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.
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.
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.
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.
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.
