In order to improve the phenomenon that different objects perform the same spectral characteristics in land use mapping of high spatial resolution data and the “mixed pixel” problem caused by lower spatial resolution in land use mapping of medium spatial resolution data, this study took GF-1 and OLI as a case and proposed a method of combining high spatial resolution data and medium spatial resolution data for fuzzy classification of land use. Firstly, texture information of GF-1 and spectral information of OLI were compressed and strengthened by principal component analysis (PCA), respectively. Compressed texture information of GF-1 and compressed spectral information of OLI were layer stacked. The combined data of three bands feature was received. Then, the feature combined data was segmented into three different levels of 60, 80, 100 based on texture and spectral characteristics of the different land use types in feature combined data. Finally, the fuzzy logic membership functions of the land use types were built based on texture and spectral difference of the different land use types. In this way, the fuzzy land use classification of the study area was carried out. Results shows that the PCA method compressed and strengthened GF-1 and OLI of study area effectively and the proposed method classified the land use of study area successfully receiving a high total accuracy of 93.52%. The method proposed in this paper offered a new idea for classification feature selecting in object-oriented classification and had some significance for other classification research of combining high spatial resolution data and high spectral resolution data.

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities of observing snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global automated binary maps of snow cover. NDSI and other classification algorithms were used to inverse subpixel information of Snow Cover Fraction (SCF), but these algorithms neglected the relation between SCF and Snow Grain Size (SGZ). The SGZ might affect snow reflectance spectral curves, while most subpixel classification algorithm took advantage of the spectral feature space. The collaborative inversion of SCF and SGZ helped improve the understanding of the physical properties of snow. Meanwhile, it was possible to improve the retrieval accuracy of SCF. The framework of spectral mixture analysis (SMA) was widely used in the target detection of remote sensing images because of its ability to extract subpixel information and SMA could use mathematical methods to model SCF with snow reflectance spectral curves with different snow grain sizes. In this paper, in view of the snow cover with MODIS remote sensing image, based on the framework of spectral mixture analysis, the snow reflectance spectral library with different grain sizes was built by asymptotic radiative transfer (ART) model, and a sparse unmixing algorithm of snow cover fraction retrieval was proposed considering the endmember variability of snow with other materials and bilinear radiative process of endmembers. The ART model had a higher efficiency compared with MIE scatter model. Meanwhile, ART model considered snow grain shape parameters. The majority algorithm of SMA assumed the endmembers independent, which might neglect the interaction of endmembers, while bilinear radiative process of endmembers could consider second-order scattering effects, which had physical meaning. This algorithm firstly used asymptotic radiative transfer model to establish reflectance spectral library with different grain sizes, and Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm was used to obtain the spectral library of vegetation, soil and rock shadow. After the establishment of a variety of spectral libraries, the root mean square error index was used to get the optimal combination of endmembers for each pixel as MODIS Snow Covered Area and Grain Sizes(MODSCAG) model, which could accurately describe the endmember variability. After the optimal endmembers combination obtained, the bilinear radiative process was added into sparse regression spectral mixture analysis to simultaneously obtain snow cover fraction and snow grain size. Experimental results showed that this method could simultaneously inverse the snow grain sizes and snow cover fraction, and the retrieved snow grain sizes is smaller than that from single band of asymptotic radiative transfer model The accuracy of retrieved snow cover fraction is increased slightly compared with MOD10A1 product.

With the rapid development of economy, air pollution has become an important environmental problem, attracting wide attention. As one of the main air pollutants, NO₂ (nitrogen dioxide) becomes focus of the related research. It was found that the concentration of NO₂ varied with different regions by comparing monitoring data in different monitoring sites. Thus, simulation of its spatial distribution and analysis of the influential factors of the underlying surface have important value. The Land-use Regression (LUR) model is a method that combines, analyzes and display a multivariate regression model with spatial land-use data, monitoring data and other relevant geographic data on a map. In this study, the land use regression model is built by using a buffer analysis, overlay analysis, Spear-man correlation analysis and multiple regression analysis and it was used to identify the underlying surface factors related to the NO₂ concentration and simulate the spatial distribution ofNO₂ concentration. The results show that the spatial distribution of NO₂ mass concentration can be modeled accurately by LUR model. Based on the influential factors of the underlying surface, the following conclusions can be drawn: The increase of urban residence area, rural residence area, industrial land area and the length of the road and the reduction of the distance from the pollution source will increase the NO₂ concentration. The increase of arable land area, green area and water area will decrease the NO₂ concentration. The map of simulation results shows that the highest NO₂ concentration is located in industrial districts and the NO₂ concentration is lower where it is far from the city center. Changing the industrial structure of industrial land and increasing the green land can help reduce the NO₂ concentration.

Landsat-8 satellite was designed to have two thermal infrared bands, TIRS band 10 and band 11. But USGS (United States Geological Survey) pointed out that some calibration errors would be found with the band 11. It is recommended to use only the TIRS band 10 in quantitative research rather than using two channels. When using a single channel algorithm in retrieving land surface temperature (LST), we must priorly have the surface emissivity and have the atmospheric correction processed. The traditional methods used in the atmospheric correction depend on the empirical relationships or atmospheric radiative transfer model. However, both of the two methods have deficiencies, for example, as the empirical method depends highly on the training data, it is incapable under certain conditions. On the other hand, the method that based on the atmospheric radiative transfer model has to run the designated codes each time, which is not an appropriate choice for producing LST. In this paper, we propose a new atmospheric correction model applied to the single channel method with Landsat-8 TIRS band 10 data. The results show that the RMSE (Root Mean Squared Error) of the total transmission is 0.003, the RMSE of the upwelling radiance is 0.0004 and the RMSE of the downwelling radiance is 0.0004. Compare with the traditional methods, the proposed model bases on the physical mechanism of atmospheric radiative transfer model and has a higher accuracy. Moreover, the proposed model could also be used without the help of any atmospheric radiative transfer model. That is, this model will have a better prospect of application.

Food security is an important guarantee for the stable development of our country and the area of planting grain is the basis of food security, so the estimation of the area of planting grain is important. Remote sensing technology is an important method of estimating crop grain area at present. The classification accuracy is affected by cloud and mist, which cannot be avoided. To solve this problem, this study presented a method for recognizing rice based on GF-1 time-series image. With long time-series of GF-1 images, three indices of middle-season rice and late-season rice, namely near infrared band reflectance (NIR), red (R) band reflectance and the normalized difference vegetation index (NDVI) characteristics are extracted. Spectrum and the characteristic curve of vegetation index time-series are fitted. We analyzed the ratios of values of discrete near infrared band, red light band and NDVI of images of multiple temporal phases falling on both sides of the sensitive area of the fitting NIR, R and NDVI time-series curve of middle-season rice and late-season rice. This area can also be seen as the target area of rice identification features and only those reaching a certain proportion can be identified as certain type of rice. Under this condition, three kinds of situation should be considered comprehensively and voted to decide final classification results. The means of samples are used to fit the curve for each image. The outliers are eliminated from the ground samples in advance. Statistical analysis of ground samples defined target characteristics. The result indicated that: (1) Using polynomial fitting method based on least square principle to fit NIR, R, NDVI time series characteristic curve, fitting effect is better when fitting degree is 3 and it can satisfy the need of subsequent classification. (2) Different setting proportions led to different classification accuracy, and the overall accuracy is 95.76%, the user accuracy of middle-season rice and late-season is 95.97% and 95.95% when the setting proportion is not less than 50%. (3) The method proposed in this study could solve the problem of the combination of complex phases, and significantly weaken the influence of cloud and fog on crop classification, especially in South China.

The drought detection of a large area by using the remote sensing data has been an important method in drought monitoring. However, the conventional remote sensing methods mainly focus on some single drought response factors, such as the soil moisture or vegetation status, and the drought monitoring study that integrated with multiple factors is relatively limited. In order to explore the relationships among multiple drought factors, a random forest algorithm was applied. Random forest is a machine learning method, which has many advantages such as being accurate, handy, fast and stable, and it has been used in many fields in recent years. In this paper, a remote sensing drought model was developed using the random forest algorithm and the multi-source remote sensing data, including MODIS, TRMM and SRTM-DEM. Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Land Cover types (LC), TRMM-Z, DEM and Available Water Capacity (AWC), which were extracted from the remote sensing data and other soil data, were used as the independent variables, and the comprehensive meteorological drought index (CI) was used as the dependent variable. The training and testing experiments were carried out in Henan Province from April to September annually between 2001 and 2010. The results showed that the model and CI had highly significant correlation and their concordance rate reached 81% with respect to the drought classes from 2009 to 2010. In the study case′s period, the overall concordance rate was 74.9% between the model results and the Standardized Precipitation Evapotranspiration Index (SPEI) of the meteorological stations, from which the concordance was found to be the highest and the vacancy and miss rate was the lowest in September. The correlation between the model results and the soil moisture within 10 cm depth was highly significant, and their correlation coefficient varied between 0.475 and 0.639, which indicated that this model could effectively detect the agriculture drought. In addition, the drought event of Henan Province from April to June in 2011 was simulated by the proposed model, and the results could reflect the actual drought situation and its spatial variation. Therefore, this method could be well applied to monitor regional drought events.

As a big city in Northeast China, Harbin has experienced dramatic urban expansion in recent decades, followed with increased impervious surface area (ISA) and land surface temperature (LST) effects. In order to explore the relationship between ISA and LST under such a rapid urbanization process in urban and rural areas, based on the National Environmental Remote Sensing temporal resource information platform of land use/cover change (LUCC) data set, we extracted the construction land for urban and rural areas between 2001 and 2015. Combined with the end-member selection model for vegetation-impervious surface-soil (V-I-S) and the fully constrained least squares linear mixed pixel decomposition model, we extracted the impervious surface (a resolution of 15 m×15 m). The mono-window algorithm and Landsat images were used to retrieve the LST of Harbin City in the summer of 2001 and 2015. The results showed that: (1) ISA had increased by 163.96 km² and an expansion of 259.05 km² had occurred to the construction land, indicating an increase of 10.26% between 2001 and 2015; the proportions of ISA in construction land for urban and rural areas are 43.92% and 21.35% respectively in 2001, and 49.14% and 34.27% in 2015. The ratio of the poor had reduced from 22.57 to 14.87%, and the change of per unit construction land in the rural area is more sensitive. (2) The urban areas are dominated by inferior low temperature, medium temperature and inferior high temperature, with fast increasing speeds; while the rural areas are dominated by inferior low temperature and medium temperature, within which the inferior low temperature and inferior high temperature have faster increasing speeds. (3) LST and ISA have a significant positive correlation, and the temperatures for the low, medium and high-density areas of ISA were raised up by 1.16, 1.45 and 1.79 ℃ respectively. Also, the temperature rose faster in the urban area than the rural area under the same coverage of ISA. In summary, the partitions of LST have severe changes, which are accompanied with substantial expansion of ISA, and the increase of LST is significant in response to the expansion of ISA.

The formation and growth of urban agglomerations have turned to be one of the main urbanization mode in China. New urbanization strategy issued by central government also gives specific attention to smart urban growth especially in rising urban agglomerations. How to select the optimized spatial pattern of urban growth for urban agglomerations becomes one of the most important parts in new urbanization planning. Cellular automata (CA) have proven to be efficient in modeling urban expansion and they have been widely applied to the simulation of urban growth in many cities. The simulation results derived from CA have also provided scientific references for making land use planning. This study established a modeling framework based on a constrained CA model to simulate the urban expansion with the consideration of ecological stress effect and it is oriented to the concept of building "ecological city in future ". The constraints of both ecological sensitivity and urban development suitability were incorporated into the urban growth modeling. Three main kinds of growth scenarios including the chaotic edge extension, the restrained expansion for ecological-conservation and the coordinated growth pattern were designed and discussed. The visual and quantitative comparison was further carried out to detect a plausible growth pattern for decision-making in land use planning. The three major indices including construction suitability, ecological security and landscape compactness were used to measure the rationality of the three designed scenarios, quantitatively. Meanwhile, urban agglomerations in the middle reach of the Yangtze River have turned to be the fourth growth pole in China, and Nanchang metropolitan area has become one of the most important development nodes of this growth pole. In recent years, Nanchang metropolitan area has experienced rapid urbanization, which has resulted in a series of ecological problems. Selecting an optimized spatial pattern is important and attractive to direct the future smart urban growth of Nanchang metropolitan area. Therefore, this study selected Nanchang metropolitan area as a case study area to analyze the rationality of the growth patterns. The results indicated that the coordinate growth scenario can meet the demands of both urban expansion and ecological conservation in Nanchang metropolitan area. The simulation pattern under this scenario can greatly reduce ecological stress effect from urban growth and also generate the highest total value of the selected three indices in 2049. The coordinate growth scenario can be used as a scientific reference for making spatial planning of Nanchang metropolitan area such as delimiting urban growth boundaries. This study also indicated that urban growth modeling incorporated with ecological stress effect can provide an efficient tool for decision-making in urban land use planning.

The rapid development of the highway has important effects on the regional ecological environment while promoting economic development. With the support of geographic information system (GIS), this study took the 10 km buffer zone around expressways of Beijing as the research area and used buffer zone analysis, spatial analysis, statistical analysis and other methods to explore the influence of the land use changes around expressways on landscape patterns. The results showed that: (1) construction land, cultivated land and forest land were the main land use types around highways in Beijing during the 10 years. The main land use change was that the cultivated land was transferred into the construction land and it accounted for 80% of all imported area. (2) For the spatial changes, the main transferring changes were that forest land, cultivated land and construction land were transferred among each other. With the increase of the distance from expressways, the main land use types changed from the cultivated land and construction land into woodland and farmland. (3) In 2005-2015, the differences of landscape indices changing were obvious in each landscape type. The changes of patch density and splitting index of water were the biggest, the shape index of cultivated land showed the fastest increasing, and the splitting index of construction land reduced a lot, which were closely related to the land use transfer. (4) Based on the land use changes, the affected range of the highway network on landscape pattern was about 6 km in Beijing and it mainly caused the patch density, landscape shape index, diversity index reduced and aggregation index increased. The main reason for differences in the spatial change of the landscape around expressways was the transfers between construction land and cultivated land and the change of forest land into construction land.

The acceleration of urbanization plays an important role in regional heat environment, whose changes may lead to a series of ecological problems. A scientific evaluation on the heat environment of urban agglomeration is essential to urban planning and construction. Based on the construction of a standard of heat environment security levels, we analyzed the spatio-temporal variation of the heat environment security pattern and its causes from land use changes in the Yangtze River Delta urban agglomeration using the MODIS land surface temperature products. The conclusions are as follows: (1) in 2015, the dangerous zones of the heat environment in the Yangtze River Delta urban agglomeration were mostly located in or closed to the urban built-up regions. For example, the “Z” region of Nanjing, Shanghai, Hangzhou, Ningbo and other cities were most obvious. The critical security zones were mostly located in suburbs while the relative security zones were mainly distributed in the northern plains of the Yangtze River. The security zones were mainly located in Hangzhou and its southern mountain and hilly region, most of the region of Tai Lake and the north of the Yangtze River Delta urban agglomerations; (2) the dangerous zone, the critical security zone, the relative security zone and the security zone showed an upward trend, a slight upward trend, a downward trend and a first downward then upward trend, respectively; (3) the primary reason of the decline in security levels of the heat environment was the high ratio of the build-up areas and the low ratio of woodlands. Additionally, the large quantity of croplands occupied by build-up areas was also the reason of the expansion of dangerous zones.

The implementation of ecological conservation and construction projects in the “Three-River Headwaters” region had a positive impact on its ecological environment. Through comparative analysis of remote sensing images in 2004 and 2012, the dataset of changes in degraded grassland after the implementation of ecological projects was acquired. Based on the dataset, we analyzed the grassland restoration trend and degradation situation in the “Three-River Headwaters” region. The results showed that the grassland presented a meliorated status at various degrees and the grassland situation improved obviously in local areas during 2004-2012 compared to the grassland degradation status in early time period of 1990-2004. The grassland degradation trend in counties of “Three-River Headwaters” region had been controlled during 2004-2012. Slight and obvious improvement dominated in all counties while occurring and intensified degradation took place in few counties. Compared with the degradation grassland area in 2004, the degradation grassland area in 2012 decreased by 5.78%, among which moderate degradation grassland decreased most obviously by 5.35%. There was a severe degradation in the source region of the Yellow River and the Yangtze River, including Maduo, Qumarlêb, southern Chindu, and southwestern Zhidoi. Analysis of the grassland restoration trend and degradation status in the “Three-River Headwaters” region after the implementation of ecological projects can not only summarize successful experience and lessons of the first-stage project, but also provide rational guidance on the implementation of a second-stage ecological project.

Taking Xichuan in the core water source area of the middle route of south-to-north water diversion project as a case, this study obtained land use data of Xichuan by interpreting TM(2004) and GF1(2014) image data and quantitatively analyzed land use change of Xichuan from 2004 to 2014. The effects of land use change on ecological environment in Xichuan during the study period were evaluated by using the model of ecological environment quality at regional scale. Moreover, the driving factors of the change in ecological environment quality in Xichuan were analyzed using the gray correlation method. The results showed that: during 2004 to 2014, the area of forest land, construction land and water increased, and the area of farmland decreased. Also, the evolution trend of forest land, woodland, shrub land and nursery garden was consistent with the overall evolution pattern of forest resources. However, suitable land for forest, non timber forest land, unwoodland showed a decreasing trend. In the spatial distribution, forest land was mainly distributed in the northern mountainous area, with the high altitude, the steep slope. Farmland and construction land were mostly distributed in southeast area, with the low altitude, the relatively gentle slope. In the 2004 and 2014, the ecological environment quality of Xichuan both showed obvious spatial difference, and showed the distribution trend of north high and south low. During the study period, the regional ecological environment quality index of Xichuan increased from 0.5443 to 0.6039, and the quality of ecological environment was improved. Moreover, the contribution of suitable land for forest and non timber forest land being converted into forest land, and returning farmland to forests to the improvement of regional ecological environment was the most greatest. The ecological environment in some areas was the negative development. The negative impact of predatory exploitation, extensive management and deforestation on the ecological environment was the most profound. During 2004 to 2014, the change of ecological environment quality in Xichuan was mainly driven by the policy and the resident' pursuit of maximizing the benefits.

Wetlands are an important part of the land resource types. The change of wetland landscape pattern is closely related to climate change and land use change. In order to obtain the status and characteristics of the change of Hanjiang River Basin wetland resources, we analyzed the characteristics of the landscape change of Hanjiang River Basin wetland during 2000-2010 and scientifically diagnosed the status of wetlands to protect wetland resources based on the monitoring data of remote sensing satellite in 2000, 2005 and 2010. We used pressure-state-response model to collect indicators of ecological health status of Hanjiang River Basin wetland from three different angles and obtained weighting factors of evaluation index using AHP. We finally quantitatively evaluated ecological health status of different wetland regions of Hanjiang River basin as a whole based on fuzzy hierarchy comprehensive analysis model. The results showed that: (1) During the ten years, total area of ??wetlands of the Hanjiang River basin decreased, but the variation intensity of wetlands of Hanjiang River basin slowed down over time. (2) The ecological health of wetlands of Hanjiang River Basin has significant spatial differences. The trend of ecological health status was healthy in the northwest and fragile in southeast. Based on the results of fuzzy hierarchy comprehensive evaluation model, ecological health of wetlands of upper reaches of Hanjiang River Basin was good while that of middle reaches of Hanjiang River Basin was sub-healthy and that of downstream of Hanjiang River Basin was fragile. The overall ecological health status of wetland landscapes of Hanjiang River Basin was sub-healthy.

Wuhan Metropolitan Area is one of the earliest comprehensive reform pilot area of resource-saving and environment-friendly society construction. It is also key areas of the Central China Development Strategy and the Yangtze River Economic Belt. Scientifically understanding temporal characteristics of land use change of Wuhan Metropolitan Area is of great significance for formulation and implementation of regional land use policy. Based on the land use data in 2000, 2005, 2010 and 2015 of Wuhan Metropolitan Area combined with GIS spatial analysis, mathematical statistics, single land use dynamic degree, transfer matrix and integrated land use dynamic degree methods of land use, we studied the general characteristics of land use change, the direction of change and regional differences in characteristics of Wuhan Metropolitan Area during 2000 to 2015, and analyzed the policy-driven factors of land use change. The results showed that: (1) for the general characteristics, during 2000 to 2015, cultivated land, woodland, grassland and unused land was diminishing while residential land and water area was increasing. (2) For the changes in direction, during 2000 to 2015, cultivated land and forest land transformed to residential land and water was the main feature. During 2000 to 2005, farmland and residential land transformed to water was the main feature. During 2005 to 2010, cultivated land transformed to residential land, water and forest land transformed to residential land were the main feature. During 2010 to 2015, cultivated land, woodland, grassland and water transformed to residential land was the main feature. (3) For spatial and temporal differences at regional scale, the largest dynamic degree of integrated land use concentrated in the central area of Wuhan Metropolitan Area. From the aspect of dynamics degree of single land use, cultivated land was concentrated in surrounding areas of Wuhan Metropolitan Area. Residential land was mainly located in central region of Wuhan Metropolitan Area. Water was concentrated in Xiantao City of Wuhan Metropolitan Area. Woodland was mainly located in Qianjiang city, Yunmeng County. The grass was mainly located in Yingshan County. (4) For the analysis of policy-driven factors, reforestation, urbanization, the rise of the Central Plains, two-oriented society, the development strategy of Yangtze River economic belt and other policies have important implications on land use change.

Human activities have significant impacts on ecosystems. As the most direct characterization of human activity, large-scale land use/cover change is used to analyze the impacts of human activities on ecosystems. Therefore, scientists have paid much attention on the classification and extraction methods of land use/cover products. It was suggested that GlobCover (2005/2006) product was precise enough for the scientific study. However, the product has some limitations. In order to improve the quality of this product, this study developed new method for mapping and monitoring national land cover information in Brazil. The new Brazilian land use/cover data in 2005 were developed by using human-computer interactive discrimination at per-cell level based on GlobCover (2005/2006) data and the combination of geographic knowledge and the major data source of Landsat TM/ETM images. The results indicated that data accuracy and cost-efficiency were both improved by the developed method. The classification accuracy was improved from 67.17% in the GlobCover to 93.39% in our new dataset. Kappa coefficient was also improved from 0.58 to 0.91. Evergreen broadleaf forest area in Brazil was the highest among all the land cover types, with an area ratio of 45.67%. Farmland/natural vegetation mosaic area followed with an area ratio of 19.19%. The third largest land cover type was closed shrub with an area ratio of 12.34%. Modification ratio of agricultural land/natural vegetation mosaic and shrub and grassland was the largest. Among them, the proportion of mixed pixels of land class decreased 3.54%, while shrub and grassland increased 3.81%. As a result, the new developed method was proved to be more efficient and accurate. It can be used for large-scale land use/cover classification and analysis in further study.

Ecological engineering such as Grain for Green Project have significant impacts on the structure of regional land use and ecosystem service functions. Based on the RUSLE model and RS & GIS spatial analysis methods, this study assessed the impacts of returning farmland on soil conservation function in the western region of Farming-pastoral Ecotone of Northern China (FPENC) during 2000-2015. The results showed that the total area of farmland in the FPENC decreased by 1663.83 km² from 2000 to 2015, which was mainly converted into forest land, grassland and construction land. The implementation of the Grain for Green Program was the main reason for farmland decrease, and the area of farmland converted into forest and grassland accounted for 66.93% of the total area of farmland reduction. The newly added farmland was mostly converted from grassland and unused land, and mainly concentrated in the northern and central regions. Besides, the soil conservation function had improved significantly in the western region of FPENC during the past 15 years, and the amount of soil conservation increased by 56.50×10⁴t, which mainly resulted from returning farmland to forest and grassland between 2005 and 2010. In addition, the increase in soil conservation caused by ecological restoration had obvious difference in different slope grades, but the increased soil conservation generally showed decrease trend with the slope increase. Nevertheless, in some areas of slope greater than 25 degrees implemented Grain for Green Project have high benefits of soil conservation. The steep slope (slope greater than 25 degrees) area is mostly a contiguous area of extreme poverty, where is the key area implemented by a new round of Grain for Green Project and poverty alleviation projects. This study about the impact mechanism of returning farmland on soil conservation function in western region of FPENC will provide quantitative scientific basis for the planning and construction of regional ecological protection and restoration projects.

Since the land economic density can only characterize the economic utilization efficiency of construction land, the lighting density of construction land is used to characterize the output efficiency of construction land. and the Kernel density analysis, ESDA and the SDE is used to analysis the spatial-temporal pattern of it. The results show that: (1) There is a strong correlation between nighttime lighting and construction land output. It is scientific to use the light density to characterize the output efficiency of construction land. (2)The efficiency is high in the east and low in the west. The output efficiency of construction land in each region continues to increase while the regional differences continue to increase. However, the average annual growth rate between regions is 0.56%, which is relatively balanced. (3)The kernel density curve indicating that the overall level of construction land output efficiency in China is low. and presents the trend converging to middle and low level club. (4) The overall Moran's I index of construction land output efficiency is positive, indicating that there is a positive spatial distribution characteristics of construction land output efficiency and the local spatial pattern changes little, there are stable and dynamic, strong and weak, and weak and strong. (5) The azimuth of construction land output efficiency is always between 72.420^° and 81.066^°, indicating the northeast-southwest direction is the main direction of the output of construction land, the main direction and the secondary direction of the construction land output benefit has dispersion.

Coastline is the boundary between land and ocean, and the coastline position determination is the important content of the coastal zone, island and reef surveying. Coastline is generally divided into the island coastline and the mainland coastline. Under the background of global warming and the influence of the natural environment and human's exploitation, the coastline has been in a state of changing. Grasping the type, location, changing process and the future trend of the coastline accurately has great significance for guiding the coastal aquaculture, coastal zone development, navigation and transportation. Therefore, accurately and quickly extracting coastline and real-time monitoring its changes has the vital significance. Remote sensing technique has the features of observing large area synchronously, timely, and economically, which makes it an excellent choice for coastline classification and extraction. Now, remote sensing methods used in coastline extraction mainly include optical remote sensing, microwave remote sensing and laser radar technology. Various methods have been presented by researchers all over the world in recent years. However, some methods focus on waterline extraction instead of the defined coastline extraction. So this paper give summarize of waterline extraction and coastline extraction separately. Beyond that, the noise-reduction methods applied in coastline extraction and the solution of the inconsistency of level data in tidal are also mentioned in this paper. Overall, the paper reviews the recent research progresses on coastline extraction by all kinds of ways at home and abroad through analyzing their advantages, disadvantages and adaptability, and introducing their applications in many fields. Finally, feasible suggestion of the future research is forecasted based on its existent insufficiency.

Human annotation is a massive labor cost for the training sample selection process when applying any kind of supervised learning algorithm for change detection based on high-resolution remotely sensed satellite images. It is limited and unreasonable to use just one single sort of classifier generated from a supervised algorithm to extract change information of variety from the time-series images both in completeness and accuracy, let alone the inevitable salt-and-pepper noise and tiny patches falsely detected which turn out to be ubiquitous in and out of geographical entities. To tackle with problems mentioned above, a change detection approach based on a new automatic training sample annotation strategy and an improved Adaboost ensemble learning algorithm was proposed. At first, the unsupervised change detection algorithm CVA was applied to generate a low-level change detection result as referencing labels for further annotation, then the low-level result was divided into several parts with different intervals to ensure the automatic acquisition of the evenly distributed training samples with confidence. Furthermore, decision stump, logistic regression and kNN were employed as the weak classifiers to construct a hybrid multi-classifiers ensemble system with the help of the improved Adaboost algorithm, which would effectively promote the classification accuracy and generalization capacity of weak classifiers by sufficiently mining and making use of the spatial information with potential values. Finally, the SLIC segmentation algorithm was implemented in the difference image, and the segmentation border information was combined with spatial contextual information to build up a dual-filter for spatial constraint aiming at decreasing the omission rate and the false alarm rate of the detection results. To verify the validity of the proposed method, we conducted experiments using two datasets of multispectral images collected by SPOT-5 and WorldView-2. Experimental results indicated that the proposed method would significantly lower the labor costs of training sample annotation and demonstrated superiority compared with four other methods in accuracy.

Land surface temperature is one of the important parameters of scientific research such as resource environment, climate change and terrestrial ecosystem. MODIS LST (Land Surface Temperature, LST) products are important data sources for land surface temperature related research. The land surface temperature information of MODIS LST products is lost in the cloud coverage area. Therefore, the land surface temperature estimation of cloud coverage areas has become a frontier research problem of thermal infrared remote sensing. In order to solve the problem of missing land surface temperature information in the cloud occlusion area of MODIS LST products. In this paper, the Qinling area is used as the research area and the experimental data of MOD11A2 from 2001 to 2017 is selected. In the traditional Inverse Distance Weighting (IDW), Regular Spline (SPLINE), Ordinary Kriging (OK) and Trend Surface (TREND) spatial interpolation method, the important influence factor of elevation is introduced. Through a large number of spatial interpolation experiments, the traditional spatial interpolation method is improved, and a MODIS LST spatial interpolation method based on DEM correction is formed. Analysis of spatial interpolation results indicates: （1） The spatial interpolation accuracy is from high to low: OK> SPLINE > IDW>TREND, and the accuracy of the OK, SPLINE, IDW, and TREND methods based on DEM correction is increased by about 0.38°C, 0.31°C, 0.32°C, and 0.78°C, respectively; (2) The accuracy of spatial interpolation results shows seasonal differences. The interpolation accuracy is higher in summer, July, and August, and the interpolation accuracy is the lowest in January. （3）The interpolation accuracy has a certain relationship with the cloud area. When the cloud coverage area is less than 1.1km², the interpolation error of the DEM+OK interpolation method is less than 0.55°C, and when the cloud coverage area is less than 3.1km², the spatial interpolation error is less than 1°C. When the cloud coverage area is less than 2.7 km², the interpolation error of the DEM+SPLINE method is less than 0.55°C, and the interpolation error of the DEM+SPLINE method is less than 1°C when the cloud coverage area is less than 10.4 km². When the cloud coverage is 1.1~2.7 km², the interpolation accuracy of DEM+SPLINE interpolation method is higher than of the DEM+OK interpolation method.

Since Urban forests played important roles in improving air, water and land quality, absorbing and mitigating carbon dioxide and many pollutants, mitigating urban heat island and reducing storm water runoff, its monitoring is a major issue for urban planners. It is of great significance to obtain the tree species timely and precisely in urban planning and green space management. At present, urban forest tree species mapping has benefitted from advances in remote sensing techniques. Using an object-oriented method combing spectral, textural, indicial and geometric features from high-resolution WorldView-2 imagery, this paper aimed to carry out the classification of seven main tree species in Fuzhou university, including Banyan (Ficus microcarpa), Mango(Mangifera indica L.), Camphor tree (Cinnamomum camphora), Bishop wood (Bischofia polycarpa), Chinese orchid tree(Bauhinia purpurea L.), Weeping fig (Ficus benjamina L.), and Kapok tree (Bombax malabaricum DC.). A random forest method was employed to determine the feature selection in this study. When eliminating 20 percent of the total features, the in situ validation results showed that the overall accuracy reached a highest value of 74.95% with Kappa coefficient of 0.67 when using 34 features for classification, which including 15 spectral features, 6 textural features, 8 indicial features and 5 geometric features, and the feature of mean spectral was the most significant, however, the standard deviation of each band is less important. The results also revealed that the feature selection of random forest could reduce or avoid the data redundancy and Hughes phenomenon, and thus could improve the classification accuracy of same type tree species. Moreover, the four additional bands of WorldView-2 imagery, especially the yellow and red edge band, and their composite indexes showed a higher importance in classification, which also indicates that these bands have great application prospects in vegetation remote sensing, especially in tree species classification.

The rapid urban expansion has induced and aggravated the urban heat island phenomenon, which makes it a big challenge for human health and human survival environment. Research is needed to explore the impacts of urban form on the surface urban heat island. Taking 13 mega cities in China as the study area, this study mainly focuses on the relationship between urban forms and urban heat islands beside the traditional impact factors of surface urban heat islands. Using the MODIS land surface temperature products of the daytime and nighttime in summer 2015 (including June, July and August), along with the land cover, population, demographic and meteorological data of these 13 cities, the relationship between urban heat island and four factors, i.e. land covers composition, spatial configuration of land covers, population and location, were explored. Furthermore, the urban heat island intensity (UHII) index was employed to evaluate the urban heat island effect, which represents the mean LST difference between the urban region and the rural region. The results indicate that the urban heat island effect varies considerably among the 13 mega cities, showing a higher mean UHII in the daytime than that in the nighttime. The factors controlling annual mean daytime UHII are the area ratio of built-up area, the area ratio of forest, the mean patch area of built-up area, the mean patch area of forest, aggregation index of built-up area and population density. The nighttime UHII is significantly influenced by the mean patch area of built-up area, the mean patch area of forest, aggregation index of built-up area and the patch density of forest. Increasing the built-up area and the forest area will both increase UHII. Measures to mitigate the urban heat island include decreasing the built-up area or increasing green urban areas. Moreover, the urban heat island effect can be mitigated by altering the form of cities, such as, reducing the mean patch area of built-up area or reducing patch aggregation.

This paper presented a method of monthly net primary production (NPP) estimation of grassland in the Three-River Headwater Region (TRH) based on GF-1/WFV data. First, a preprocessing of radiometric calibration and atmospheric correction is applied on GF-1/WFV 1A data by ENVI software. Secondly, geocoding is processed by Rational Function Model (RFM) with GF-1/WFV RPC (Rational Polynomial Coefficient) and the orthophoto images with high georeferenced accuracy are conducted after block adjustment. The processed GF-1/WFV data is comparable in space and time. Then, cloud and cloud shadow per scene are detected using Multi-feature Combined method; NDVI is retrieved based on GF-1/WFV image and monthly NDVI is generated by Maximum Value Composite (MVC) method. The values of pixels still affected by cloud or cloud shadow cover in monthly NDVI mosaic are extrapolated using linear regression using least square method based on MODIS 13Q1 NDVI. Finally monthly NPP of grassland is calculated based on Carnegie-Ames-Stanford Approach (CASA) with monthly NDVI and other variables including monthly total precipitation, monthly averaged temperature and monthly total solar radiation. A case study was conducted in Maduo country and results showed that: (1) reliable monthly NDVI data at medium spatial resolution can be obtained based on GF-1/WFV under the support of MODIS 13Q1 product; (2) The accuracy of estimated grassland NPP based on GF-1/WFV was over 70% based on field data validation, which is better than MODIS 17A3 NPP production and the former can occupied more detailed NPP spatial variation. Monthly NPP can be successfully estimated based on GF-1/WFV under the support of MODIS 13Q1 product in TRH. However, some details need to be improved for further study: (1) more area of cloud and cloud shadow in images, lower precision of the extrapolated NDVI and the error of simulated NPP may be greater; (2) in low temperature, NPP is 0 in CASA, which overestimates the grassland NPP because underground root of grassland is still alive in TRH in winter and NPP should be negative; (3) monthly NDVI generated by MVC represents the best growth situation of vegetation in the period, not the average one, which may overestimates NPP. Besides, mapping accuracy of vegetation type will also affect the simulated NPP result precision; (4) field data collection is difficulty due to the study area is in remote area of high altitude, so the current ground data is not enough to cover all months in growth season and the uncertainty of this method remains to be further tested.

With the improvement of Beijing rail transportation, the subway has become an important transportation for people's daily travel. Monitoring and controlling land subsidence along metro line become an important basic work to ensure the normal operation of linear engineering. Based on 55 images of the 3m high-resolution TerraSAR-X data covering Beijing, this paper used multi-temporal InSAR analysis technology to obtain the ground settlement deformation information along the subway network from April 2010 to December 2016, and analyzed the spatial-temporal evolution of the ground settlement along the Beijing subway network. The spatial-temporal ground subsidence related to the construction of subway tunnels is usually modeled by peck formula in the space domain, which is used to calculate the ground surface settlement and determine the maximum settlement value of the settlement trough curve and the settlement trough width. Taking Ciqikou-Guangqumen station section as an example, the InSAR results were modeled by Peck formula, we estimated the spatial development characteristics of the ground subsidence trough. The results show that there are different degrees of the deformation along Beijing subway line, and the serious deformation is mainly concentrated in the eastern and northeastern regions, annual maximum subsidence rate greater than 100mm/a. Compared with other lines, the overall situation of lines 4 and 10 is relatively stable, followed by lines 14 and Yizhuang, and the uneven settlement of lines 6 and 7 is the most serious. In addition, the road sections show different deformation characteristics in different construction periods, and the settlement of the construction period is more serious than that of the operation period. The width and maximum settlement value of the settlement trough between Ciqikou and Guangqumen station (line 7) show an increasing trend from 2010 to 2016, the maximum settlement trough width reaches about 180 m.

As one of the climate change risk which profoundly affected the natural environment and human society, heat waves has aroused increasing attentions all around the world. Based on VSD （The Vulnerability Scoping Diagram), the evaluation index system of t heat waves risk is constructed. With the method of exploratory spatial data analysis, the spatial-temporal characteristic, hot spots evolution and spatial differentiation of heat waves risk in Fujian Province from 2000 to 2015 are carefully examined. The results show that: ① The index of heat waves risk is decreasing in Fujian Province, at the same time, the internal transitions among different risk levels are obvious . ② The spatial distribution of heat waves risk in Fujian Province has the “layer structure”, and the risk index varies from central to peripheral areas as characteristic of “low-high-low”. ③ The spatial agglomeration degree of heat waves risk decreases. The hot spots presents a tendency of shrinking, from “multi-core” to “dual core”, while the cold spots presents a tendency of stabilizing in the northeast. ④ The heat waves risk in Fujian Province can be divided into 5 types, including high risk area of capital-deputy provincial area, sub-high risk area of prefecture-level city district, medium risk area of river valley, sub-low risk area of coastal plain, and low risk area of eastern-inland mountainous area. This result is the prejudgment of spatial evolvement of heat waves risk of Fujian Province in the future. It also can provide references for risk management and public service facilities .

The monitoring of vegetation cover change is the basis of regional resource and environmental bearing capacity research. This paper estimates the vegetation of Yunnan Province from 2001 to 2016 by calculating the MODIS-NDVI vegetation index from 2001 to 2016, supplemented by trend analysis, and coefficient of variation. Next, the spatial and temporal variation characteristics of vegetation coverage and its distribution relationship with topographic factors are discussed in depth. Results are shown as follows: ① From 2001 to 2016, the vegetation coverage in Yunnan shows a significant increase, with a growth rate of 4.992%/10 a.② Spatially, the spatial pattern of vegetation coverage appears to be gradually decreasing from the south to the north and from the west to the east. The vegetation coverage is highest in the west and southwestern Yunnan and the lowest in the northwestern Yunnan. The stability of the vegetation coverage is characterized by increasing volatility from southwest to northeast; the increase of vegetation coverage in northeastern Yunnan was significantly better than other areas. The study region of the vegetation coverage change trend which was increased, basically stable and decreased, accounting for 49.53%, 43.76% and 6.71%, respectively.③ The area transfer matrix results of vegetation coverage in the three periods from 2001-2006, 2006-2011, and 2011-2016 all showed that the vegetation cover evolution area was larger than the degraded area, and the ratios of the two were 1.42, 1.63, and 2.0. It indicates that the vegetation coverage shows a continuous improvement trend in the study area. ④ The relationship between vegetation coverage and topographic factors in Yunnan Province shows that the average vegetation coverage increases first, then decreases, then increases, and then decreases with the increase in altitude; it increases first and then decreases with increasing slope; Changes have gradually decreased from north to south.