基于Landsat-8数据的镇赉县干旱监测研究

选取吉林省白城市镇赉县为研究区,通过遥感影像获取归一化植被指数、增强型植被指数、归一化水分指数和土壤调节植被指数,分别构建相应的植被指数-地表温度特征空间并计算其温度植被干旱指数,用野外采样点的受灾程度和面积数据进行验证。结果表明,不同土壤类型的地表温度存在差异,在植被旺盛期,基于归一化植被指数的温度植被干旱指数具有较高的旱情监测精度,可有效地用于大范围的干旱监测。     

基于地表温度与植被指数特征空间反演地表参数的研究进展

遥感反演的地表温度(Ts)和植被指数(VI)构成的特征空间结合模型分析可以对显热通量、潜热通量及土壤含水量等地表参数进行估算.这种方法比较实用,且不过多地依赖地面观测数据.随着研究的深入,许多学者在Ts/VI特征空间基础上提出了更加丰富的空间变量.基于此,以不同空间变量为标准,分类介绍在Ts/VI特征空间的基础上对地表能量通量及土壤水分等参数的反演.其中包括在Ts/NDVI特征空间基础上提出温度植被干旱指数和条件植被温度指数来监测干旱;利用Ts/albedo特征空间反演蒸发比;用DSTV/VI特征空间反演蒸散量;用地气温差/植被指数特征空间反演蒸散量等.并介绍了Ts/VI特征空间与微波遥感结合反演地表含水量等相关研究的进展情况,最后提出未来研究的发展方向.     

旱情遥感监测方法及其进展

我国水资源短缺，旱灾一直是我国造成损失最大的自然灾害之一，遥感技术具有宏观，快速，客观，经济等常规手段不具备的优势，在旱情监测方法具有广阔的应用前景，综述了遥感监测干旱的热惯量法，作物缺水指数法，归一化植被指数距平法，供水植被指数法和微波遥感法，介绍了在此领域中近年来的进展。     

基于MODIS数据的山东省土壤墒情遥感动态监测与分析

干旱灾害是我国主要的自然灾害之一。近年来,连续性、极端干旱灾害时有发生,对我国粮食安全、饮水安全和生态安全造成严重威胁。土壤墒情是旱情监测的重要指标,遥感技术具有观测范围广、实时性强以及成本低廉等优势,可以广泛应用于土壤墒情监测。本文分析了土壤墒情与地表参数NDVI(归一化植被指数)和LJST(地表温度)的关系,建立了基于NDVI和LST、并考虑土壤类型的土壤墒情遥感监测模型。利用该模型,基于MODIS遥感影像和地面实测墒情,对2010年10月到2011年5月山东省旱情进行了动态监测。监测结果显示:山东省的旱情经历了不断加重,再到逐渐缓解。然后又局部加重。最终全部缓解的过程,干旱核心区为鲁南地区,与实际情况一致。     

黄河三角洲地表特征参数的遥感研究

地表特征参数与下垫面特征密切相关,它是研究地表物质平衡和能量平衡的基础,因而应用遥感方法反演区域地表特征参数日益受到重视。本文主要应用现有的地表特征参数遥感反演模型,利用AVHRR和TM数据反演了黄河三角洲的地表特征参数:地表反照率、地面温度和植被指数等,并根据反演结果研究了地表特征参数及其合理组合所反映的地表特征。农田植被和天然植被的植被指数变化规律不同;在植被全覆盖区域,植被指数与反照率成幂函数关系,在极干或极湿情况下,地表温度与反照率成线性关系;地表特征参数的合理组合反映出黄河三角洲下垫面覆盖度低,裸地较多,地表较湿润,蒸发量较大。     

卫星遥感信息系统在林火及干旱监测中的应用

根据几种投影方式的特点，再结合贵州地理位置，采用麦卡托投影方式来进行火灾检测，火灾自动监测系统能迅速找出火点位置并计算火面积，监测范围广，时效较常规地面监测手段提前1－3天。旱情的遥感监测基于土壤水分和植被状况，对于裸地卫星遥感重点是土壤的含水量，对于有植被覆盖的区域，遥感的重点是植被指数的变化以及植被冠层蒸腾状况的变化。从贵州的实际情况和地表条件，分别选用植被指数法、第四通道遥感下垫面温度法、植被供水指数法及热惯量方法来进行干旱监测。通过实例认为，气象卫星确实能大范围地对干旱进行宏观监测，其监测面积精度高，不仅可以迅速准确确定受灾面积，而且还可精确到县，在实际运用中可操作性很强，能为政府部门抓好防旱抗旱提供详细的数据参考，做好灾情预报和指导抗灾救灾工作服务。     

卫星遥感地表温度的真实性检验研究进展

地表温度是多种地表过程模型的输入参数,遥感反演地表温度是估算区域及全球尺度上地表辐射平衡和能量收支的关键手段。对遥感地表温度开展真实性检验有利于客观评价其精度和稳定性,对遥感地表温度的反演及应用都具有重要意义。简单回顾了通过遥感手段反演地表温度的基本原理和常用方法。回顾并分析了基于实测地表温度的检验方法、基于辐亮度的检验方法、交叉比较以及时间序列分析4种典型地表温度真实性检验方法的优缺点。在此基础上,重点总结了地表温度直接检验方法中地面观测数据获取方法、检验对象,分析了直接检验中的不确定来源。最后,对地表温度真实性检验中存在的问题进行了讨论。     

青城山地区植被光谱与叶面积指数关系初探

根据青城山地区所采集的植物高光谱数据,提取了三种植被指数:①归一化植被指数(NDVI);②比值植被指数(RVI);③差值植被指数(DVI)。并结合该研究区实测的相应叶面积指数(LAI),分别建立各植被指数与叶面积指数之间的转换模型,得出了预测模型。结果表明:采用比值植被指数RVI进行试验区的LAI反演效果更好。本次研究结合植被光谱与植被指数,反映了植被指数与对应叶面积指数之间的关系,为二者之间的相互转换提供必要参考,为森林遥感监测提供了一定的依据。     

基于FY-3 A/VIRR数据的藏西北干旱遥感监测研究

藏西北地区生态环境脆弱,由于地形复杂、气候独特,该区的观测资料非常缺乏。利用遥感技术开展藏西北地区的干旱监测,能获取在空间上连续变化的地表干旱情况,对于指导该区农牧业生产具有重要的意义。基于FY-3A/VIRR的一级数据和标准旬产品（地表温度、植被指数）,采用温度植被干旱指数（TVDI）进行藏西北地区的干旱监测研究,并将监测结果分别与基于EOS/MODIS数据监测的结果、同期的野外实测土壤水分数据以及气象站点的降水量数据进行了对比分析。结果表明：利用FY-3A/VIRR数据的TVDI遥感监测结果与实测土壤水分、气象站累计降水量数据均呈显著的负相关关系,通过了0.01水平的显著性检验;利用FY-3A/VIRR数据与EOS/MODIS数据估算的TVDI干旱等级空间分布特征基本一致,FY-3A/VIRR数据可以代替EOS/MODIS数据在藏西北地区开展干旱遥感监测,可为指导藏西北地区农牧业生产提供数据支持。     

阜平县地表温度遥感反演及其空间特征研究

以阜平县为研究区域,基于Landsat 8遥感影像和ASTGTM2 DEM数据,利用辐射传输方程法反演地表温度,运用统计学方法分析地表温度与归一化植被指数(NDVI)、海拔和坡向之间的关系.结果表明:阜平县地表温度整体呈西高东低分布,地表温度与植被覆盖、地形要素之间的关系密切,地表温度高值区域主要分布在研究区西部和东北部海拔较高且NDVI值较高区域,低值区域主要分布在东南部海拔较低且NDVI值较低区域.地表温度随海拔和NDVI的升高逐渐减小,呈现明显的负线性相关;不同坡向的地表温度分布具有一定的差异,阳坡的地表温度高于阴坡.     

Designing of the perpendicular drought index

In this paper, a simple, effective drought monitoring method is developed using two dimensional spectral space obtained from reflectance of near-infrared (NIR) and Red wavelengths. First, NIR–Red reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape and in which different surface targets possess certain spatial distribution rules. Second, perpendicular drought index (PDI) is developed on the basis of spatial characteristics of moisture distribution in NIR–Red space, as well as the relationships between PDI and soil moisture is examined. Validation work includes: comparison of PDI with in-situ drought index obtained from field measured data in the study area which includes bulk soil moisture content at different soil depths, field moisture capacity and wilting coefficient, etc.; and comparison of PDI with other recognized drought monitoring methods such as LST/NDVI and vegetation temperature condition index (VTCI). It is evident from the results that graph of PDI of field measured plots demonstrates very similar trends with ground truth drought data, LST/NDVI and VTCI. PDI is highly correlated with in-situ drought values calculated from 0 to 20 cm mean soil moisture with correlation coefficients of R ² = 0.49 (r = 0.75). This paper concludes that PDI has a potential in remote estimation of drought phenomenon as a simple, effective drought monitoring index.     

利用MODIS数据产品进行全国干旱监测的研究

利用MODIS植被指数和陆地表面温度产品建立全国3个农业气候区NDVI-Ts、NDVI-ΔT和NDVI-ATI空间,并由NDVI-Ts、NDVI-ΔT和NDVI-ATI空间分别建立温度植被干旱指数(TVDI)、温差植被干旱指数(DTVDI)和表观热惯量植被干旱指数(AVDI)3个干旱评价指标研究全国干旱分布,利用实测土壤含水量对3个干旱指标进行检验评价.NDVI-ΔT空间中的湿边基本与横坐标平行,表明当土壤水分处于饱和状态或植被完全无水分胁迫条件下,植被和土壤对缓冲环境温度变化的能力大体相当;由NDVI-ATI空间看出,随着植被覆盖增加,表观热惯量有增加的趋势.对比3个干旱评价指标表明:当监测范围较大,区域内地形复杂时,由NDVI-Ts空间计算的TVDI评价干旱最合理,由NDVI-ΔT空间计算的DTVDI在干旱监测中也具有一定的价值,而由NDVI-ATI空间计算的AVDI已经不能合理评价干旱.     

Analysing transition of land surface temperature and derived indices with respect to elevation values in Kohima Saddar

Human activities have affected the urban environment resulting in a drastic change in the surface temperature. The impact of urban heat islands is noticeable in urban areas than in rural areas. The thermal band of Landsat 8 data is used to retrieve the spatial distribution of land surface temperature (LST) over Kohima Sadar for the years 2009, 2015 and 2020 with the Mono-window algorithm. Urban Thermal Field Variance Index (UTFVI) is used to assess the ecological condition in the area impacted by LST. Cartosat-1 Digital Elevation Model (Carto DEM) is used to understand the variations of LST and indices values with reference to the elevation profile located at different random points. The variations in the land cover are categorized as per the values of normalized difference vegetation index (NDVI) and built-up density index (BUI). This work estimates the influence of elevation over LST, vegetation, and the built-up area. Results implies a negative correlation between LST and NDVI whereas a positive correlation between LST and BUI. Likewise, NDVI and BUI show a strong negative correlation. It is observed that LST is independent of elevation profile but the variation of LST depends on the impact of change in topography urbanization, deforestation, and afforestation. There is no significant relationship of elevation with the variations in NDVI and BUI values. It is observed that the impact of emissivity influences the estimation of LST values. For the locations having the highest and lowest LST, NDVI, and BUI values, 50 random points are generated for the entire region, and validation is executed with the google earth historical image.

     

2000-2014年蒙古高原植被覆盖时空变化特征及其对地表水热因子的响应

利用MODIS NDVI数据、同期地表水热组合数据和植被类型数据,对2000-2014年蒙古高原生长季和三季（春、夏、秋季）植被覆盖时空演变特征及其对地表水热因子响应模式进行分析。研究表明：这15 a来,蒙古高原生长季及三季归一化植被指数NDVI均呈增加趋势,且呈显著增加趋势区域主要集中在内蒙古地区,一定程度上反映了该地区生态恢复工程的有效性。研究区植被覆盖变化与地表水分指数LSWI有密切的关系,因此证明研究区植被覆盖的增加归因于自然和人为因素的共同作用。不同类型植被NDVI均呈增加趋势,其中荒漠植被NDVI增加最明显,森林植被增加平缓,且存在季节性差异。此外,不同类型植被NDVI受水热因子影响也存在季节性差异。     

西藏地表温度时空演变特征及影响因子

地表温度（land surface temperature, LST）是反映生态环境状况的重要指标。西藏作为气候变化的敏感地区,掌握其LST的时空变化有利于深入了解西藏热环境演化过程,为长期监测高原基础生态变化提供帮助。研究基于谷歌地球引擎获取西藏2000—2020年的MODIS LST数据,采用归一化分级方法对LST进行5个等级的划分,利用趋势分析、热力空间分析以及重心迁移等方法分析了研究区近20年来的LST时空演变特征。同时,选取归一化植被指数（normalized difference vegetation index, NDVI）、裸土指数（bare soil index, BI）、垂直不透水面指数（perpendicular impervious surface index, PISI）、湿度（WET）以及高程（digital elevation model, DEM）等5个影响LST的地表参数,结合多尺度地理加权回归,探讨了LST影响因子的作用尺度与作用效力。结果表明：2000—2020年,西藏LST均值由18.72 ℃上升至20.28 ℃,年均增长0.09 ℃,LST呈现微弱上升态势。20年来,LST在所有年份皆具有西北高、东南低的空间分布格局,LST增温趋势亦表现为西北高、东南低的分布特征。低温区和高温区空间分布聚集,形状简单、规则;次低温区、中温区以及次高温区空间分布破碎,形状复杂。2000—2020年各温区重心分布具有明显的方向性,且各温区重心迁移轨迹具有显著差异。特别是,低温区重心与高温区重心迁移轨迹呈现出由相向而行到背向而行的转变,反映出研究区东西部区域LST差距经历了由缩小到扩大的过程。DEM和WET对LST具有负向影响,BI、PISI和NDVI具有正向影响,常数项在不同生态区具有不同的影响性质。DEM具有较小的作用尺度以及最强的作用效力,常数项具有最小的作用尺度以及仅次于DEM的作用效力。     

Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.     

Retrieving of Land Surface Temperature Using Thermal Remote Sensing and GIS Techniques in Kandaihimmat Watershed,Hoshangabad, Madhya Pradesh

Remotely sensed thermal infrared (TIR) data have been widely used to retrieve land surface temperature (LST). LST is an important parameter in the studies of urban thermal environment and dynamics. In the study, an attempt has been made using LANDSAT 8 thermal imagery to compute LST and the associated land cover parameters viz; land surface emissivity (LSE), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference water index (NDWI). Landsat 8 TIRS band 10 & 11 (thermal bands) during 21 Oct. 2016, 22 Nov.2016, 24 Dec. 2016 and 09 Jan. 2017 were processed for LST analysis. However, band 5 & band 4 of the imagery was processed for NDVI, band 6 & band 5 for NDBI and band 2 & band 5 for NDWI analysis. LST has been derived from both the bands 10 &11 and validated by in-situ observations on the date and time of satellite overpass from the study area. Band 10 derived LST have shown much temperature difference while comparing with the in-situ observations. However, LST derived from band 11 found similar & close to the in-situ measurements. Relationship between band 11 results and in-situ observed measurements were developed, which has showing a strong correlation with (r² = 0.991). Land surface emissivity were also evaluated which shows variation in different land cover surfaces like vegetation, settlement, forest cover and water body. The study has proven that land surface temperature derived from satellite band 11 is the actual surface temperature of the study area.     

青藏高原高寒草地植被指数变化与地表温度的相互关系

为了解脆弱的高原生态环境对升温过程的响应, 利用1982-2006年国家标准地面气象站地表温度和GIMMS-NDVI数据集, 探讨了青藏高原高寒草地植被指数和地表温度的变化特征及其相互关系. 结果表明:1982-2006年, 高寒草地NDVI、地表温度整体均呈现增加趋势, 年均NDVI、生长季NDVI、年最大NDVI(NDVI_max)与年均地表温度、生长季地表温度的上升趋势分别为0.007 (10a)^-1、0.011 (10a)^-1、0.007 (10a)^-1与0.60 ℃·(10a)^-1、0.43 ℃·(10a)^-1; NDVI_max与地表温度显著相关的地区达70.49%. 但是高原地形、气候、水文环境的空间差异性导致高寒草地NDVI与地表温度的相关关系十分复杂. NDVI_max与年均地表温度的相关性最为显著; 在返青期和枯萎期, NDVI与地表温度均为显著正相关. 不同的植被覆盖条件下, NDVI对地表温度的响应不同:植被覆盖差以及退化严重的地区, NDVI_max与地表温度呈负相关性; 反之, NDVI_max与地表温度主要表现为正相关.     

The relationship between land surface temperature and land use/land cover in Guangzhou,China

The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI were effective indicators for quantifying LULC impacts on LST.