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.     

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.     

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.     

基于植被指数和土地表面温度的干旱监测模型

干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品---土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。     

S patio-temporal pattern of land use and land cover and its effects on land surface temperature using remote sensing and GIS techniques: a case study of Bhubaneswar city,Eastern India (1991–2021)

Urbanization produces substantial land use changes by causing the construction of different urban infrastructures in the city region for habitation, transportation, industry, and other reasons. As a result, it has a significant impact on Land Surface Temperature (LST) by disrupting the surface energy balance. The objective of this paper is to assess the impact of land-use/land-cover (LU/LC) dynamics on urban land surface temperature (LST) of Bhubaneswar City in Eastern India during 30 years (1991–2021) using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms (MLA). The finding reveals that the mean LST over the entire study domain grows significantly between 1991 and, 2021due to urbanization (β coefficient 0.400, 0.195, 0.07, and 0.06 in 1991, 2001, 2011, and 2021 respectively) and loss of green space (β coefficient − 0.295, − 0.025, − 0.125 and − 0.065 in 1991, 2001, 2011 and 2021 respectively). The highest class recorded for agricultural land (49.60 km², accounting for 33.94% of the total land area) was in 1991 followed by vegetation (41.27 km², 28.19% of the total land area), and built-up land (27.59 km², 18.84% of the total land area). The sharp decline of vegetation cover will continue until 2021 due to increasing built-up areas (r = − 0.531, − 0.329, − 0.538, and − 0.063 in the 1991, 2001, 2011 and 2021 respectively). Built-up land (62.60 km², accounting for 42.76% of the total land area, an increase of 35.01 km² from 1991) as the highest class followed by water bodies (21.57%, 32.60 km² of the land area), and agricultural land (31.57 km², 21.57% of the land area) in 2021. Remote sensing techniques proved to be an important tool to urban planners and policymakers to take adequate steps to promote sustainable development and minimize urbanization influence on LST. Urban green space (UGS) can help improve the overall liveability and environmental sustainability of Bhubaneswar city.

     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

黔桂喀斯特山地NDVI的垂直分布特征

黔桂喀斯特山地地形复杂,植被覆盖度垂直特征分异显著,以往研究多从气候因子响应方面探讨其垂直分布差异,而研究区人地矛盾尖锐,人类活动对植被分布有重要的影响。文章以黔桂喀斯特山地为例,利用2010年MODIS13Q1 NDVI数据表征植被覆盖度,结合高程、坡度和坡向等地形特征,不同土地利用类型的分布情况,叠置分析研究区的NDVI垂直分布特征。结果表明:黔桂喀斯特山地以林地、耕地和草地为主,不同土地利用类型随海拔、坡度和坡向的变化呈现不同的分布特征。研究区NDVI平均值为0.59,其中林地NDVI最大,达到0.63,草地为0.58,耕地最小为0.54。空间分布上,贵州境内NDVI值大部分为0.5~0.6,广西境内自西北向东南NDVI值由0.8逐渐降低至0.4,以0.6~0.7为主。NDVI在垂直梯度上分布特征显著,与植被垂直地带性分布以及不同地类的垂直分布特征有密切关系。海拔分布上,NDVI在海拔小于200 m区间最小,400~600 m的区间最大;北部贵州整体海拔较高,但植被覆盖度较低;南部广西海拔较低,但植被覆盖度较高。坡度分布上,在坡度小于35°范围,随坡度增大,耕地、水域、建设用地面积迅速减少,林草地面积逐步增加,使得NDVI随坡度增大逐渐增大。坡向分布上,NDVI不随坡向变化呈现明显变化,仅偏东坡向稍大于偏西坡向。研究表明应根据海拔和坡度等地形特征,并考虑土地利用情况,因地制宜进行生态建设。      

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

In this study, the vegetation dynamics in Heilongjiang province and their relationships with climate variability were assessed using normalized difference vegetation index (NDVI) and meteorological datasets from 1981 to 2003. The conclusions from our results are as follows: (1) After 1981, vegetation cover, as indicated by the NDVI, exhibited an insignificant increasing tendency. However, the inter-annual variations of the NDVI showed apparent spatial differentiations. (2) The inter-annual changes of the NDVI were different from season to season. The spring and autumn NDVI values increased, while the summer and winter NDVI decreased. (3) The annual NDVI was significantly correlated with precipitation. Thus, as compared to temperature, precipitation was the dominant climatic factor affecting the vegetation dynamics in Heilongjiang province. (4) The trend in the NDVI showed a marked homogeneity corresponding to regional and seasonal variations in climate. Additionally, land use changes also play an important role in influencing the NDVI trends over some regions. All of these findings will enrich our knowledge of the natural forces that impact the stability of boreal ecosystems and provide a scientific basis for the environmental management in Heilongjiang province in response to climate change and human activities.     

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.     

内蒙古西辽河平原植被指数时空变化及其影响因素研究

西辽河平原位于我国北方农牧交错带,属半干旱气候,发育科尔沁沙地,生态环境极其脆弱,开展植被指数时空变化及其影响因素研究,对于预测土地退化风险意义重大,可为该流域生态环境保护治理及水资源合理开发利用提供技术支撑。利用2000—2019年MODIS NDVI数据,采用一元线性回归趋势法和Mann-Kendall检验分析了近20年来该地区的植被生长变化趋势及突变情况。从影响植被生长的水热条件出发,分析了NDVI值与气象因素（降水、气温）、土壤湿度、地下水埋深等因子的相关关系;结合人类活动,分析了土地利用类型变化对NDVI值的影响。结果表明:（1）2000—2019年生长季NDVI值整体呈上升趋势,不存在显著突变点,最高值0.56,最低值0.41。（2）NDVI值在空间上呈现“东高西低”的分布特征,不同用地类型的NDVI值由大到小依次为耕地>林地>沼泽地>滩地>草地>盐碱地>沙地。（3）92.5%的区域植被呈增长趋势,7.5%的区域植被呈减少趋势。（4）NDVI值与降水、气温、土壤湿度呈正相关关系,相关系数分别为0.86,0.78,0.81,降水对植被影响最大。（5）最适宜天然植被生长的地下水埋深约为3 m,当地下水埋深大于10 m时,NDVI值会随着埋深的增加剧烈减小。（6）人类活动如土地开垦、植树造林是近20年来NDVI值呈增加趋势的主要原因之一,在一定程度上改善了当地生态环境。     

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。     

Geospatial tools for assessing land degradation in Budgam district,Kashmir Himalaya,India

Land degradation reduces the ability of the land to perform many biophysical and chemical functions. The main aim of this study was to determine the status of land degradation in the Budgam area of Kashmir Himalaya using remote sensing and geographic information system. The satellite data together with other geospatial datasets were used to quantify different categories of land degradation. The results were validated in the field and an accuracy of 85% was observed. Land use/land cover of the study area was determined in order to know the effect of land use on the rate of land degradation. Normalized differential vegetation index (NDVI) and slope of the area were determined using LANDSAT-enhanced thematic mapper plus (ETM+) data, advanced space borne thermal emission and reflection radiometer, and digital elevation model along with other secondary data were analysed to create various thematic maps, viz., land use/land cover, geology, NDVI and slopes used in modelling land degradation in the Kashmir Himalayan region. The vegetation condition, elevation and land use/land cover information of the area were integrated to assess the land degradation scenario in the area using the ArcGIS ‘Spatial Analyst Module’. The results reveal that about 13.19% of the study area has undergone moderate to high degradation, whereas about 44.12% of the area has undergone slight degradation.     

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.     

Trends and variation in vegetation greenness related to geographic controls in middle and eastern Inner Mongolia,China

Extensive studies have investigated the relationships between climate change and vegetation dynamics. However, the geographic controls on vegetation dynamics are rarely studied. In this study, the geographic controls on the trends and variation of vegetation greenness in middle and eastern Inner Mongolia, China (mid-eastern Inner Mongolia) were investigated. The SPOT VEGETATION 10-day period synthesis archive of normalized difference vegetation index (NDVI) from 1999 to 2007 was used for this study. First, the maximum value compositing (MVC) method was applied to derive monthly maximum NDVI (MNDVI), and then yearly mean NDVI (YMNDVI) was calculated by averaging the MNDVIs. The greenness rate of change (GRC) and the coefficient of variation (CV) were used to monitor the trends and variation in YMNDVI at each raster grid for different vegetation types, which were determined from a land use dataset at a scale of 1:100,000, interpreted from Landsat TM images in 2000. The possible effects of geographic factors including elevation, slope and aspect on GRC and CV for three main vegetation types (cropland, forest and steppe) were analyzed. The results indicate that the average NDVI values during the 9-year study period for steppe, forest and cropland were 0.26, 0.41 and 0.32, respectively; while the GRC was 0.008, 0.042 and 0.033 per decade, respectively; and CVs were 10.2, 4.8 and 7.1%, respectively. Cropland and steppe shared a similar trend in NDVI variation, with both decreasing initially and then increasing over the study period. The forest YMNDVI increased throughout the study period. The GRCs of the forest also increased, although GRCs for cropland and steppe decreased with increasing elevation. The GRCs of cropland and steppe increased with increasing slope, but the forest GRCs were not as closely related to slope. All three vegetation types exhibited the same effects in that the GRC was larger on north-facing (shady) slopes than south-facing slopes due to differences in water conditions. The CVs of the three vegetation types showed different features to the GRC. The CVs for all three vegetation types were not affected by aspect. The CVs for forest and cropland showed minor effects with changes in elevation and slope, but the CV for steppe decreased with increasing slope, and increased with increasing elevations to 1,200 m, before decreasing at higher elevations. Our findings suggest that the role of geographic factors in controlling GRC should also be considered alongside climate factors.     

气候变化对中国北方荒漠草原植被的影响

气候变化对陆地生态系统的影响及其反馈是全球变化研究的焦点之一。利用气候变量实现对遥感植被指数所表示的植被绿度信息的模拟，可以尝试作为表达生物圈过去和未来状态的一种途径。利用1961-2000年的气温、降水和1983-1999年的NOAA/AVHRR资料，分析了中国北方地带性植被类型荒漠草原植被分布区的短尺度气候的年际和季节变化，及其对植被的影响。结果表明，过去40年中该区域年际气候变化表现为增温和降水波动。年NDVI的最大值（NDVI_max）可以较好地反映气候的变化，过去17年中NDVI_max出现的时间略有提前。综合分析NDVI、植被盖度、NPP、区域蒸散量、土壤含水量及其气候的年际变化，表明增温加剧了土壤干旱化，降水和土壤含水量仍是制约本区植被生长的根本原因。     

ERA-Interim地表温度数据集在青藏高原冻土分布制图应用的适用性评估

地表温度综合反映了大气、植被和土壤等因素的能量交换状况, 是冻土分布模型和一些寒区陆面过程模式的上边界条件, 对多年冻土分布制图和活动层厚度估算有重要意义. 为了评估ERA-Interim 地表温度产品在青藏高原地区的适用性, 综合比较了青藏高原69个海拔2 000 m以上气象站1981-2013年地面实际观测值与ERA-Interim之间的差异及其分布状况. 结果表明, 两种资料的变化趋势一致, 但是ERA-Interim地表温度在数值上与实际观测值差别显著, 平均偏低7.4℃. 原因之一可能是由ERA-Interim再分析资料格点的海拔高度与气象站实际海拔高度差异引起的. 根据两种温度产品之间海拔的差异, 对ERA-Interim地表温度重新进行模拟, 经过模拟后的ERA-Interim地表温度与实际观测值的差值在大部分气象站变小, 平均偏高0.4℃. 因此, 经过重新模拟的ERA-Interim地表温度基本能够反映青藏高原地表温度的真实情况. 以模拟后的ERA-Interim地表温度作为地面冻结数模型的输入参数模拟了青藏高原冻土分布, 结果表明青藏高原多年冻土区面积为1.14×10⁶ km², 季节冻土区面积为1.43×10⁶km².     

典型冰冻圈地区植被变化对人类活动的响应研究 ——以祁连山为例

在全球气候变化、种植业结构调整等背景下,亟需对祁连山区的植被展开长期有效的监测和持续研究。基于2000-2017年分辨率为250 m的MODIS数据,采用Mann-Kendall时间序列非参数估计模型、相关分析等方法,分析了祁连山区生长季NDVI与植被盖度的时空变化特征及其与气候因子的相关性,并从正反两方面就人类活动对对植被的影响做了讨论,得出结论如下：（1）从东向西祁连山年平均NDVI整体上逐渐减小,NDVI随海拔升高呈先增大后减小的特征,NDVI最大的区域分布在海拔2 700~2 900 m的范围内;（2）祁连山区域内NDVI显著减小的区域占祁连山总面积的0.6%,而显著增加的区域占总面积的33.6%,植被呈现出整体向好,局部退化的趋势;（3）2000-2017年,林地、草地和其他土地利用区的植被盖度分别以0.0029、0.0026和0.0004的速率增加,工矿用地的覆盖度以0.0112的速率在减少,反映出工矿业开发活动是造成植被盖度下降的主要因子;（4）植树造林区植被NDVI以0.0455的速度增加,而工程实施和矿产开发区NDVI以0.0125速度降低,表明人类活动是导致植被群落变化的主要因素之一。     

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.

     

Analysis of mining-induced variation of the water table and potential benefits for ecological vegetation: a case study of Jinjitan coal mine in Yushenfu mining area,China

The impact of high-intensity coal mining on water resources has drastically affected the local environment in the Yushenfu mining area, Northwest China. Many studies have ascertained that coal mining has caused reduction of the phreatic water level and vegetation death, which has deteriorated the fragile arid and semiarid ecosystems. However, at Jinjitan coal mine, it has been observed that the phreatic water level rose due to coal mining, which has been beneficial for the local ecology, especially the vegetation. In this study, the variation of the phreatic water table and its ecological effects were investigated by in-situ tests and remote sensing. The results suggest that the recovery of the permeability of the loess aquitard controls the vertical leakage of phreatic water, whereas horizontal recharge is controlled by land subsidence. An ecological impact evaluation, using the normalized difference vegetation index (NDVI), demonstrates that the vegetation is being gradually improved. Compared to a small improvement of ~9.7% in the unexploited area, a?~23.3% improvement occurred in the exploited area where low-density shrubs transformed to high-density shrubs and herbs. Emergence of phreatic water at the land surface in low-lying areas of the working faces may induce the formation of an oasis and wetland system in arid and semiarid areas. These findings could change the conventional negative impression around mining and improve ecological restoration practices.