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.     

Investigating vegetation biophysical and spectral parameters for detecting light to moderate grazing effects: a case study in mixed grass prairie

Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands.     

Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok,India

In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.     

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R ² value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.     

Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

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.

     

Assessment of land-use change and its impact on the environment using GIS techniques: a case of Kolkata Municipal Corporation,West Bengal,India

It is axiomatically true that urbanization in India's metropolises and large cities has been exacerbated since the beginning of the millennium, consuming the natural and semi-natural ecosystem on the outskirts of the city, resulting in a zone with a distinct climate known as urban climate. Such a climate—the result of a built-up environment is distinctly different from the natural climate as the paved surface and concrete skyscrapers not only destroy the natural ecosystem, it peculiarly induce a different kind of insolation, cooling and air drainage were lacking in green space, water bodies and open space cannot accommodate with environmental rhythm properly, resulting into the accumulation of heat, ecological derangement of subsurface soil which can easily be predicted by GIS analysis. This paper is an attempt to measure urban growth and its impact on the environment in the metropolitan city Kolkata. The use of satellite data and GIS techniques to detect urban expansion is a highly scientific strategy. Using geospatial techniques, the current study attempts to examine major urban changes in Kolkata and its surroundings from 1988 to 2021. Landsat 5 TM and Landsat 8 OLI temporal data are used to identify land-use change through unsupervised classification; Spectral Radiance Model and Split Window Algorithm method are used for identifying land surface temperature change. SRTM DEM (30 m) has been used to identify flood risk zones and several spectral indices like Normalized Difference Vegetation Index and Modified Normalized Difference Water Index are a further extension for environmental assessment. By all such suitable methods, a clearer change in an urban environment is detected within the period of 33 years (1988–2021). The result shows that the population changes, vegetation cover and built-up area, and accessibility are at a rapid rate. These changes are causing major environmental degradation in the city. The classification result indicates that appropriate land use planning and environmental monitoring are required for the long-term exploitation of these resources.

     

Environmental change detection in the central part of Iraq using remote sensing data and GIS

This study aims to assess the potential of several ancillary input data for the improvement of unsupervised land cover change detection in arid environments. The study area is located in Central Iraq where desertification has been observed. We develop a new scheme based on known robust indices. We employ Landsat (multispectral scanner, thematic mapper, and enhanced thematic mapper) satellite data acquired in 1976, 1990, and 2002. We use the Normalized Deferential Vegetation Index, Normalized Differential Water Index (NDWI), Salinity Index (SI), and Eolian Mapping Index. Two new equations were applied for the SI and the NDWI indices. Validation was performed using ground truth data collected in 16 days. We show that such an approach allows a robust and low-cost alternative for preliminary and large-scale assessments. This study shows that desertification has increased in the study area since 1990.     

利用MODIS和AMSR-E进行积雪制图的比较分析

MODIS和被动微波辐射计AMSR-E提供了识别积雪的不同方法.MODIS首先计算反映积雪在1.6μm强吸收特性的归一化差值积雪指数NDSI,在剔除卷云的影响后,得到MODIS积雪分布.AMSR-E则根据积雪在微波波段的差异性散射特性识别积雪.通过案例分析比较了MODIS和AMSR-E积雪分布,发现由于云的遮蔽使MODIS积雪分布面积会比实际小,但由于MODIS的空间分辨率很高,得到的积雪边界线轮廓清晰.而微波由于不受云的影响,得到的AMSR-E积雪分布比较符合实际,但积雪的边界线较粗.     

城市下垫面空间特征对地表产汇流过程的影响研究综述

快速城市化显著改变了城市下垫面空间特征,对地表产汇流过程产生了重要影响。综述城市下垫面空间特征对地表产汇流过程的影响研究进展,以下垫面空间特征对产汇流过程的影响为主线,聚焦不透水面和微地形两类空间特征,总结城市下垫面空间特征及其表征方法;从不透水率与不透水面空间变化、数值模拟与物理实验等方面,分别梳理不透水面和微地形等空间特征对地表产汇流过程的影响研究成果。展望未来城市下垫面空间特征对地表产汇流过程的影响研究重点和发展方向,主要包括影响机理解析、主控空间特征参数识别与敏感性分析、城市水文效应适应性应对等,以期为快速城市化下城市水文效应解析和城市洪涝灾害防治提供一定参考。     

不透水面遥感提取及应用研究进展

不透水面信息的提取方法与应用是近年来城市规划、热岛效应分析、水环境监测和水资源管理等诸多领域的研究热点.遥感技术的发展使不透水面快速准确提取成为可能.从影像特征(光谱、空间几何、时间)选择、分类器(参数、非参数)选择和空间尺度(像元、亚像元尺度)选择3个方面归纳和总结了各种不透水面遥感提取方法原理、应用现状和存在问题,回顾了不透水面在城市化监测、人口估计、水环境监测、热岛效应分析、水文气候建模分析等领域的应用,指出了不透水面遥感提取和应用的发展方向.     

Urban landscape extraction and analysis based on optical and microwave ALOS satellite data

Accurate mapping of urban land cover from satellite data provides essential input to urban landscape analysis, modelling and urban ecosystem studies. Additionally, analysis of urban landscape metrics will provide a positive step towards comprehensive understanding of the features of urban landscape structure and further planning. In the present study, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images and ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) dual-polarized (FBD) microwave images were used to extract urban land cover information by applying the decision tree method, and additional Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER/GDEM) was used to reduce the effects of mountains in Synthetic Aperture Radar (SAR) images due to high backscattering from urban construction land. A set of landscape metrics, such as landscape diversity, edge density and landscape shape indices with supplementary ecological meanings, were chosen to quantitatively analysis urban landscape patterns in arid environments. The overall accuracy assessment result was 91.50%, and the experimental results demonstrate that synergetic use of optical and SAR ALOS data has the potential and advantages for Arid Urban Region mapping, while the decision tree method showed intuitive simplicity and computational efficiency. The quantitative analysis results of landscape metrics showed that distribution of landscape types in Urumqi city were inhomogeneous, the urban landscape dominated by a few classes. Urbanization in this region has resulted in dramatic increases in patch density (PD), edge density (ED) and landscape shape complexity.     

城市不透水面与地表温度定量关系的遥感分析

作为城市下垫面的最主要组成部分,城市不透水面是引发城市热岛效应的主要因素,但目前不透水面与地表温度之间的定量关系仍不明确,不同研究的结论也不尽相同。选取了上海、广州、北京、长沙、兰州、福州等6个代表我国不同区域的城市作为研究区来进一步研究城市不透水面与城市热环境的定量关系。采用Landsat ETM+影像和线性光谱混合分析法提取出各个研究区的不透水面,利用热红外波段反演出各研究区的地表温度,采用多种回归模型和大样本量对二者之间的相关关系进行定量分析;在此基础上讨论了不透水面对城市热环境的影响机制。结果表明：不透水面与地表温度呈明显的正相关关系,并且以指数函数为最佳拟合模型,回归方程的相关系数均为0.750以上,最高可达0.954 1;高不透水面盖度地区的升温幅度更大,比低不透水面盖度地区高出0.600~1.700 ℃,其原因主要是高不透水面盖度地区的植被覆盖率低,蒸腾蒸发作用小。     

基于随机森林模型的城市不透水面提取研究 ——以呼和浩特市为例

城市不透水面信息对于城市生态环境动态演化过程研究具有重要意义。以Landsat 8遥感影像为数据源,以呼和浩特市为实证区域,进行了随机森林模型应用于城市不透水面的提取研究,并与目前应用广泛的支持向量机模型进行了对比分析。研究表明：在不同的抽样比例训练样本条件下,随机森林模型对于城市不透水面的提取精度均优于支持向量机的提取精度;对于随机森林模型和支持向量机模型,70%的训练样本比例均为最佳训练样本抽样比例。在该抽样比例下,随机森林模型提取城市不透水面的总体分类精度为93.29%,Kappa系数为0.9051,支持向量机模型的总体分类精度为91.26%,Kappa系数为0.8757;随机森林模型对于城市裸土的识别度较高,能更好地将城市裸土和不透水面进行区分,而支持向量机模型对于城市裸土、不透水面和绿地的区分能力均弱于随机森林模型。综合而言,随机森林模型对城市不透水面的提取精度优于支持向量机模型,随机森林模型可以有效应用于城市不透水面提取领域,进一步丰富了城市不透水面提取方法体系构成。     

多源遥感数据图谱协同岩石单元分类方法

岩石单元的结构、构造、差异风化和出露状况在遥感图像上综合表现为图形纹理特征即“图”标志，其矿物成分和组合则表现为光谱特征即“谱”标志.传统遥感岩石单元分类以利用其光谱特征为主，图形纹理特征为辅，因此分类精度有限.以新疆维吾尔自治区与甘肃省交界的北山西段为研究区，开展岩石单元图形指数和光谱指数协同分类方法研究.基于Worldview-2全色图像构建的图形指数，能够量化岩石单元的层理、构造、展布形态和微地貌等特征，包括0°和45°定向滤波图像及灰度共生矩阵计算出的同质性和异质性特征图像、熵特征图像；光谱指数基于Worldview-2多光谱图像和ASTER（Advanced Spaceborne Thermal Emission and Reflection Radiometer）短波红外波段图像利用比值、和-差方法构建.多源遥感图像构建的光谱指数其光谱波段涵盖可见光-近红外及短波红外，包括RI（Ratio index）_ASTER、SI（Spectral index）_ASTER、SI_Worldview-2.采用面向对象方法对建立的图谱指数进行多尺度分割，依据不同岩石单元出露规模建立适宜的分割尺度，利用光谱指数自动提取相应岩石信息，实现岩石单元自动分类.结果表明，实验区基于图谱协同方法共划分出17类岩石单元，总体精度达到83.62%，而单独利用Worldview-2和ASTER图像，仅划分出13类和14类岩石单元.提出的图谱协同岩石分类方法可为我国西部高海拔深切割无人区地质调查及找矿工作提供新思路和遥感技术支撑.      

Assessment of impervious surface growth in urban environment through remote sensing estimates

The fast growth in population and expansion of urban built area has led to the transformation of the natural landscape into impervious surfaces. Remote sensing-based estimate of impervious surface area (ISA) has emerged as an important indicator for the assessment of water resources depletion in urban areas and developed a correlation between land-use change and their potential impact on urban hydrology. In the present work, a remote sensing-based Impervious Surface Area (ISA) was carried out for New Okhla Industrial Development Authority (NOIDA) city, one of the fastest growing cities in National Capital Region (NCR) of India. The impervious surface area (ISA) of NOIDA was calculated for the years 2001, 2007 and 2014 using multi-temporal LANDSAT thermal data by applying linear spectral mixing analysis (LSMA) techniques to monitor the growth rate of impervious surface. The results observed by analysis of multi-temporal satellite images show an extreme temporal change in the growth of ISA in the city. The ISA observed for the year 2001 is 28 sq.km; in 2007, its increase was 48 sq.km and was 132 in 2014. The results were observed from this work through the use of satellite data which is very important for water resource management, planning and prediction of ISA impact on hydrology.     

基于时间序列建模的城市热岛时间尺度成分分离方法与应用

城市热岛效应是全球与区域气候变化研究中的焦点问题。基于2001—2012年较长时间序列的北京市MODIS地表温度产品及相关NDVI和反射率产品,给出地表温度时间序列构建方法。基于站点气象观测资料进行的精度验证表明地表温度时间序列构建方法可行,并最终给出城市热岛强度的量化方案。研究选取统计学中X-11-ARIMA时间序列建模方法,分离并分析城市热岛强度时间序列的结构性成分。分析发现,以平均城乡温差为指标的北京城市热岛强度季节性特征明显,与城乡土地利用状况、季节性地表覆盖、地物热特性以及气候因子等联系密切。趋势—循环特征与城市扩张速度及入选城市区域面积相关。以已发生城市热岛区域城乡平均温差为指标的北京城市热岛强度趋势—循环特性在12年间表现平稳。时间序列建模分析提取出不规则变动成分,为定量研究偶然因素对城市热岛的影响提供了可能。     

Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R₅₂₅). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R² = 0.96 and RMSE = 5.80ug.cm^?2 for Cab if validated by SIM30, while R² = 0.95 and RMSE = 6.39ug.cm^?2 for SLR. NN also gives satisfactory result with R² = 0.80 and RMSE = 5.93ug.cm^?2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R² and 12.20ug.cm^?2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.     

两种用于作物冠层叶绿素含量提取的改进光谱指数

在深入探讨目前广泛使用的提取叶绿素含量的植被指数的光谱响应机制基础上,利用PROSPECT+SAIL模型模拟的作物冠层反射率样本数据对比分析了这些植被指数对叶绿素含量变化的敏感性差异,包括PSSRa、PSSRb、PSNDa、PSNDb、NPCI、PRI、MCARI和TVI等.结果表明,上述植被指数或对土壤背景变化敏感,或受高值LAI影响趋于饱和,对作物叶绿素含量反演效果均不理想.提出了4种基于TVI和MCARI的改进植被指数MTVI1、MTVI2、MCARI1和MCARI2,揭示了它们对土壤背景和LAI不敏感,对叶绿素含量变化更为敏感的光谱机制,并根据实验数据对其进行验证.实验表明,改进的植被指数MTVI2和MCARI2是作物冠层叶绿素含量较好的预测器,可据此建立作物冠层叶绿素含量反演模型.     

基于GEE的东北三省城市建设用地扩张研究

根据社会经济和人口变化,基于云平台实现中高分辨率遥感影像城市建设用地的快速提取,可以高效准确地对长时间序列大范围城市建设用地扩张情况进行动态变化监测,为城市的管理和规划提供借鉴和参考。本文基于Google Earth Engine（GEE）云平台,利用NPP/VIIRS（suomi national polar orbiting partnership/ visible infrared imaging radiometer suite）年度平均夜间灯光数据和阈值分割法提取城市区域,获得3 142景覆盖东北三省城市的Landsat影像;在原始光谱波段的基础上构建指数、纹理和地形特征,利用SEaTH算法进行特征优化,根据JM距离的取值将特征数量从20个精简到12个;在城市区域中利用随机森林（random forest, RF）算法结合最优特征对Landsat月度合成影像进行再次分类,更加精确地提取建设用地。研究结果表明,东北三省城市建设用地提取平均总体精度和Kappa系数分别为96.19%、0.92,有较高的效率和可靠性;1989—2019年东北三省城市建设用地总面积扩张49.07%,省会城市中沈阳扩张速率较快,长春次之,哈尔滨最慢;人口因素和经济因素是推动城市建设用地扩张的主要因素。