Changes In Land Cover Types Research Articles

TREND ANALYSIS OF TEMPORAL VARIATIONS IN EVI WITH RESPECT TO RAINFALL OF JAIPUR DISTRICT

For the protection and restoration of vegetation, it is imperative to comprehend and map current states of its distribution. Vegetation mapping and its quantification helps in understanding and correlating the factors that contribute to its spread cover over different regions. ‘Jaipur city’, also called Pink city is the capital of the state of Rajasthan lying in the semi-arid zone. Irregular and intermittent rainfall and drastic changes in land use and land cover types highly influence vegetation cover in the region. In order to study influence of erratic patterns of rainfall on vegetation, this study analyses the trend followed by the PostMonsoonal vegetation pattern of Jaipur with respect to the monsoonal rainfall in the years 2003 to 2010 with help of remote sensing data. Freely available remotely sensed data of Enhanced Vegetation Index (EVI) from MODIS and rainfall data from water resources department are processed here to exhibit the dependence of natural vegetation distribution in arid and semi-arid regions on rainfall. It is concluded that the EVI value rises with rise in rainfall. Coefficient of correlation value of 0.138 suggests that other factors may also be responsible for variations in EVI than rainfall. It is also concluded that MODIS EVI gives good estimation of seasonal patterns.

Мониторинг современного состояния лесов по материалам дистанционного зондирования и ILWIS ГИС

This paper focuses on the assessment of the changes in land cover types in Yamal peninsula over the past two decades, which shows local environmental aspects of the climate change in North. Technically, the data processing was per- formed in ILWIS GIS, using semi-automated methods of image interpretation and supervised classification. The results of the classification analysis enable to identify changes in land cover types and their distribution in Yamal.

A flexible spatiotemporal method for fusing satellite images with different resolutions

Abstract Studies of land surface dynamics in heterogeneous landscapes often require remote sensing data with high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial resolution images through blending two types of data, i.e., frequent coarse spatial resolution data, such as that from MODIS, and less frequent high spatial resolution data such as that from Landsat. The proposed method is based on spectral unmixing analysis and a thin plate spline interpolator. Compared with existing spatiotemporal data fusion methods, it has the following strengths: (1) it needs minimum input data; (2) it is suitable for heterogeneous landscapes; and (3) it can predict both gradual change and land cover type change. Simulated data and real satellite images were used to test the performance of the proposed method. Its performance was compared with two very popular methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an unmixing-based data fusion (UBDF) method. Results show that the new method creates more accurate fused images and keeps more spatial detail than STARFM and UBDF. More importantly, it closely captures reflectance changes caused by land cover conversions, which is a big issue with current spatiotemporal data fusion methods. Because the proposed method uses simple principles and needs only one fine-resolution image as input, it has the potential to increase the availability of high-resolution time-series data that can support studies of rapid land surface dynamics.

Spatiotemporal Data Fusion Using Temporal High-Pass Modulation and Edge Primitives

Most spaceborne sensors have a tradeoff between high spatial and high temporal resolutions. This tradeoff limits the use of remote sensing data in various applications that require images in both the high spatial and high temporal resolutions. In this paper, we propose a novel technique to create a fine spatial and high temporal resolution images at a ground-based data processing system. Resourcesat-2 is one of the Indian Space Research Organization missions, and it carries the Linear Imaging Self-Scanning sensors (LISS III and LISS IV) and an Advanced Wide-Field Sensor (AWiFS) onboard. The spatial resolution of LISS III is 23.5 m, and that of AWiFS is 56 m. The temporal resolution of LISS III is 24 days, and that of AWiFS is five days. The proposed method creates a synthetic LISS-III image at 23.5-m spatial and five-day temporal resolutions. It is based on the subpixel relationship between a single AWiFS–LISS-III image pair, which is acquired before or after the prediction date. In temporal data composition, spurious spatial discontinuities are inevitable for land-cover type changes. These discontinuities were identified with temporal edge primitives and were smoothed with a spatial-profile-averaging method. A synthetic LISS-III image for time $t_{k}$ is predicted from an AWiFS image at time $t_{k}$ and a single AWiFS–LISS-III image pair at time $t_{0}$ , where $t_{0}\ne t_{k}$ . Experimental results demonstrated that the proposed method is superior in terms of the computational efficiency and prediction accuracy with the other existing methods.

Research note: Sankey diagrams for visualizing land cover dynamics

Comparison of categorical maps from two or more points in time is a common technique to detect land cover and land use change. Cross-tabulation matrices, which contain information on the sizes of categorical differences between two maps, are often used to describe the amount and type of land cover change that has occurred between two points in time. However, the use of multiple matrices to describe changes occurring over more than one time interval can be difficult to interpret. This article presents a graphical method for presenting the land cover information contained in one or more cross-tabulation matrices based on Sankey diagrams, which depict the flow of energy or materials through a network. Through the example of a series of land cover maps of the San Juan, Puerto Rico area (1999–2003), this form of Sankey diagram is demonstrated to efficiently and elegantly present information on land cover persistence and change over multiple time intervals.

The Study of Impact of Urbanization on Urban Heat Island with Temperature Variation Analysis of MODIS Data Using Remote Sensing and GIS Technology

Udupi and its surroundings are experiencing unprecedented urbanization in recent times due to concentrated developmental activities with impetus on industrialization for the economic development of the region. This concentrated growth has resulted in the increase in population and consequent pressure on infrastructure, natural resources and ultimately giving rise to increase in built up area which causes a plethora of serious challenges such as climate change, urban heat Island effects, etc. Prior to urban heat island effect study, we must have knowledge about urbanization growth process along with land use/land cover changes. The study of rate of urbanization growth gives the primary idea about the ineffective infrastructure planning and resource management in advance. The local temperature is one of the major climatic elements to record the changes in the atmospheric environment brought about by industrialization, increasing population and massive urbanization. The current warming trends in temperature around Udupi and its surroundings based on the analysis of satellite thermal imagery could be utilized to infer the development process in this region. The urbanization pattern within the study area is reflected by the trends of increasing temperature which results in the urban heat island effect and also this research focuses to analyze the contribution of the built up area growth due to change in land cover types of Udupi Taluk during the years 2000, 2006 and 2010 to the urban heat Island using GIS technology 2010 and also an attempt to locate the spatial extent of urban heat island areas using the temperature distribution maps using MODIS satellite data of three years 2000 (March 29), 2006 (March 29) and 2012 (March 30) processed using IDRISI Software developed by Clarks University Lab.

A three-component method for timely detection of land cover changes using polarimetric SAR images

This study proposes a new three-component method for timely detection of land cover changes using polarimetric synthetic aperture radar (PolSAR) images. The three components are object-oriented image analysis (OOIA), change vector analysis (CVA), and post-classification comparison (PCC). First, two PolSAR images acquired over the same area at different dates are segmented hierarchically to delineate land parcels (image objects). Then, parcel-based CVA is performed with the coherency matrices of the PolSAR data to detect changed parcels. Finally, PCC based on a parcel-based classification algorithm integrating polarimetric decomposition, decision tree algorithms, and support vector machines is used to determine the type of change for the changed parcels. Compared with conventional PCC based on the widely used Wishart supervised classification, the three-component method achieves much higher accuracy for land cover change detection with PolSAR images. The contribution of each component is evaluated by excluding it from the method. The integration of OOIA in the method greatly reduces the false alarms caused by speckle noise in PolSAR images as well as improves the accuracy of PolSAR image classification. CVA contributes to the method by significantly reducing the effect of the classification errors on the change detection. The use of PCC in the method not only identifies different types of land cover change but also reduces the false alarms introduced by the change in the environment. The three-component method is validated in land development detection, which is important to many developing countries that are confronting a growing problem of unauthorized construction land expansion. The results show that the three-component method is effective in detecting land developments with PolSAR images.

Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment

Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins.

English

Suitable location and influx of immigrants have made pastoral peri-urban Kajiado County a centre of two conflicting interests; urban sprawl and agricultural intensification.  To understand the resulting trends over the period 1984-2010 satellite image based analysis of land use cover changes and interviews with landowners were conducted. Interviews focused on the changes and trends in land use, their causes and future forecasts. Percentage changes in land use and land cover types for the years 1984 to 2004, 2004 to 2010 and 1984 to 2010 were determined. Between 1984 and 2010, significant (p < 0.05) changes occurred in built-up, crop land, rangelands, bare ground, rocky areas and woodlots and riverine vegetation. Only water bodies showed no significant changes. Land use and cover changes resulted from activities due to human population growth and agricultural expansion which in turn resulted in increase in riverine vegetation and woodlots. The interviews revealed that selling of land contributed to land use change depending on the intended use of the buyers who were mainly influenced by the physical location with respect to distance to Nairobi, urban centres, electricity supply and road networks. As per the findings of the study, the present scenario demands a revision in the zoning policy using the land use land cover maps produced, involvement of all stake holders and revision of implementation strategies given that the 2008 land use master plan for the area was not actualized.   Key words: Pastoral peri-urban, immigrants, land use change, intended land use, Kajiado County.

Satellite Earth observation data to identify anthropogenic pressures in selected protected areas

Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.

Temporal changes in land cover types and the incidence of malaria in Mangalore, India.

Malaria contributes to 881000 deaths worldwide annually and India is a major contributor in the region. This study aimed at detecting land cover changes and assesses their relationship with the burden of malaria in Mangalore taluk of southern India. Landsat TM images were obtained from the U.S. Geological Survey data repository. The statistics for the malaria incidences in the region were obtained from the National Vector Borne Diseases Control Program division of the State of Karnataka. The images were preprocessed, classified and change detection statistics were employed for major land cover types. An increase in the urban land cover by 20% with a reduction in the mountainous terrain by 34.7% and vegetation by 38.7% was noted between the years 2003 and 2005. The annual incidence of malaria increased five-fold from 203 to 1035/100000 population during the period. This study demonstrates the application of publicly available remote sensed data as a cost effective approach to study the agent, host and environment relationships in resource scarce settings which would provide valuable information planning and policy making at regional levels.

Radiative forcing over China due to albedo change caused by land cover change during 1990–2010

Land cover change affects surface radiation budget and energy balance by changing surface albedo and further impacts the regional and global climate. In this article, high spatial and temporal resolution satellite products were used to analyze the driving mechanism for surface albedo change caused by land cover change during 1990–2010. In addition, the annual-scale radiative forcing caused by surface albedo changes in China’s 50 ecological regions were calculated to reveal the biophysical mechanisms of land cover change affecting climate change at regional scale. Our results showed that the national land cover changes were mainly caused by land reclamation, grassland desertification and urbanization in past 20 years, which were almost induced by anthropogenic activities. Grassland and forest area decreased by 0.60% and 0.11%, respectively. The area of urban and farmland increased by 0.60% and 0.19%, respectively. The mean radiative forcing caused by land cover changes during 1990–2010 was 0.062 W/m2 in China, indicating a warming climate effect. However, spatial heterogeneity of radiative forcing was huge among different ecological regions. Farmland conversing to urban construction land, the main type of land cover change for the urban and suburban agricultural ecological region in Beijing-Tianjin-Tangshan region, caused an albedo reduction by 0.00456 and a maximum positive radiative forcing of 0.863 W/m2, which was presented as warming climate effects. Grassland and forest conversing to farmland, the main type of land cover change for the temperate humid agricultural and wetland ecological region in Sanjiang Plain, caused an albedo increase by 0.00152 and a maximum negative radiative forcing of 0.184 W/m2, implying cooling climate effects.

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.

A robust anomaly based change detection method for time-series remote sensing images

Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.

Spatially varying relationships between land-cover change and driving factors at multiple sampling scales

Modeling the relationships between environment, human activity, and natural conditions is very important for understanding human–environment interactions. This study aims at examining how these relationships vary over spatial sampling scales and investigating the spatially varying relationships between land-cover changes and driving factors, as well as the variations in the relationships at different sampling scales in the Tibetan Autonomous Prefecture of Qinghai Province, P.R. China. Regular sampling methods are used first to generate eight sets of data points at different scales, and then the values for land-cover changes and the factors are extracted for these data points. Geographically weighted regression (GWR) model is applied to analyze the relationships between land-cover changes and the factors at different sampling scales. The results indicate that the influences of the factors (e.g. the signs, significances, and values of coefficients) change greatly over different sampling scales; similarly, for different types of land-cover changes, the contributions of the factors also vary. Generally, roads, rivers, and lakes contribute greatly to land-cover changes, while villages, temples, and elevations contribute less. A possible reason is that the densities of roads, rivers, and lakes is much greater than those of villages and temples, and the populations in temples and villages are too small to have much effect on land-cover changes. The results demonstrate that the sampling scales have an important influence on the relationships between land-cover change and the factors.

Distance metric-based forest cover change detection using MODIS time series

More than 12 years of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). As this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250–500m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel's annual time series to annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer's accuracy of 85%. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of deforestation within a MODIS pixel increases, but that relatively small changes in forest cover were still detectable from MODIS. Annually, over 80% of pixels with >20% deforested area were correctly identified and the timing of change showed good agreement with reference data. Errors of commission were largely associated with pixels located at the edges of disturbance events and inadequate characterization of land cover changes unrelated to deforestation in the reference data. Although our case studies focused on forests, this method is not specific to detection of forest cover change and has the potential to be applied to other types of land cover change including urban and agricultural expansion and intensification.

Land Cover Dynamics in Beni Chougrane Mountains, North West of Algeria, Using Remote Sensing

Land cover change is the result of complex interactions between social and environmental systems, systems that evolve over time. While climate and biophysical phenomena have long been the main drivers of changes in land surfaces, the human is now behind most of the changes affecting terrestrial ecosystems. The main objective of this work is to show the characterization and monitoring of land cover change in semi-arid Mediterranean area. The changes in agro-forest area which is a land use mode in the mountains of Beni-Chougrane at local scale.We used Support Vector Machines method for classification of Landsat TM image, and change detection technique to analyze change of land cover types by comparing the satellite observations of Landsat TM from 1984 to 2009.Our analysis showed that proportion of forest cover decreased from 41% in 1984 to 14% in 2009 that from approximately 190 hectares/year and agriculture land from 18 % to 1.5 %. The results showed that all land cover and lad use area have experienced structural changes in it's globally, Intensive regression of woody natural vegetation imposed by fires and unsustainable use of resources, a remarkable decline in land occupied by agriculture. Suggesting an immediate response to a policy based on priorities for the preservation, protection, development and rational use of land areas.

1990-2011年三峡库区生态系统服务价值演变及驱动力

作为我国西南部重要的生态屏障和生态走廊，三峡库区具有重要的战略意义，其生态问题非常值得关注。研究以覆盖三峡库区的4期Landsat TM遥感影像为数据源，通过人机交互解译分别获得1990、1998、2006、2011年的土地利用/覆盖，参照中国陆地生态系统单位面积生态服务价值当量表，采用研究区单位面积产量与全国农田粮食单位面积产量的比值作为地区修订系数，计算库区生态系统单位面积生态服务价值当量表，同时利用生物量对林地的生态系统服务价值作进一步的修订。然后结合土地利用结构和生态敏感性指数，定量分析土地利用变化引起的生态服系统务价值变化及其驱动因子。结果显示：1990-2011年间，三峡库区生态系统服务价值主要由林地支撑（占76.75%），其次是水域、耕地（共23.19%），草地的贡献率最小（0.22%），总生态系统服务价值从1990年的479.55 亿元增加到2011年的680.83 亿元；各项服务功能价值中，食物生产功能价值下降，其他各项功能价值上升，以土壤形成与保护功能上升幅度最大，达36.61 亿元，其次是气体调节（33.10 亿元）；驱动力分析表明，库区生态系统服务价值变化的主要原因是人类活动，特别是自1998年实施退耕还林工程以来 ，各自然生态系统的面积发生较大变化，同时，自然生态系统的健康程度和社会政策对生态系统服务价值变化的影响也不忽可视。研究表明，加强林地、草地和水域等生态系统服价值高的土地利用/覆盖类型保护，是维持库区生态系统稳定性的有效措施。;As the most important ecological barrier and ecological corridor in Southwestern China, the Three Gorges Reservoir region was strategically important and significant. Since the 1990s, in company with the accomplishment of the Three Gorges hydro-junction project, a series of key national ecological and forestry projects have been initiated, including the protective forests along the Yangtze River, the natural forests and Resources conservation, the conversion of cropland to forest, the afforestation of the reservoir surrounding area and the afforestation of both banks of the Yangtze River. These have led to significant changes of land use and land cover (LULC) types and their spatial distribution. More than twenty years has been gone. However, it is unknown that how the accomplishment of the ecological projects affected the change of ecosystem service values and what were the effects of these projects on the protection of the Three Gorges Reservoir region. There is a strong need to assess the effectiveness of the projects, including the dynamics of LULC types and the induced changes of ecological service values. The objective of this study was to analyze the spatiotemporal characteristics of ecological service changes and investigate the factors that have driven the dynamics in the Three Gorges Reservoir region. In this study, the Landsat Thematic Mapper^TM images that cover the Three Gorges Reservoir region were acquired for years of 1990, 1998, 2006 and 2011, respectively, and visually interpreted for LULC types. The per unit ecosystem service equivalent value of the ecosystems within the region was calculated by using the corresponding value of the terrestrial ecosystems for the whole country as a reference and the ratio of the per unit grain productivity of the study area to the corresponding value of the country as a revised coefficient. The obtained per unit ecosystem service equivalent value was further modified based on the value of biomass from forested land related to ecological service value. The changes of ecological service values induced by LULC dynamics and corresponding driving factors were quantitatively analyzed by combining land use structure index and ecological sensitivity index. The results showed that (1) during the past twenty one years, the ecological service values of the Three Gorges Reservoir region were mainly supported by the forested land with a percentage of 76.75%, followed by the water and cropland land (23.19%), and the contribution of the grassland was the least (0.22%). The total ecological service value increased from 47.96 billion yuan in 1990 to 68.08 billion yuan in 2011; (2) All the ecological service values increased except food production. The value from soil formation and protection had the greatest increase with an increment of 3.66 billion yuan, followed by the gas regulation (3.31 billion yuan); (3) The analysis of driving factors showed that the changes of the ecological service values were mainly due to human activities, especially the great change of the area for each of the natural ecosystems since the conversion of cropland to forest started in 1998. Moreover, the effects of both the health of the nature ecological systems and the social policy on the changes of the ecological service values could not be ignored. The findings implied that enhancing the protection of LULC types that have higher ecosystem service values, including forested land, grassland, water bodies and so on, is an effective way to stabilize the ecosystems of the Three Gorges Reservoir region.

Monitoring dynamic changes of global land cover types: fluctuations of major lakes in China every 8 days during 2000–2010

Remote sensing images can be used to delineate variations in the area of lakes and to assess the influence of environmental changes and human activities. However, because lakes are dynamic, results obtained from individual images acquired on a single date are not representative and do not accurately reflect ongoing changes. In this study, we used 8-day moderate resolution imaging spectroradiometer (MODIS) composite data from 2000 to 2010 to map water surface changes over 629 lakes in China. We combined automatic extraction of training data and support vector machine classification to derive the spatial distribution of these large water bodies. The producer’s and user’s accuracies for MODIS images were 91.06 % and 89.81 %, respectively, when compared with interpretation results from 30 m resolution Landsat images taken on similar days. Area changes, variability, inundation intensity, and rainy seasons of the 629 lakes were analyzed based on this multi-temporal lake database. The total area of the 629 lakes increased over the study period, primarily as a result of the expansion of lake areas on the Tibetan Plateau. There were 12 lakes with a maximum area >1,000 km2, and six of these decreased in area from 2000 to 2010. The shrinkages of Poyang Lake and Dongting Lake were −54.76 and −25.08 km2/a, respectively. The area of lakes on Tibetan Plateau, in northern Xinjiang, northeastern Inner Mongolia, and northeastern China varied little, while lakes on the Yangtze Plain, in southern Inner Mongolia, and central Xinjiang fluctuated considerably. Inundation intensity increased for lakes on the Tibetan Plateau, in northern Xinjiang, Heilongjiang, and Jilin, while inundation extent decreased in central Xinjiang, southern Tibet, southern Inner Mongolia, Sichuan, and on the Yangtze Plain. This study is an attempt to develop high-frequency specific land cover maps to improve applicability of general land cover maps. The lake products serve as an important supplement to hydrologic data. The lake database enables the generation of new land surface process models, which could improve the precision of simulations, based on more accurate observations of dynamic lake systems.

Urban change detection with polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar data: a case study of Tai’an, China

Change detection in Tai’an city of eastern China using a pair of qual-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) data was studied. The procedures consisted of polarimetric features extraction, optimal polarimetric feature group selection, supervised classification, and result accuracy assessment. Feature extraction from PALSAR data was performed first, and then the polarimetric features were categorized into several groups. Polarimetric optimum index factor (POIF) and distance factor (DF) were selected to measure and evaluate the suitability of each feature group for urban change detection. The best group of features was identified including linear polarization correlation coefficient ( ρ HH–VV ), right–left (R–L) circular polarization correlation coefficient ( ρ RR–LL ), total power (TP), and cross-polarization isolation (XPI). Afterward, four difference images of the identified features extracted from the two PALSAR data were derived, respectively. Then, the random forest (RF) classifier was employed to perform a supervised classification of the four difference images. Three classes were quantified, including no-change, change from undeveloped area to developed area, and vice versa. The overall accuracy of change detection was about 84% and Cohen’s Kappa coefficient was 0.71. Consequently, satisfactory outcomes were obtained in the application of the polarimetric ALOS PALSAR data of moderate resolution in detecting urban land use and land cover type changes.