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.     

整合陆地表面温度与植被指数信息进行地表覆盖变化研究

利用植被指数(NDVI)的多时相或时序资料进行地表覆盖研究已取得了大量成果。随着陆地表面温度(LST)遥感反演精度的不断提高,将LST与NDVI结合起来进行地表植被动态变化的监测已成为可能。选择我国北方草地及其东南部毗邻的农牧交错带(farmingpastoralzone)为研究区。该区位于内蒙古高原东南边缘和黄土高原北部,半干旱气候,经纬度范围为:35°～50°N,100°～125°E。     

黑龙江省漠河地区土地覆被与地表温度时空变化特征研究

以我国高纬度多年冻土区漠河县城区及郊区862 km²的区域为研究对象,基于RS和GIS技术对该区地表覆被变化和地表温度进行研究。结果表明：地表类型和地表温度反演结果与现场比对结果一致;从1988年到2015年,经过火灾后重建和城镇化发展,建设用地面积增加了11.33 km²;以2015年为例,冬季由于积雪覆盖,有79.02%的区域无植被覆盖,夏季有80.91%的区域为高植被覆盖。冬季地表温度和高程具有高度相关性,高程每升高100 m,地表温度平均升高2.27℃,表明该区地表温度也存在显著的逆温现象。该区城镇热岛效应全年存在,城镇区域地表温度高于全区平均值,夏季最显著,最高温差可达6.37℃。地表温度与NDVI具有明显的负相关关系;不同土地利用类型地表温度由低到高的顺序为水体-林地-草地-裸地-建筑用地。     

Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis,Xinjiang, China

Land use and land cover (LULC) changes caused by human activities have strong influences on regional environment. Land surface temperate plays an important role in studying the impact of LULC changes on regional environment. In this paper, remotely sensed thermal infrared data were used to assess land surface temperature (LST) in the Weigan and Kuqa river oasis, Xingjiang, one of the important agricultural areas in the northwestern China. The present study deals with the extraction of LST and the relationship between LULC changes using Landsat 5 TM acquired on September 25, 1989, and September 6, 2011. The results indicate that the surface temperature of water body, bare land, and desert changed significantly between 1989 and 2011. In general, the LST was lower in 1989 than in 2011. There were no lower, higher, and highest temperature zones in 1989. However, the minimum temperature was 10.7 °C in 1989 and 15.8 °C in 2011. The maximum temperature was 29.3 °C in 1989 and 41.8 °C in 2011. Regarding the LULC types, the desert features in the Gobi Desert warmed more quickly than the oasis. So, the temperature of the oasis was lower than the surrounded areas, resulting in a so-called “cold island” phenomenon. Oasis cold island effect index (OCIEI) shows that stability of oasis had rising trend from 1989 to 2011. In addition, the impact of LULC changes on LST was analyzed and the driving forces were also analyzed from 1977 to 2011. This study is significant for further understanding of the energy exchange status of soil-plant-atmospheric system and the regional heat distribution in arid and semi-arid areas of the northwest China.     

Estimation of land coverage from a land cover classification derived from remotely sensed data

Remotely sensed data are an attractive source of land cover information. In many applications the required information relates to the extent or coverage of land cover class(es) in a region, which is generally derived from a count of the pixels allocated to the class(es) of interest in a classification. A highly accurate classification is not required for the derivation of accurate estimates of class coverage, provided the classification is accompanied by appropriate information on its quality. For instance, the information on classification quality contained in the classification confusion matrix can be used to significantly increase the accuracy of the estimates of land coverage. This is illustrated with reference to a case study focused on the estimation of despoiled land coverage in administratively defined local district in industrial South Wales from Landsat TM data. The accuracy of the investigation was assessed relative to a map of despoiled land cover for this region produced by conventional methods. From an image classification of moderate accuracy, the classification accuracy ranged form 57–83% between the districts investigated, a pixel count provided estimated of despoiled land coverage that were only poorly correlated to the mapped coverage;r = 0.27. Using the information on the pattern of error in the class allocation contained in the classification confusion matrix the estimation accuracy was increased significantly, with a correlation ofr = 0.81 observed between the remote sensing based estimate and the mapped land coverage. Furthermore, the r.m.s. error in despoiled land coverage estimation was reduced by approximately half, to less than 1% district area, when the classification was used in conjunction with information on the pattern of classification error.     

新疆于田绿洲地表温度对土地利用/覆被的响应监测

以于田绿洲为例,以Landsat TM/ETM+图像为主要的数据源,提取了于田绿洲的土地利用/覆被信息.利用单窗算法反演了地表温度,综合分析了2001-2011年间于田绿洲±地利用/覆被变化(LUCC)及其对地表温度的影响,同时利用像元分解模型得出绿洲植被覆被度,并分析了其与地表温度的关系.结果表明:在2001-2011年的10 a期间,于田绿洲最主要的土地利用类型是以人类为主导因素的,同时以自然利用类型向人工利用类型的转化最为显著.耕地、水体等以人类为主导因素的土地利用类型面积增加了809.65 km2,盐渍地等以自然为主导的土地利用类型面积减少了758.76 km2,人为影响是于田绿洲较主要的驱动因素.于田绿洲土地利用/覆被变化改变了地表温度的空间分布特征,在土地利用/覆被类型发生变化的区域,地表温度有升有降,但总体上地表温度的空间分布与土地覆被类型的空间变化格局存在明显的一致性.地表温度的变化与植被覆被度的变化有关,证明了植被在改变下垫面热量分布的格局中扮演着重要作用.     

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.

     

多种再分析地表温度资料在中国区域的适用性分析

利用1979-2010年中国805个站点的逐日地表温度观测资料与ERA-Interim、ERA-Interim/Land、JRA-55、NCEP/NCAR和NCEP/DOE五种再分析地表温度资料, 对五种再分析地表温度资料在中国区域的适用性进行了比较与分析. 结果表明: 从空间分布特征来看, JRA-55资料与观测资料最为接近; 五种再分析地表温度资料在东部地区的适用性好于西部地区, 且均不能很好地模拟出青藏高原地区和西北西部地区的地表温度分布. 从地表温度的长期变化来看, 五种再分析地表温度资料虽然可以描述地表温度变化趋势, 但均存在低估现象, 且低估现象随着地表温度的增加变得更加严重. 在地表温度人工观测和自动观测交接的时间段, 可以使用再分析资料作为地表温度均一化的参考序列.     

Dynamics of land use/cover changes and the analysis of landscape fragmentation in Dhaka Metropolitan, Bangladesh

Rapid urban expansion due to large scale land use/cover change, particularly in developing countries becomes a matter of concern since urbanization drives environmental change at multiple scales. Dhaka, the capital of Bangladesh, has been experienced break-neck urban growth in the last few decades that resulted many adverse impacts on the environment. This paper was an attempt to document spatio-temporal pattern of land use/cover changes, and to quantify the landscape structures in Dhaka Metropolitan of Bangladesh. Using multi-temporal remotely sensed data with GIS, dynamics of land use/cover changes was evaluated and a transition matrix was computed to understand the rate and pattern of land use/cover change. Derived land use statistics subsequently integrated with landscape metrics to determine the impact of land use change on landscape fragmentation. Significant changes in land use/cover were noticed in Dhaka over the study period, 1975–2005. Rapid urbanization was manifested by a large reduction of agricultural land since urban built-up area increased from 5,500?ha in 1975 to 20,549?ha in 2005. At the same time, cultivated land decreased from 12,040 to 6,236?ha in the same period. Likewise, wetland and vegetation cover reduced to about 6,027 and 2,812?ha, respectively. Consequently, sharp changes in landscape pattern and composition were observed. The landscape became highly fragmented as a result of rapid increase in the built-up areas. The analysis revealed that mean patch size decreased while the number of patches increased. Landscape diversity declined, urban dominance amplified, and the overall landscape mosaics became more continuous, homogenous and clumped. In order to devise sustainable land use planning and to determine future landscape changes for sound resource management strategies, the present study is expected to have significant implications in rapidly urbanizing cities of the world in delivering baseline information about long term land use change and its impact on landscape structure.     

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.     

基于不同土地覆盖类型NDSI阈值优化下的青藏高原积雪判别

美国国家雪冰数据中心（NSIDC）发布的MODIS第6版本逐日积雪范围产品（V6）仅提供了归一化积雪指数（NDSI）,而用户往往关心的是积雪范围或积雪覆盖率。NSIDC推荐全球积雪范围最佳的NDSI阈值为0.4,但是青藏高原地形复杂,积雪斑块化特征明显,单一的NDSI阈值并不能精确地判识不同下垫面上的积雪。不同的土地覆盖类型可能影响积雪判别的NDSI阈值。以青藏高原为研究对象,基于高分辨率卫星Landsat-5 TM数据,获取了青藏高原不同土地覆盖类型下判识积雪的最优NDSI阈值。结果表明,在草地和稀疏植被地表类型下,最优NDSI阈值分别为0.33和0.40;在其他下垫面类型下,最优NDSI阈值为0.47。以Landsat 8 OLI数据为"真值"对该NDSI阈值确定的积雪范围进行了精度检验。结果表明,采用新的NDSI阈值获取的MOD10A1 V6积雪范围产品的总体精度OA、错分误差OE和漏分误差UE分别为87.88%、5.20%和6.87%。而采用传统的0.4阈值时,其OA、OE和UE分别为87.36%、3.98%和8.60%。这表明考虑不同土地覆盖类型下的NDSI阈值优化可以有效地提高青藏高原积雪判别精度,特别是对占比面积较大的草地区域,通过NDSI阈值优化可以更加准确地识别积雪范围。     

Application of the Landsat-5TM Image Data in the Feasibility Study of Mudflow Hazards in the Southern Taihang Mountains

The Taihang Mountains area is an area in North China where serious mudflow hazards take place frequently. The hazards often obstrust traffic and make it difficult to carry out conventional ground investigations of the mudflow hazards. This paper introduces the feasibility study of mudflow hazards by using Landsat-5TM data. The study has achieved a great success through adopting both the faint spectral enhancement technique for mudflow fans (or other depositional areas) and comprehensive study of the environmental background of pregnant mudflows. Thus, remote sensing as a fast, convenient, low-cost and effective technical method can be used to recognise the situation of mudflow hazards so that effective rescue can be provided.     

Effects of land use/cover change on regional land surface temperatures: severe warming from drying Toshka lakes,the Western Desert of Egypt

Natural Hazards - Toshka depression in the Western Desert of Egypt witnessed big floods in late 1990s from the Nile River behind the High Dam, where many lakes were formed. By the year 2003, the...     

Comparison of two split-window methods for retrieving land surface temperature from MODIS data

Land surface temperature (LST) is a key parameter in environment and earth science study, especially for monitoring drought. The objective of this work is a comparison of two split-window methods: Mao method and Sobrino method, for retrieving LST using MODIS (Moderate-resolution Imaging Spectroradiometer) data in North China Plain. The results show that the max, min and mean errors of Mao method are 1.33K, 1.54K and 0.13K lower than the standard LST product respectively; while those of Sobrino method are 0.73K, 1.46K and 1.50K higher than the standard respectively. Validation of the two methods using LST product based on weather stations shows a good agreement between the standard and Sobrino method, with RMSE of 1.17K, whereas RMSE of Mao method is 1.85K. Finally, the study introduces the Sobmao method, which is based on Sobrino method but simplifies the estimation of atmospheric water vapour content using Mao method. The Sobmao method has almost the same accuracy with Sobrino method. With high accuracy and simplification of water vapour content estimation, the Sobmao method is recommendable in LST inversion for good application in Ningxia region, the northwest China, with mean error of 0.33K and the RMSE value of 0.91K.     

Changes of land cover in the Yarlung Tsangpo River basin from 1985 to 2005

Land cover is closely related to environmental changes and socioeconomic development. Land-cover change in the Tibetan Plateau (TP) is different from that in the lowlands; however, a detailed land-cover change in areas such as the Yarlung Tsangpo River (YTR) basin in the TP has not been reported. To fill this gap, the current study explores the land-cover change between 1985 and 2005 in the YTR basin. The results show that only 1 % of the land cover in the YTR basin changed during this time period. The most significant land-cover changes included increases in forest and built-up areas as well as decreases in grassland, water and wetland areas. By percentage, the most rapid land-cover change occurred for built-up areas with an annual variation of 2.07 %. There was an obvious vertical distribution pattern for land-cover types in the YTR basin; from low to high, the average altitudes were forest, farmland, built-up, grassland, water and wetland, and bare land. The average altitude and slope for most land-cover types did not vary over the past 20 years. However, the average altitude and slope of built-up significantly decreased, especially in the zone between 3,500 and 4,000 m. The water and wetland area in altitudes above 4,500 m increased; however, they decreased in the zone between 3,500 and 4,000 m. Natural factors cause most land-cover changes, whereas the increasing intensity of human activities cause some changes to built-up and farmland. Additional attention should be paid to the study of the mechanism of land-cover change in the TP.     

南京河西地区地面沉降成因分析

为了查明南京河西地区地面沉降的成因,笔者等在系统研究水文地质与工程地质条件的基础上,研究了地面沉降的分布特征和发展规律,以及地面沉降与地下水位、软土分布及建筑荷载之间的关系,进而揭示了南京河西地区地面沉降的成因。研究结果表明：南京河西地区不开采地下水,地面沉降与软土层厚度和建筑荷载分布关系密切,地面沉降主要受建筑工程影响,即建筑荷载和深基坑降水的作用。     

Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island,Malaysia

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.