且滞后3个月正相关最为明显。     

辽宁省植被覆盖度时空演变及其对气候因子的响应

基于MODIS-NDVI数据及像元二分模型,对辽宁省植被覆盖度进行估算,并在此基础上探讨了辽宁植被覆盖度时空演变特征及其对气候因子的响应,结果表明：2000-2018年辽宁省植被覆盖度整体呈波动增加趋势,年平均增长速率为0.38%,呈增加趋势的面积占总面积的92.3%;2000年以来,裸地及低覆盖区域占比逐年减小,中低覆盖、中覆盖及高覆盖区域占比分别增加11.35%、11.00%和9.22%;辽宁省植被覆盖度与气候因子的关系表现出明显的空间差异性,其中,西部易旱地区覆盖度与气温呈显著负相关,与降水呈正相关,东部地区覆盖度与气温呈正相关,与降水呈负相关;辽宁植被覆盖度对降水的响应存在一个月的滞后期,对气温的响应无滞后效应。     

龙口市污水灌区与清水灌区冬小麦植被指数变化差异分析

为了研究污水灌溉对农田土壤环境和作物生长的影响,利用2008—2009年10期中国环境与灾害监测预报小卫星(HJ-1A)CCD1影像数据,结合龙口市冬小麦物候期的变化特征提取出单一冬小麦种植区,反演了冬小麦生长期内的归一化植被指数(NDVI),并对污水灌区和清水灌区的冬小麦植被指数进行比对分析,最后结合相应的重归一化植被指数(RDVI)和叶面积指数(LAI)进行验证。结果显示,研究区内污水灌溉已经对灌区的作物生长产生了一定的影响,污水灌溉区作物的生长状况明显不及清水灌溉区作物长势。说明污水灌溉会导致冬小麦的长势变差,对污水排放的控制和污水无污化处理势在必行。     

美国南达科他州Black Hills国家森林公园2000年森林火灾后植被恢复过程研究(英文)

森林火灾在景观上往往造成不同程度的森林冠层损失,而冠层影响光合作用和蒸散,因此刻画灾后森林冠层恢复的轨迹对于了解生态系统过程具有重要意义。森林冠层的损失和恢复通常采用叶面积指数(LAI)或其它能够反映冠层光合能力的植被指数进行表征。本研究中,我们采用Terra卫星搭载的中分辨率成像光谱仪(MODIS)的长时间序列影像(2000-2009年)来重建火灾后森林冠层恢复的过程。以美国南达科他州布莱克山国家森林公园(The Black Hills National Forest, South Dakota)为例,该地区在2000年8月24日经历了一次大的自然火灾,烧毁了近33 785 ha森林,其中大部分是美国黄松林。基于LAI的研究表明,植被冠层光合能力在3年内(2001-2003年)基本恢复,这主要来自于林下未烧毁草地在灾后的快速生长;火烧迹地的NDVI和EVI在这3年内也呈现恢复的态势。可见,LAI、NDVI和EVI在火灾几年之后便难以有效地识别火烧迹地。然而,陆地表面水分指数(基于近红外和短波红外波段的遥感标准化指数,简称LSWI),能够有效地识别和追踪火烧迹地至今的整个过程(2000-2009年)。这一研究结果也使得采用其它具有近红外和短波红外波段的传感器研究森林火灾迹地恢复和干扰过程成为可能,其中包括Landsat 5 TM影像(可追溯至1984年)。更长时间序列的数据对于研究森林火灾灾后生态系统干扰和恢复过程、森林演替模拟以及碳循环具有重要的支撑作用,LSWI指标证明能够有效地刻画这一过程。     

Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models.     

Effect of cover management factor in quantification of soil loss: case study of Sungai Akah subwatershed,Baram River basin Sarawak,Malaysia

The present study evaluates the effectiveness and suitability of cover management factors (C factor) generated through different techniques like land use/land cover-based arbitrary value (C_LULC), Normalised Different Vegetation Index-based methods C_NDVI1 and C_NDVI2 and Modified Soil Adjusted Vegetation Index 2-based method (C_MSAVI2). The C factors generated using these four methods were tested in the calculation and assessment of annual average soil loss from an upland forested subwatershed in the Baram river basin using the Revised Universal Soil Loss Equation (RUSLE). The four cover management factor maps generated by this analysis show some variation among the results. The LULC method uses a single arbitrary value for each LULC type mapped in the subwatershed. The other three methods show a range of C values within each mapped LULC type. The effects of these variations were tested in the RUSLE by keeping the factors such as rainfall erosivity (R), soil erodibility (K), slope-length and steepness (LS) constant. The maximum annual average soil loss is 1191 t. ha^?1. y^?1 based on the C_LULC. Soil losses estimated with other three methods are very different compared to those estimated with the C_LULC method. The highest calculated soil loss values were 1832, 1674 and 1608 t. ha^?1. y^?1 in the study area based, respectively, on C_NDVI1, C_NDVI2 and C_MSAVI2 C factors. These maximum values represent the worst pixel scenario values of soil loss in the region. The statistical analysis performed indicates different relationship between the parameters and suggests the acceptance of the methodology based on C_NDVI2 for the study area, instead of a single value method such as C_LULC. Among the other two methods, the C_MSAVI2 was found to be more consistent than the C_NDVI1 method, but both methods lead to over-prediction of annual soil loss rate and therefore need to be reconsidered before applied in the RUSLE.