植被覆盖度与温度关系的MODIS高光谱研究

针对我国局部区域的植被覆盖度与气候变化研究仍然较少的现状,该文以四川盆地为例,利用MODIS高光谱数据,以植被覆盖度作为监测植被动态变化指标,根据皮尔逊相关系数分析植被覆盖度与气候的相关性,总结2003—2010年四川盆地植被覆盖度动态变化趋势,研究温度变化对植被覆盖度的影响。研究表明:植被覆盖度与温度为极强正相关,除地震等特殊情况,两者变化趋势相似,夏高冬低。     

基于遥感技术的汪清县森林资源动态监测研究与实践

以吉林省汪清县为研究区,根据地理国情普查成果应用及监测工作通知要求,探讨了遥感技术在汪清县森林资源监测应用上的主要内容和方法。利用吉林省地理国情普查、基础测绘成果数据,结合Landsat、SPOT、ALOS等卫星影像数据,进行汪清县土地覆盖信息提取及动态监测、森林类型提取及动态监测、植被覆盖度提取及动态监测、森林参数模型反演及动态监测、驱动因子选取等内容进行了详细地说明,同时,针对汪清县森林资源各项的动态变化情况进行了驱动力分析。     

植被覆盖地表土壤水分遥感反演

以地域特色突出的新疆渭干河-库车河三角洲绿洲为研究区,联合使用雷达数据和光学遥感数据,对干旱区绿洲土壤和植被水分信息进行提取。在同期光学遥感影像数据提取植被归一化差分水分指数基础上,利用"水-云模型"从雷达数据总的后向散射中去除植被影响,建立土壤后向散射系数与土壤含水量的关系,相关系数为HH极化R2=0.5227,HV极化R2=0.3277。结果表明利用C波段HH极化雷达影像数据结合光学影像数据,进行干旱半干旱地区棉花、玉米等农作物种植区地表土壤水分反演时,在中等覆盖条件下去除植被影响有较好的效果。     

基于MODIS数据的火险潜在指数（FPI）及其应用研究

死、活可燃物含水率大小决定森林点燃的难易度,是判断林火能否发生、进行林火预报的重要因子。本文应用火险潜在指数（FPI,Fire Potential Index）模型,从这2个方面分析研究可燃物湿度对林火发生的影响。利用MODIS遥感数据提取FPI模型所需因素（气象数据： 相对湿度、温度; 植被数据： 10 h时滞可燃物湿度、归一化水分指数、植被绿度）,并将获得的2004年10月黑龙江省和2008年3月南方几省的气象、植被数据输入FPI模型,得到火险指数和火险等级划分。实践证明,应用该模型能够提高火险在时间和地理分布上的预报能力及预防技术。     

胜利矿区植被覆盖度时序变化的空间异质性监测

通过对胜利矿区的地理位置、气候条件等背景的分析,本文为实现获取时序性植被覆盖度的空间异质性的目的,使用ENVI、GIS、Matlab等软件,基于胜利矿区1985—2017年的Landsat TM/ETM+/OLI遥感数据计算NDVI,利用像元二分模型计算植被覆盖度,得到研究区植被覆盖度均值的时序变化情况。采用转移矩阵法和Sen+Mann-Kendall法对研究区域内不同等级的植被覆盖转移情况及变化趋势情况进行分析。研究表明：胜利矿区植被覆盖度均值波动较大,呈轻微下降趋势。在监测时段内68.36%的高植被覆盖区域植被发生了退化,只有3.2%左右的极低植被覆盖区域得到了良好的改善。此外,研究区植被覆盖度受到结构性因子和随机性因子的影响,空间异质性明显,灌溉区由于人为干涉,植被生长良好,极低植被覆盖面积维持在3%以下,植被覆盖显著下降区域主要集中在露天采坑、排土场等矿业景观区。     

基于GEE的地表覆盖与气候状态关联研究

为探究地表覆盖与气候状态间的关联性,本文选取2019年的Landsat影像数据,结合温度、降水量、PM_2.5浓度3种气候指标,利用GEE平台,结合NDVI、MNDWI、NDBI,采用SVM、RF、CART方法进行地表覆盖分类,探究气候指标与地表覆盖类型分布的关联性;提出了使用3种气候指标构建分类特征进行地表覆盖分类的方法,并通过消融试验分析了气候指标对地表覆盖分类精度的影响。结果表明:①RF有较好的分类结果,总体精度为96.0%;②3种气候指标均能提高地表覆盖分类精度,其中PM_2.5浓度效果最好;③温度与植被、水体关联性较大,PM_2.5浓度与城区、植被关联性较大,降水量与耕地关联性较大。     

八步沙林场防沙治沙区植被覆盖度时空演变分析

以古浪县八步沙林场防沙治沙区域及其周边乡镇为研究区,基于Landsat影像,利用植被覆盖度遥感估算方法提取了1991-2019年研究区的植被覆盖度数据,并对区域内植被覆盖度变化的总体趋势、时空演变特征、影响因素等进行了深入分析.结果 表明:①1991-2019年区域内植被覆盖度明显向好发展,极低植被覆盖度区域逐年减少,...     

基于GIS的福州市生态环境遥感综合评价模型

在ENVI软件的支持下,利用TM卫星遥感数据提取福州市生态环境评价因子;利用1 10万地形数据,在GIS环境下生成高程和坡度(由等高线生成)栅格数据,并通过ERDAS转换成ENVI可读的数据格式,经过投影转换,使之与提取的环境因子进行复合,生成综合影像;通过线性回归方法确定植被、水分、热容量、土壤及地形等因子的权重,建立福州市生态环境遥感评价模型,并利用该模型对福州市的生态环境进行评价。     

山东省植被覆盖度变化与气候因子相关性分析

为分析山东省植被覆盖度变化及其与降水量、温度等气候因子变化的相关性,该文采用2005—2015年的NDVI、降水量和温度数据,利用重心模型和相关系数法,进行了植被覆盖度与降水、温度的月动态变化和季度动态变化分析。研究结果显示,在2005—2015年的10年间,山东省植被覆盖度在整体上呈现增长趋势,植被覆盖度与气候因子无论是在月动态变化还是季动态变化都表现出不同程度的正相关性,植被NDVI的季度走向与降水和温度的季动态变化趋势几乎一致,并且温度对植被生长的影响大于降水对植被生长的影响。研究验证了植被覆盖度的变化与气候因子的变化有一定的关系。     

基于MODIS数据的青岛市城市热岛效应非一致性分析

利用2011年各月的MODIS数据,反演得到青岛地区地表温度数据和植被覆盖数据,对其地表温度与植被覆盖的分布状况及相互关系进行定量研究。实验表明,青岛市存在热岛效应,且热岛效应夏秋季节在市区表现较为明显,在夏秋季节内地表温度与植被覆盖之间呈现显著的负相关关系。此外,还对比了青岛市与上海市的城市热岛效应的不同,对青岛市热岛效应非一致性作出进一步分析。     

中国陆地生态系统脆弱带遥感模型

本研究通过对我国陆地生态系统8个典型样地的植被指数取样实验和图像计算结果发现，这8个样地植被指数随着水、热因子的季节变化，在时间和空间上具有一定的“绿波推移”和“景观更替”规律。在中国东部湿润的季风区（样地1－3），随着纬度的增高，其月平均植被指数与月平均气温有较大的相关。发现降水相对丰沛的地带，热量和光照条件的变化成为植被生长和变化的自然限制因子；而在中国北方森林－森林草原－典型昌原－荒漠草原－荒漠地带上，随着从东部（湿润地区）到西部（干旱地区）干湿条件的更替，月平均植被指数与降水多寡有较大的正相关关系。在8个样地上都呈现出共同的规律，即定向风的分布与植被指数的分布在时间和空间上具有逆相分布的“套合关系”。尤其在时间上有相逆套合关系，这正是中国北方沙尘暴和沙漠化加剧的自然原因。本研究定量地给出了我国陆地不同经纬度带生态系统脆弱季节和累积时间的分布。     

机载Lidar数据的农作物覆盖度及LAI反演

虽然Lidar点云数据已被广泛应用于获取森林各项结构参数,但这些方法并不适合于低矮的灌丛、林地和农作物。本文以玉米为研究对象,提出利用机载Lidar点云数据的强度信息和全波形数据中的距离与扫描天顶角信息,反演农作物覆盖度和LAI的方法。在黑河进行的飞行实验和地面验证表明,该方法具有较高精度,也表明Lidar在低矮自然植被监测和农业应用上有较大潜力。     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

An approach to estimate the evapotranspiration using NOAA/AVHRR DATA

Evapotranspiration (ET) is continued process wherein moisture from soil and vegetated surface is transferred to the atmosphere. Changes in evapotranspiration are likely to have large impacts on terrestrial vegetation. Evapotranspiration is a seasonally varying property at a given place; changes in it reflect the status of soil moisture and terrestrial vegetation. Through water balance, ET can include major shifts in vegetative patterns and or its condition leading to climate change. Therefore, in this paper, it is attempted to estimate the evapotranspiration over various land cover using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km. For this purpose, a semi-empirical model has been proposed to estimate the ET. Regression analysis has been carried out to develop an empirical relation between individual land cover surface temperature and ET, which will be helpful to know the effect of each land cover surface temperature on ET. In which, it is observed that surface temperature over grassland is more effective on ET in comparison to other land cover in March 1999 on the Mupfure, Zimbabwe catchment area. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planner.     

Spatial and Temporal Relationships among NDVI,Climate Factors,and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.     

Effects of vegetation cover on summer climate in China during 1982–2001

In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in most zones of China; these suggest that, when the vegetation cover increases, the summer precipitation will increase, and the lagged correlations show a significant difference between zones. The stronger correlations between NDVI in previous season and summer climate occur in three zones (Mid-temperate zone, Warm-temperate zone and Plateau climate zone), and this implies that vegetation changes have more sensitive feedback effects on climate in the three zones in China.     

中国夏季气候对植被覆盖的影响:1982～2001(英文)

In this paper, we apply lagged correlation analysis to study the effects of vegetation cover on the summer climate in different zones of China, using NOAA/AVHRR normalized difference vegetation index (NDVI) data during the time period from 1982 to 2001 and climate data of 365 meteorological stations across China (precipitation from 1982 to 2001 and temperature from 1982 to 1998). The results show that there are positive correlations between spring NDVI and summer climate (temperature and precipitation) in m...     

Improving a Penman–Monteith evapotranspiration model by incorporating soil moisture control on soil evaporation in semiarid areas

Penman–Monteith (PM) theory has been successfully applied to calculate land surface evapotranspiration (ET) for regional and global scales. However, soil surface resistance, related to soil moisture, is always difficult to determine over a large region, especially in arid or semiarid areas. In this study, we developed an ET estimation algorithm by incorporating soil moisture control, a soil moisture index (SMI) derived from the surface temperature and vegetation index space. We denoted this ET algorithm as the PM-SMI. The PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity, and validated with Bowen ratio measurements at seven sites in the Southern Great Plain (SGP) that were covered by grassland and cropland with low vegetation cover, as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation cover. The results show that in comparison with the other methods examined, the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error (RMSE) of 0.91 mm/d, bias of 0.33 mm/d, and R² of 0.77. For three forest sites, the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three algorithms. At all the 10 validation sites, the PM-SMI algorithm performed the best. PM-SMI 8-day ET estimates were also compared with MODIS 8-day ET products (MOD16A2), and the latter showed negligible bias at SGP sites. In contrast, most of the PM-SMI 8-day ET estimates are around the 1:1 line.     

中国西北地区NDVI变化及其与温度和降水的关系

稀疏的植被覆盖是干旱和半干旱地区最主要的环境特征,因此长期定量的植被分布和变化观测能够分析干旱和半干旱地区的环境变化。在以干旱和半干旱地区为主要的中国西北地区存在着森林减少、土地侵蚀、盐碱化和沙漠扩张等严重的环境问题,生态环境十分脆弱。通过NOAA/AVHRR建立近20年来中国西北地区NDVI变化序列,利用差分法、斜率变化和主成分分析3种方法分析植被变化。3种方法显示出基本一致的结果,即大部分地区植被状况恶化,局部地区有所好转。通过分析植被变化与温度、降水变化的关系,发现NDVI与降水存在明显的正相关关系,而与温度变化的关系并不明显,表明降水是影响西北地区植被变化最主要的自然因素。     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)