Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

A global land-cover validation data set,part I: fundamental design principles

A number of land-cover products, both global and regional, have been produced and more are forthcoming. Assessing their accuracy would be greatly facilitated by a global validation database of reference sites that allows for comparative assessments of uncertainty for multiple land-cover data sets. We propose a stratified random sampling design for collecting reference data. Because the global validation database is intended to be applicable to a variety of land-cover products, the stratification should be implemented independently of any specific map to facilitate general utility of the data. The stratification implemented is based on the Köppen climate/vegetation classification and population density. A map of the Köppen classification was manually edited and intersected by two layers of population density and a land water mask. A total of 21 strata were defined and an initial global sample of 500 reference sites was selected, with each site being a 5?×?5 km block. The decision of how to allocate the sample size to strata was informed by examining the distribution of the sample area of land cover for two global products resulting from different sample size allocations to the 21 strata. The initial global sample of 500 sites selected from the Köppen-based stratification indicates that these strata can be used effectively to distribute sample sites among rarer land-cover classes of the two global maps examined, although the strata were not constructed using these maps. This is the first article of two, with the second paper presenting details of how the sampling design can be readily augmented to increase the sample size in targeted strata for the purpose of increasing the sample sizes for rare classes of a particular map being evaluated.     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

基于3S技术的土地利用/土地覆盖变化野外采样框架设计 ——以东北地区黑龙江省为例

土地利用/土地覆盖变化是全球环境变化的重要组成部分,随着3S技术的不断成熟和发展,运用RS、GPS和GIS技术进行土地利用/土地覆盖变化研究已成为一种越来越成熟的方式和手段。从空间抽样模型理论出发,以我国黑龙江省为例,运用RS、GPS和GIS技术,通过对黑龙江省道路网、土地利用区划、土地利用/土地覆盖类型、土地利用/土地覆盖1 km×1 km格网数据等空间信息分布的综合考虑、分析,设计了土地利用/土地覆盖变化的综合野外采样框架。框架主要包括采样区的布设、采样路线和采样点的选择等。由于以多层空间信息为采样依据,经实践检验,该采样框架具有经济实用等优点。     

Using random walker for knowledge transfer in classifying multi-temporal VHR images

Acquiring land cover types from very high resolution (VHR) images is of great significance to many applications and has been intensively studied for many years. The difficulties in image classification and the high frequencies of remote sensing image acquisition make it urgent to develop efficient knowledge transfer approaches for understanding multi-temporal VHR images. This letter proposed a knowledge transfer approach that uses the label information of the existing VHR images to classify multi-temporal images. The approach was implemented in three steps: object-based change detection, knowledge transfer of label information, and random walker (RW) classification. The proposed approach was tested by two datasets with each having two temporal images acquired on the same geographical areas. The experimental results showed that the proposed approach outperformed the support vector machine (SVM) algorithm in classifying multi-temporal images and can reduce the influence of spectral confusions on image classification.     

Evaluation of sediment yield by using remote sensing and GIS: A case study from the Phulang Vagu watershed,Nizamabad District (AP), India

The primary objective of this paper is to identify soil erosion zones and to suggest appropriate measures for control of soil erosion using remote sensing, GIS and conventional technique in the Phulang Vagu watershed in the Sriramsagar catchment area of Andhra Pradesh. The digital imagery data of the study area is obtained from the IRS-IC (LISS-III) satellite whereas the toposheets and rainfall data of the study area were obtained from the Survey of India. Satellite images were interpreted to prepare land use/land cover maps by using ERDAS image processing system. Out of 725.983 km 2 of the study area, about 301.435 km 2 is wasteland which is identified as susceptible for soil erosion. Toposheets of the study area were used to prepare drainage and slope maps. Drainage pattern is mainly dendritic with a density of 1.26 km -1 and the stream slope is 0.00614. The arithmetric average method is used to find average annual rainfall. The above parameters were used to calculate the amount of soil erosion from the catchment area. It was found that 882.389 m 3 km -2 year -1 of soil is being eroded from the catchment area which is more than the value adopted in the design of Sriramsagar reservoir. Therefore soil conservation measures such as vegetative cover in the waste land are needed and 12 check dam sites have been proposed by superimposing drainage map and slope map in conjunction with land use/land cover map. With these soil conservation measures, the soil erosion could be kept within the design value of Sriramsagar reservoir.     

Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya)

The dynamics of savannah vegetation are still poorly understood. This study aims at analysing land cover changes over the past 20 years in the rangelands area of Narok District, Kenya. To analyse the impact of inter-annual climate variability and human activities on land cover modifications in the area, change detection techniques based on remote sensing data at different spatial and temporal resolutions were used. Coarse spatial, high temporal resolution NOAA (National Oceanic and Atmospheric Administration) data were analysed to investigate the role of inter-annual climate variations on the ecosystem. A combination of time contextual and spatial contextual change detection approaches was used on a set of three high spatial resolution Landsat images to map land cover modifications over the past 20 years. Both datasets are highly complementary in the detection of land cover dynamics. On the one hand, the coarse spatial resolution data detected areas that are sensitive to inter-annual climate fluctuations, but are not subjected to land cover conversion. On the other hand, the high spatial resolution data allowed the detection of land cover conversions or modifications between two consecutive dates that have a more permanent character and are independent of climate-induced fluctuations in surface attributes.     

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft, as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.     

Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km² the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.     

Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing

Two common approaches to estimate the area of land cover or land-cover change are to use a confusion matrix to adjust the area derived from pixel counting and to use a survey sampling regression estimator that takes advantage of auxiliary variables to improve the precision of the estimated area. These two seemingly divergent approaches to area estimation are encompassed within the general framework of model-assisted estimation. The theory and methods of model-assisted estimation can be applied to expand the options for area estimators and to extend these estimators to sampling designs beyond those currently in use. The objectives of area estimation and map accuracy assessment can be addressed by the same sample data. This suggests the need for research to identify or develop sampling designs that effectively and efficiently achieve this dual-purpose use of these data.     

Estimating burned area for Tropical Africa for the year 1990 with the NOAA-NASA Pathfinder AVHRR 8 km land dataset

The international scientific community recognizes the long-term monitoring of biomass burning as important for global climate change, vegetation disturbance and land cover change research on the Earth's surface. Although high spatial resolution satellite images may offer a more detailed view of land surfaces, their limited area coverage and temporal sampling have restricted their use to local research rather than global monitoring. Low spatial resolution images provide an invaluable source for the detection of burned areas in vegetation cover (scars) at global scale along time. However, the automated burned area mapping algorithm applicable at continental or global scale must be sufficiently robust to accommodate the global variation in burned scar signals. Here, the estimation of the percentage of a pixel area affected by a fire is crucial. In a first step, an empirical method is used which is based on a function between the change in Normalized Difference Vegetation Index (NDVI) and the surface area affected by fire. Next, a new statistical method, based on the Monte Carlo algorithm, is applied to compute probabilities of burned pixels percentages in different neighbourhood conditions.     

Object-based land cover change detection for cross-sensor images

Accurate and timely land cover change detection at regional and global scales is necessary for both natural resource management and global environmental change studies. Satellite remote sensing has been widely used in land cover change detection over the past three decades. The variety of satellites which have been launched for Earth Observation (EO) and the large volume of remotely sensed data archives acquired by different sensors provide a unique opportunity for land cover change detection. This article introduces an object-based land cover change detection approach for cross-sensor images. First, two images acquired by different sensors were stacked together and principal component analysis (PCA) was applied to the stacked data. Second, based on the Eigen values of the PCA transformation, six principal bands were selected for further image segmentation. Finally, a land cover change detection classification scheme was designed based on the land cover change patterns in the study area. An image–object classification was implemented to generate a land cover change map. The experiment was carried out using images acquired by Landsat 5 TM and IRS-P6 LISS3 over Daqing, China. The overall accuracy and kappa coefficient of the change map were 83.42% and 0.82, respectively. The results indicate that this is a promising approach to produce land cover change maps using cross-sensor images.     

基于遥感和GIS的东亚土地覆盖年际变化研究

土地覆盖的年际变化是以土地覆盖的宏观分布模式为基准，在外界驱动因子的作用下发生的年与年之间的变化，因此，为揭示东亚地区土地覆盖的年际变化特征，首先选取东亚地区时相一致的不同空间分辨率（1km和8km)的NDVI影像进行了非监督分类，并总结了东亚土地覆盖的宏观分布模式，然后以时间序列的8kmAVHRRNDVI数字影像为基础，应用跨平百分率分析方法生成每年5-9月距平百分率分级影像，并以该影像为基础分析总结了东亚土地覆盖的年际变化特征，结果显示，该方法及其揭示的现象比较客观地反映了东亚土地覆盖年际变化特征。     

An automated approach for segmenting and classifying a large sample of multi-date Landsat imagery for pan-tropical forest monitoring

The TREES-3 project of the Joint Research Centre aims at assessing tropical forest cover changes for the periods 1990-2000 and 2000-2010 using a sample-based approach. This paper refers to the 1990-2000 assessment. Extracts of Landsat satellite imagery (20 km × 20 km) are analyzed for these reference dates for more than 4000 sample sites distributed systematically across the tropical belt. For the processing and analysis of such a large amount of satellite imagery a robust methodological approach for forest mapping and change detection has been developed. This approach comprises two automated steps of multi-date image segmentation and object-based land cover classification (based on a supervised spectral library), followed by an intense phase of visual control and expert refinement. Image segmentation is done at two spatial scales, introducing the concept of a minimum mapping unit via the automated selection of a site-specific scale parameter. The automated segmentation of land cover polygons and the pre-classification of land cover types mainly aim at avoiding manual delineation and at reducing the efforts of visual interpretation of land cover to a reasonable level, making the analysis of 4000 sample sites feasible. The importance of visual control and correction can be perceived when comparing to the initial automatic classification result: about 20% of the polygon labels were changed through expert knowledge by visual interpretation. The component of visual refinement of the mapping results had also a notable impact on forest area and change estimates: for a set of sample sites in Southeast Asia (~ 90% of all sites of SE-Asia) the rate of change in tree cover (deforestation) was assessed at 0.9% and 1.6% before and after visual control, respectively.     

Landsat approaches to map agro-pastoral farming in the wetlands of southern Sudan

Machine-learning algorithms (MLA) are coming of age within satellite remote sensing (SRS). This study compares the performance of a number of MLAs with more traditional indices and algorithms to map annual agro-pastoralist farming activity in southern Sudan. Two Landsat images from the early dry season 2014 and 2015 were analysed thoroughly and evaluated by interpretation of farming cover from very high resolution (VHR) images on Google Earth (GE). Traditional SRS indices based upon red and near infrared (NIR) bands used for monitoring rangelands did not perform well for the wet rangeland conditions compared to the use of blue and shortwave infrared (SWIR) bands. The species distribution model programme, MaxEnt, was used to produce a continuous farming activity indices using only Landsat-derived variables. Compared to other SRS classification approaches, maximum entropy (MaxEnt) showed the best overall performance to map farming activity followed by classification tree analysis (CTA). Overall mapping agreement >95.0% was reached for most methodologies, with MaxEnt showing very high mapping agreement (≥98.5%) for both years. When the result of MaxEnt’s good performance is put together in a 2014–15 or a 1999–2002 change detection scenario, it corroborates ground reports on massive human abuses that have taken place in Unity state of southern Sudan.     

A satellite data set for tropical forest area change assessment

A database of largely cloud-free (less than 2.5% of all sites have more than 5% cloud cover), geo-referenced 20 km?×?20 km sample sites of 30 m resolution optical satellite imagery have been prepared for the 1990 and 2000 epochs. This spans the tropics with a systematic sample located at the degree confluence points of the geographic grid. The resulting 4016 sample pairs are to be used to measure changes in the area of forest cover between the two epochs. The primary data source was the National Aeronautics and Space Administration's (NASA's) global land survey (GLS) data sets. Visual screening of GLS images at all 4016 confluence points from each date identified 2868 suitable pairs where no better alternatives exist (71.6% of the sample). Better alternatives could be found for 26.6% of the sample, substituting cloudy or missing GLS data sets at one or the other epoch or both (GLS-1990 or GLS-2000). Gaps were filled from the United States Geological Survey (USGS) Landsat archives (1070 samples), data from other Landsat archives (53 samples) or with alternatives to Landsat, that is, 15 samples from Satellite Pour l'Observation de la Terre (SPOT). This increased the effective number of sample pairs to 3945 representing 98% of all target samples. No suitable image pairs were found for 71 confluence points, which were not randomly distributed, but mostly concentrated in the Congo basin, where around 15% of the region remains un-sampled. Variations in date of image acquisition and geometric fidelity are documented. Results highlight the importance of combining systematic data-processing schemes with targeted image acquisition and archiving strategies for global scale applications such as deforestation monitoring and shows that by replacing cloudy or missing GLS data with alternative imagery, the overall coverage of the sample sites within the ecological zones ‘Tropical rainforest’ and ‘Tropical mountain system’ can be improved by 16%.     

基于Landsat影像的ALEXI模型小流域验证

基于静止卫星数据开发的陆气相互作用模型(ALEXI模型)为地表能量平衡过程分析提供了大尺度空间拓展,为认识大尺度的陆气相互作用提供了新途径,已被应用于干旱监测、流域水文分析以及气候变化研究。使用高精度卫星分辨率获得的通量结果对ALEXI进行初步的验证与评估,选择下垫面类型复杂的小流域为研究区,以比较成熟的基于Landsat流域分析的SEBAL模型结果为验证源,对比分析同时期ALEXI模型的地表通量结果,研究发现ALEXI模型与SEBAL模型能够反映较一致的地表能量交换信息格局,统计分析能够得到较一致的结果。此外,由于模型自身的限制因素以及地表观测误差的影响,还需进一步开展定量对比验证方面的工作。     

单极化合成孔径雷达影像在土地利用分类中的潜力分析

从我国土地利用调查应用出发,为了解决我国多云多雨地区土地利用分类及遥感动态监测问题,以面向对象影像分割、分类软件--Definiens Developer作为处理平台,对中分辨率星载合成孔径雷达（SAR）（以ENVISAT ASAR和Radarsat-1为例）、高分辨率星载SAR（以TerraSAR-X为例）进行分类处理,分析了单极化星载中、高分辨率星载SAR在土地利用分类中的能力,并对该模式星载SAR在土地利用分类中的影像特征和可解析程度进行了小结。     

Land cover characterization and change detection for environmental monitoring of pan-Europe

Environmental studies need up-to-date and reliable information on land use and land cover. Such databases, which can be characterized by a high spatial accuracy and that can be updated easily, are currently not available for Europe as a whole. We investigated the applicability of satellite data for Pan-European Land Cover Monitoring (PELCOM). The main objective was to develop a method by which to obtain a 1 km spatial resolution pan-European land cover database that can be updated easily using National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) satellite data. The database will be used as input for environmental impact studies and climate research. The study takes full advantage of both multi-spectral and multi-temporal 1 km AVHRR data. The proposed methodology for land cover mapping has its limitations in monitoring changes due to the spatial resolution and the limited accuracy of AVHRR-derived land cover data. Therefore, a change detection technique based on the use of thematic fraction images highlights those areas where the proportions of the various land cover types have changed.     

Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images

In this landscape-scale study we explored the potential for multitemporal 10-day composite data from the Vegetation sensor to characterize land cover types, in combination with Landsat TM image and agricultural census data. The study area (175 km by 165 km) is located in eastern Jiangsu Province, China. The Normalized Difference Vegetation Index (NDVI ) and the Normalized Difference Water Index (NDWI ) were calculated for seven 10-day composite (VGT-S10) data from 11 March to 20 May 1999. Multi-temporal NDVI and NDWI were visually examined and used for unsupervised classification. The resultant VGT classification map at 1 km resolution was compared to the TM classification map derived from unsupervised classification of a Landsat 5 TM image acquired on 26 April 1996 at 30 m resolution to quantify percent fraction of cropland within a 1 km VGT pixel; resulting in a mean of 60% for pixels classified as cropland, and 47% for pixels classified as cropland/natural vegetation mosaic. The estimates of cropland area from VGT data and TM image were also aggregated to county-level, using an administrative county map, and then compared to the 1995 county-level agricultural census data. This landscape-scale analysis incorporated image classification (e.g. coarse-resolution VGT data, fineresolution TM data), statistical census data (e.g. county-level agricultural census data) and a geographical information system (e.g. an administrative county map), and demonstrated the potential of multi-temporal VGT data for mapping of croplands across various spatial scales from landscape to region. This analysis also illustrated some of the limitations of per-pixel classification at the 1 km resolution for a heterogeneous landscape.