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1.
This paper presents a land use and land cover (LULC) classification approach that accounts landscape heterogeneity. We addressed this challenge by subdividing the study area into more homogeneous segments using several biophysical and socio-economic factors as well as spectral information. This was followed by unsupervised clustering within each homogeneous segment and supervised class assignment. Two classification schemes differing in their level of detail were successfully applied to four landscape types of distinct LULC composition. The resulting LULC map fulfills two major requirements: (1) differentiation and identification of several LULC classes that are of interest at the local, regional, and national scales, and (2) high accuracy of classification. The approach overcomes commonly encountered difficulties of classifying second-level classes in large and heterogeneous landscapes. The output of the study responds to the need for comprehensive LULC data to support ecosystem assessment, policy formulation, and decision-making towards sustainable land resources management.  相似文献   

2.
In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km2 in 1990 to 3 794.9 km2 in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).  相似文献   

3.
Impact assessment of watershed development activity assumes greater importance in present day agriculture. Considering the ability of remote sensing technology in watershed monitoring and impact assessment, a study was carried out to investigate the Impact Assessment of Karnataka Watershed Development Project (DANIDA) in Koralahallihalla Sub watershed in Sindagi taluk of Bijapur district in Northern Karnataka using satellite data of two periods i.e., IRS 1?C, LISS-III data of 30 December, 1997 (pre-treatment) and IRS P6, LISS-III data of 17 December, 2004 (post-treatment). The land use/land cover map was derived from the supervised classification. The results revealed that there has been no major shift in cropping patterns over a period of 7?years (1997?C2004). However, rabi cropped area has decreased drastically (187?ha), which might be due to the continuous droughts that occurred during the implementation period. On the other hand, kharif and double cropped area have increased marginally (103?ha and 96?ha, respectively). Increase in double cropped area showed that there was increase in irrigated land, which were earlier being used as rainfed and wastelands turned in to cultivated lands as seen in scrub lands and rabi cropped areas of the sub watershed. Wastelands in the sub-watershed has decreased marginally (36?ha). The vegetation vigour of the sub-watershed has been derived from the NDVI maps of both the periods. These NDVI maps indicate that there was a significant change in biomass status of the sub watershed. The vegetation vigour of the area was classified into three classes using NDVI. Substantial increase in the area under high and low biomass levels was observed (319?ha and 77?ha, respectively). The benefit-cost analysis indicates that the use of remote sensing technology was 2 times cheaper than the conventional methods. Thus, the repetitive coverage of the satellite data provides an excellent opportunity to monitor the land resources and evaluate the land cover changes through comparison of images for the watershed at different periods.  相似文献   

4.
Remote classification of land-use/land-cover (LULC) types in Brazil's Cerrado ecoregion is necessary because knowledge of Cerrado LULC is incomplete, sources of inaccuracy are unknown, and high-resolution data are required for the validation of moderate-resolution LULC maps. The aim of this research is to discriminate between Cerrado and agriculture using high-resolution Landsat 7 ETM+ imagery for the western region of Bahia state in northeastern Brazil. The Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms were applied to a ～3000 km2 subset, yielding comparable classification accuracies. The panchromatic band was reserved for validation. User's and producer's accuracies were highest for non-irrigated agriculture (～94%) but lower for Cerrado Lato Sensu (89%). Classification errors likely resulted from spatial and spectral characteristics of particular classes (e.g. riparian forest and burned) and overestimation of other classes (e.g. Eucalyptus and water). Manual misinterpretation of validation data may have also led to lower reported classification accuracies.  相似文献   

5.
张东  张万昌 《国土资源遥感》2004,15(2):32-34,38
详细阐述了应用全国1km土地利用/覆盖25层分层栅格数据合并生成单层土地利用/覆盖图的方法。通过对初始合成结果的归类、合并和空间平滑,有效减少了图像上像元分布零散的情况,突出了土地利用的空间总体分布格局。具体应用表明,这种处理方法得到的土地利用/覆盖图能够满足水文、地学分析的要求。  相似文献   

6.
This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.  相似文献   

7.
The present study aimed at quantification of sediment yield for Ahar River basin of Udaipur district in Rajasthan, India by a regional empirical model using RS and GIS techniques. The land use/land cover (LULC) map of the study area was prepared by supervised classification using satellite imagery of IRS-P6 LISS III. Overall accuracy of the prepared LULC map was 90.78%. The major portion of the study area (49%) is covered with rangeland. Slope map for the study area was developed using digital elevation model. The slope in most of the study area (40% of the total area) ranges from 1% to 4%. In addition, drainage density map of the study area was generated on micro-watershed basis. The study area is covered by a dendritic pattern of drainage which shows that rocks in the area are homogeneous and uniformly resistant to water flow. The drainage density in the study area is 1.11 km km−2. Annual sediment yield of the study area was quantified by Garde model. The mean annual runoff and sediment yield for the area was 37.58 million m3 and 8,760 m3/year, respectively. Finally, appropriate sites for construction of soil conservation measures are suggested using Boolean logic analysis method on combined slope and drainage maps.  相似文献   

8.
Hyperion is a space borne sensor which provides powerful tool in discriminating land cover features including urban area and in preparation of urban maps. It gives hyperspectral images in 242 bands within 400?nm to 2,500?nm wavelength range with 10?nm band-width. The Hyperion image in raw form is badly affected with several atmospheric effects which cause haziness. In this study hyperspectral image is atmospherically corrected by using FLAASH model of ENVI. After atmospheric correction the urban area was mapped using the spectral endmember collected by the procedure which includes minimum noise fraction (MNF), pixel purity index (PPI) and n-dimensional visualization in ENVI software. The aim of this study is to map the urban area using several mapping techniques such as Spectral Angle Mapper (SAM), Mixture Tune Matched Filtering (MTMF) and Linear Spectral Unmixing. The urban land covers displayed noticeable differences from one another in the spectral responses in the Hyperion image. The overall accuracy of the SAM classified map was 89.41%, which indicated good potential of Hyperion image for Classification. Use of the other approaches, linear spectral unmixing and MTMF have improved the classification results.  相似文献   

9.
This paper investigates statistical relationships between land use/land cover (LULC), Landsat-7 ETM+ imagery and landscape mosaic structure in southern Cameroon where the conversion of tropical rain forest to shifting cultivation leads to dynamic processes, acting on the spatial aggregation of various LULC types. A Global Positioning System (GPS) was used in the field to identify a total of 171 shifting cultivation patches representing eight LULC types in two sub-areas. Because of the lack of a cloud-free image for the date of field sampling, the ETM+ imagery was acquired 2 months after field survey, during which it was assumed that no significant changes in LULC occurred (all dry season). Per pixel correlations were developed between spectral reflectance data, vegetation indices and LULC. As an exploratory study, several statistical methods (analysis of variance, means separations (Tukey HSD), principal component analysis (PCA), geo-statistical analysis, image classification and landscape metrics) were applied on point data and sensor images for evaluating the spatial variability within the landscape. Most variables explained 30–72% of LULC variation in the whole dataset. Those variables with high information content of LULC (infrared bands 4, 5, 7 and derived indices and PC1) also showed long ranges (6 km) spatial dependence as compared to those varying only within 1 km range. The results of these statistical analyses suggested the need to group some LULC types and the application of the Maximum Likelihood Classifier (MLC) for supervised classification provided a LULC map with the highest accuracy (81%) after consolidation of perennial LULC types, such as bush fallow, forest fallow and cocoa plantations. Landscape metrics computed from this map showed a high level of patch diversity and connectivity within the landscape and provided input data that can further be used to simulate predictive maps as substitute to cloud-covered sensor imageries. Landsat-7 ETM+ imagery proved to be useful in discriminating (with about 80% accuracy) the most dynamic LULC types such cropped plots and young fallow patches (shifting every season) and the extension front of the agricultural landscape.  相似文献   

10.
面向对象的城市土地利用分类   总被引：1，自引：0，他引：1  
利用面向对象的信息提取技术,以高分辨率的广州市QuckBird影像为例,将城市用地分为：居民地、水体、道路、林地和农业用地等5类,并将其与传统基于像素光谱信息的分类方法进行了比较。结果表明：视觉上,面向对象的分类方法克服了传统方法无法克服的“椒盐”噪声的影响;精度上,面向对象信息提取技术的总体精度高达89．53％,比传统方法提高了11％;并且各类地物信息的提取精度均有所提高,其中林地、道路的精度有了较大提高。  相似文献   

11.
ABSTRACT

Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extraction and Tehran metropolitan area in Iran is selected as the study area. Results show that NDVI, LULC, and altitude explained 86% of the ULST °C variation. Unexpectedly, lower LST is observed near the major roads, which was due to the presence of vegetation along the streets and highways in Tehran. The results also revealed that variation in the ULST was influenced by the interaction between altitude – NDVI, altitude – road, and LULC – altitude. This indicates that the individual examination of the underlying factors of the ULST variation might be unilluminating. Performance evaluation of the four models reveals a close performance in which their R2 and Root Mean Square Error (RMSE) fall between 60.6–62.1% and 2.56–2.60 °C, respectively. However, the difference between the models is not statistically significant. This study evaluated the predictive performance of several models for ULST simulation and enhanced our understanding of the spatial influence and interactions among the underlying driving forces of the ULST variations.  相似文献   

12.
为分析河北省张家口市在经过三北防护林三期建设后林地覆盖度变化情况,通过利用张家口2006,2010年两景同期TM影像数据,使用ERDAS软件首先提取植被指数(NDVI),根据像元二分法利用ERDAS的建模工具Spatial Modeler计算出该地区植被覆盖度,利用非监督分类方法对植被覆盖度进行分类、赋色,最后得出张家口市2006—2010年的植被覆盖度分类图,结果表明四年间该市植被覆盖面积增加698.44 km2,与第二次国家林业调查数据基本相符,说明利用遥感反演的方法能够快速、准确地获取该地区的植被覆盖度信息,以及利用NDVI监测植被覆盖度变化方法的可行性。  相似文献   

13.
In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.  相似文献   

14.
Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes.  相似文献   

15.
Abstract

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.  相似文献   

16.
Standard false colour composites (Std. FCC) on 1:50,000 scale was visually interpreted in conjunction with soil survey to prepare physiographic-soil map. Thirteen mapping units were delineated indicating soil association at family-level. Soil and land resource was evaluated for their land capability and irrigation suitability for its sustained use under irrigation. Land capability and land irrigability maps were generated as attribute map. These maps were integrated to suggest potential land use map. Current land use/land cover map prepared by visual analysis was spatially analysed in relation to potential land use to study potential changes in land use / land cover using GIS. The study reveals that 14.66% area has no limitation and can be brought to intensive agriculture by double cropping.  相似文献   

17.
In this study we explored the potential of open source data mining software support to classify freely available Landsat image. The study identified several major classes that can be distinguished using Landsat data of 30 m spatial resolution. Decision tree classification (DTC) using Waikato environment for knowledge analysis (WEKA), open source software is used to prepare land use land cover (LULC) map and the result is compared with supervised (maximum likelihood classifier – MLC) and unsupervised (Iterative self-organizing data analysis technique - ISODATA clustering) classification techniques. The accuracy assessment indicates highest accuracy of the map prepared using DTC with overall accuracy (OA) 92 % (kappa = 0.90) followed by MLC with OA 88 % (kappa = 0.84) and ISODATA OA 76 % (kappa = 0.69). Results indicate that data set with a good definition of training sites can produce LULC map having good overall accuracy using decision tree. The paper demonstrates utility of open source system for information extraction and importance of DTC algorithm.  相似文献   

18.
This study investigated rice cropping practices and rice growing areas in the Vietnamese Mekong Delta using MODIS 250 × 250 m normalized difference vegetation index (NDVI) data acquired during the 2002 and 2007 rice cropping seasons. Data processing was conducted in five main steps: (1) constructing time-series MODIS NDVI data; (2) noise filtering of the time-series MODIS NDVI data using empirical mode decomposition (EMD); (3) extracting and evaluating phenological rice training patterns from the smooth time profiles of NDVI; (4) classifying rice cropping systems using support vector machines (SVMs); and (5) conducting an error analysis using ground reference data and government rice statistics. The results indicated that EMD was an efficient filter for noise removal in the time-series MODIS NDVI data. The filtered temporal NDVI profile characterized the distinct behaviors of the rice cropping systems. The estimated sowing and harvesting dates were compared with the field-survey data and indicated root mean square error (RMSE) values of 7.5 and 8.2 days, respectively. The comparison results between the 2002 classification map and the ground reference data indicated that the overall accuracy for the 2002 data was 92.9% with a Kappa coefficient of 0.89, while in 2007 these values were 93.8% and 0.90, respectively. At the district level, there was good agreement between the MODIS-based estimated areas and government rice statistics for 2002 and 2007 (R 2 ≥ 0.85). An investigation of changes in cropping practices from 2002 to 2007 showed that 12.9% of the area used for double-cropped irrigated rice in 2002 had been converted to triple-cropped irrigated rice by 2007, whereas 27.4% of the area used for triple-cropped irrigated rice in 2002 had been converted to double-cropped irrigated rice by 2007.  相似文献   

19.
On the Caribbean island of Puerto Rico, forest, urban/built-up, and pasture lands have replaced most formerly cultivated lands. The extent and age distribution of each forest type that undergoes land development, however, is unknown. This study assembles a time series of four land cover maps for Puerto Rico. The time series includes two digitized paper maps of land cover in 1951 and 1978 that are based on photo interpretation. The other two maps are of forest type and land cover and are based on decision tree classification of Landsat image mosaics dated 1991 and 2000. With the map time series we quantify land-cover changes from 1951 to 2000; map forest age classes in 1991 and 2000; and quantify the forest that undergoes land development (urban development or surface mining) from 1991 to 2000 by forest type and age. This step relies on intersecting a map of land development from 1991 to 2000 (from the same satellite imagery) with the forest age and type maps. Land cover changes from 1991 to 2000 that continue prior trends include urban expansion and transition of sugar cane, pineapple, and other lowland agriculture to pasture. Forest recovery continues, but it has slowed. Emergent and forested wetland area increased between 1977 and 2000. Sun coffee cultivation appears to have increased slightly. Most of the forests cleared for land development, 55%, were young (1-13 yr). Only 13% of the developed forest was older (41-55+ yr). However, older forest on rugged karst lands that long ago reforested is vulnerable to land development if it is close to an urban center and unprotected.  相似文献   

20.
The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes.  相似文献   

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