基于时序NDVI的昭觉植被覆盖度变化研究   总被引：1，自引：0，他引：1  

陈江玲  徐京华  李国明  王渊博  武文娟 《测绘与空间地理信息》2016,(11)

归一化植被指数(NDVI)能精确地反映植被绿度、光合作用强度,在一定程度上反映着植被的演化信息,是评价生态环境状况的重要指标之一。本文利用Landsat TM数据分别对昭觉地区2009年和2014年的NDVI进行计算,并分别利用均值法、像元二分模型及NDVI差值植被指数对研究区域植被覆盖变化进行定量分析,结果表明:昭觉县NDVI均值上升了11.6%,植被覆盖度中极度改善的面积比例约占38%,昭觉县整体NDVI植被覆盖度显著提高,并对其变化原因进行简要分析,为生态环境建设提供决策依据。  相似文献   

中国北方地区秋季植被变化及对气候变化的响应研究     

安佑志  高炜  高志强  施润和 《测绘与空间地理信息》2016,(11)

基于1982~2006年的AVHRR GIMMS NDVI数据,使用一元线性回归和分段线性回归等方法,通过对中国北方地区植被变化及其与气候因子的关系研究,揭示该地区近25年来在不同时段的植被变化趋势及对气候变化的响应规律,从而为该地区的生态环境变化研究提供理论依据。研究结果表明:1)中国北方地区秋季植被在25年时间内整体呈上升趋势。秋季NDVI在秋季温度断点之前以上升趋势为主,秋季NDVI在秋季温度断点之后仍以上升趋势为主,但上升趋势有所放缓。2)通过分段线性回归方法和相关分析研究得出中国北方地区秋季温度是秋季NDVI变化的主要驱动力。在秋季温度断点之后,秋季温度仍呈上升趋势而降水呈显著减少的面积增多,从而在温度和降水双重影响下的干旱胁迫导致植被下降;当秋季温度下降而秋季降水增多时干旱发生概率变小,从而使秋季NDVI呈上升趋势。  相似文献   

桂林毛村不同植被类型土壤微生物数量与碳酸酐酶活性比较     

黄芬  黄艳梅  杨利超  白冰  曹建华 《地质论评》2016,62(1):94-102

以非岩溶区林地为对比,分析了桂林毛村岩溶区4种不同植被类型土壤微生物数量及碳酸酐酶(CA)活性的季度动态变化规律,发现以下主要结果:1随着植被的正向演替,岩溶区弃耕地、草地、灌丛及林地微生物数量及CA活性逐渐升高,微生物总数从64.07×10~4cfu/g上升到178.23×10~4cfu/g,CA活性从0.77 U/g上升到1.82 U/g,岩溶区林地大于非岩溶区林地。2在岩溶区不同植被类型,微生物组成均表现为细菌最多(平均值95.14%),放线菌次之(平均值2.79%),真菌数量最少(平均值1.75%)。而在非岩溶区表现为细菌最多(平均值90.95%),真菌次之(平均值5.32%),放线菌最少(平均值3.73%)。3微生物数量季节动态整体表现为春季至夏季逐渐上升,至秋季达到最高,冬季下降,微生物总数的增长依赖于细菌的倍数增长,真菌和放线菌影响较小。CA活性整体表现为夏季和冬季低于春季和秋季,秋季达到最大值。4 CA活性与细菌及微生物总数呈极显著的正相关,表明土壤CA主要来源于细菌的分泌。  相似文献   

执行器退化的遥机器人系统量化反馈容错控制     

郑柏超  傅曦雨  杨鑫  阚亚进 《南京气象学院学报》2016,8(6):499-504

针对遥机器人系统中存在执行器退化、控制输入量化,且编、解码端的量化灵敏度参数不匹配的问题,设计了遥机器人系统的鲁棒量化反馈容错控制系统.控制器结构的线性部分由线性矩阵不等式给出,旨在解决系统指标性能问题,非线性部分则用于处理量化参数不匹配问题.经稳定性理论证明,提出的鲁棒量化反馈容错控制方法能消除执行器退化及量化参数不匹配等的影响,并确保从机器人能渐近跟踪主机器人.最后,算例仿真验证了方法的有效性.  相似文献   

太湖地区HJ-1卫星CCD数据反演气溶胶及大气校正     

郑尧  王琪洁  梁忠壮 《测绘工程》2016,25(5):10-15

针对HJ-1卫星CCD数据,利用改进的暗像元法反演气溶胶光学厚度(AOD),再利用反演的AOD对其进行大气校正。将反演的气溶胶与地基太阳光度计数据进行对比验证,发现当反演的AOD值大于0.2时,反演值与地基观测值的相关系数为0.964,符合MODIS业务化反演AOD的精度要求。再将反演得到的气溶胶带入6S辐射传输模型中,对HJ-1卫星CCD数据进行大气校正实验。结果表明,该方法能有效提高HJ-1卫星CCD数据大气校正的精度,更好地复原地物的真实光谱信息。  相似文献   

植被生物量高光谱遥感监测研究进展   总被引：2，自引：0，他引：2  

姚阔  郭旭东  南颖  李坤  江淑芳  孙婷婷 《测绘科学》2016,(8):48-53

植被生物量的评估对于研究全球碳循环具有重大意义,而高光谱遥感技术为精确反演地表属性提供了重要的数据支持。针对如何更好地应用高光谱遥感技术进行植被生物量精确反演的问题,该文详细阐述了国内外应用高光谱技术估测植被生物量的研究进展。对反演植被生物量所涉及的数据源、反演模型的构建方法及其模型特点、反演模型应用对象等内容进行了综合评述,并通过分析认为,高光谱遥感技术较传统的多光谱遥感技术在生物量反演精度上有了显著的提高。同时,对建模方法、多源遥感数据融合以及模型通用性等方面的研究进行了展望,以达到在大尺度范围内对植被生物量进行准确反演的目的。  相似文献   

利用端元光谱内插的青海植被覆盖度估算研究     

杜英坤  燕琴  童李霞  王晓波 《测绘科学》2016,41(9):87-90,169

针对利用像元二分模型估算植被覆盖度的精度不高的问题,该文基于OSAVI,提出了选定模型参数(OSAVIs和OSAVIv)的方法,并将该方法应用于青海省植被覆盖度估算。该方法通过高分辨率影像在研究区内选取纯裸地和纯植被样点,并将纯裸地样点的OSAVI作为纯裸地样点像元的OSAVIs,将纯植被样点的OSAVI作为纯植被样点像元的OSAVIv,利用样点像元的OSAVIs和OSAVIv值,通过普通克里金内插法,求得研究区每个像元对应的OSAVIs和OSAVIv。经精度验证结果表明:此方法较常规的参数选取方法,RMSE由0.170降至0.156,MAE由0.137降至0.124。经进一步分析表明,此方法对边缘验证点和非边缘验证点的估算精度都有所提高,由于配准误差和周围地表漫反射的影响,边缘验证点的估算精度低于对非边缘验证点的估算精度。  相似文献   

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico     

Yan Gao  Adrian Ghilardi  Jaime Paneque-Galvez  Margaret Skutsch  Jean François Mas 《国际地球制图》2016,31(9):1019-1031

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.  相似文献   

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data     

Tasneem Ahmed  Shweta Gupta  Balasubramanian Raman 《国际地球制图》2016,31(7):715-738

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.  相似文献   

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM     

Nitika Dangwal  N.R. Patel  Mamta Kumari  S.K. Saha 《国际地球制图》2016,31(6):682-693

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.  相似文献