遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文)

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).     

Habitat analysis for sambar in corbett national park using remote sensing and gis

Habitat analysis for sambar in terms of food, cover, water, space and extent of edge in Corbett National Park using remote sensing and GIS has been attempted. Other physical parameters include climate, topography, fire history, disturbance regimes, weeds etc. IRS-IB LISS II data (FCC, hardcopy) on 1:50.000 scale was interpreted to generate vegetation cover and density map. Other maps showing drainage, water bodies, roads, human habitations and contours were prepared using Survey of India topographical maps. During evaluation of sambar habitat information regarding habitat parameters and their tolerance was collected from existing literature as well as during field observations. Twenty-two transects of one km. length were laid down in all the strata randomly to collect information regarding the structure and composition of the forest and also habitat use (direct and indirect evidences) by sambar. This was then integrated using condition-based equations in the GIS domain to generate suitability maps. Actual sightings on the ground to a large extent supported the results.     

Geospatial approach for tiger habitat evaluation and distribution in Corbett Tiger reserve,India

India has a rich repository of flora and fauna, but the rapid decline of wildlife and threat to its habitat has been a serious cause of concern. Hence, protected areas have been set up to achieve specific conservation objectives to facilitate timely and reliable information on forest types and its composition, degradation status and their suitability for different species of flora and fauna. In the present study, evaluation of tiger habitat in Corbett Tiger reserve is carried out using remote sensing, ground and other ancillary sources and is integrated using GIS using multi-criteria model. The results indicated that sal, mixed sal, miscellaneous forest, plantation, grassland, agriculture and scrub land are the major land use/land cover types and majority of the study area is covered under dense forest. Tiger habitat suitability analysis showed that large proportion of the area (51.4%) was found to be highly suitable followed by moderately suitable area (31%). Further, the correlation drawn between range-wise suitability area and actual tiger population in Corbett Tiger Reserve CTR indicated a positive correlation of 0.73. Disturbance to wildlife habitat, vegetation degradation and shrinking passage corridor are the major concern in CTR.     

The assessment of potential land use in the proposed irrigation command using Remote Sensing and GIS

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.     

Estimating the Effects of Map Error on Habitat Delineation for the California Spotted Owl in Southern California

The reliability of habitat maps that have been generated using Geographic Information Systems (GIS) and image processing of remotely sensed data can be overestimated. Habitat suitability and spatially explicit population viability models are often based on these products without explicit knowledge of the effects of these mapping errors on model results. While research has considered errors in population modeling assumptions, there is no standardized method for measuring the effects of inaccuracies resulting from errors in landscape classification. Using landscape‐scale maps of existing vegetation developed for the USDA Forest Service in southern California from Landsat Thematic Mapper satellite data and GIS modeling, we performed a sensitivity analysis to estimate how mapping errors in vegetation type, forest canopy cover, and tree crown size might affect delineation of suitable habitat for the California spotted owl (Strix occidentalis occidentalis). The resulting simulated uncertainty maps showed an increase in the estimated area of suitable habitat types. Further analysis measuring the fragmentation of the additional patches showed that they were too small to be useful as habitat areas.     

Site Suitability Analysis for Urban Development Using GIS Based Multicriteria Evaluation Technique

Identification of suitable site for urban development in hilly areas is one of the critical issues of planning. Site suitability analysis has become inevitable for delineating appropriate site for various developmental initiatives, especially in the undulating terrain of the hills. The study illustrates the use of geographic information system (GIS) and multicriteria evaluation (MCE) technique for selection of suitable sites for urban development in Mussoorie municipal area, Dehradun district, Uttarakhand. For this purpose Toposheet and IKONOS satellite data were used to generate various thematic layers using ArcGIS software. Criteria using five parameters, i.e. slope, road proximity, land use/land cover, land values and geological formation were used for site suitability analysis following land evaluation. The generated thematic maps of these criteria were standardized using pairwise comparison matrix known as analytical hierarchy process (AHP). A weight for each criterion was generated by comparing them with each other according to their importance. With the help of these weights and criteria, final site suitability map was prepared.     

Land Cover and Vegetation Change in the Yellow River Delta Nature Reserve Analyzed with Landsat Thematic Mapper Data

Abstract

Coastal wetland is a major part of wetlands in the world. Land cover and vegetation mapping in a deltaic lowland environment is complicated by the rapid and significant changes of geomorphic forms. Remote sensing provides an important tool for coastal land cover classification and landscape analysis. The study site in this paper is the Yellow River Delta Nature Reserve (YRDNR) at the Yellow River mouth in Shangdong province, China. Yellow River Delta is one of the fastest growing deltas in the world. YRDNR was listed as a national level nature reserve in 1992. The objectives of this paper are two fold: to study the land cover status of YRDNR, and to examine the land cover change since it was declared as a nature reserve. Land cover and vegetation mapping in YRDNR was developed using multi‐spectral Landsat Thematic Mapper (TM) imagery acquired in 1995. Land cover and landscape characteristics were analyzed with the help of ancillary GIS. Land use investigation data in 1991 were used for comparison with Landsat classification map. Our results show that YRDNR has experienced significant landscape change and environmental improvement after 1992.     

黑龙江省呼玛至十八站公路路域生存环境质量评估

针对建设绿色公路不可避免地对路域生存环境质量造成影响的问题,本文选用Landsat 8卫星影像、ASTER GDEM 30 m分辨率的DEM数据、2020版30 m全球地表覆盖数据,利用ENVI 5.3和ArcGIS 10.2平台协同操作,从公路的路域植被覆盖度变化、土壤侵蚀度、生存环境适宜性3个角度分析对生存环境质量的影响。研究结果表明,公路建设前后路域植被覆盖度超75%区域从2 673.8 km²降至2 389.82 km²,植被覆盖度为45%~75%的区域从997.31 km²降至767.19 km²;土壤侵蚀程度以中度侵蚀和高度侵蚀为主,中度侵蚀和高度侵蚀总计面积3 927.99 km²,占比达79.04%;生存环境适宜性方面,公路左侧呼玛河流域及附近最高,公路右侧的林区大面积图斑块次之。     

Habitat evaluation for sarus crane in the Keoladeo National Park using IRS LISS III and pan merged data and GIS

The Keoladeo National Park, Bharatpur, a man-made fresh water wetland carved out of a natural depression on the floodplain of two minor tributaries of the Yamuna-Gambhir and the Banganga is the country’s finest waterfowl habitat. This important wetland was set aside as a bird sanctuary in 1956 and it was elevated to the status of a National Park in 1981. It was also designated a Ramsar site- a wetland of international importance under the Ramsar convention. This important wetland has distinction of being the only Indian wetland to be included under both the Ramsar and the World Heritage convention. The attempt has been made to evaluate the habitat of Sarus crane in the Keoladeo National Park using satellite data — IRS LISS III and PAN merged product and GIS. Geocoded data of IRS —1C LISS III of 21 March 1999 on 1: 50,000 scale and PAN data of March 17, 1999 were used to generate the vegetation cover type map and open water. The maps showing drainage, human habitations, contours, roads, etc. were prepared using the Survey of India topographical sheets and contour map of park area. Information regarding habitat parameters was collected from the existing literature and field observations. The Sarus crane mainly fed in the wetland on the rhizome ofNymphaea sp.,Scirpus tuberosus andEleocharis plantaginea. As there were changes in their habitat requirements at different seasons, the sighting of Sarus crane in each habitat were recorded along with the time and activity during observation. The most utilized habitat for the entire period of study was moderately wet grassland followed by pools. The pools were used mainly during the summer. The water depth requirement observed was between 30–40 cm and 20–40 cm. The suitability maps for Sarus crane were then generated using all remote sensing based and conventional information using rule based equations in the GIS within the Keoladeo National Park.     

Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model

Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model.     

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.     

Integrating RapidEye and ancillary data to map alpine habitats in South Tyrol,Italy

In this paper, we present a two-stage method for mapping habitats using Earth observation (EO) data in three Alpine sites in South Tyrol, Italy. The first stage of the method was the classification of land cover types using multi-temporal RapidEye images and support vector machines (SVMs). The second stage involved reclassification of the land cover types to habitat types following a rule-based spatial kernel. The highest accuracies in land cover classification were 95.1% overall accuracy, 0.94 kappa coefficient and 4.9% overall disagreement. These accuracies were obtained when the combination of images with topographic parameters and homogeneity texture was used. The habitat classification accuracies were rather moderate due to the broadly defined rules and possible inaccuracies in the reference map. Overall, our proposed methodology could be implemented to map cost-effectively alpine habitats over large areas and could be easily adapted to map other types of habitats.     

Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m²) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

Investigating the Promotion Possibility of No-Hunting Areas to the Protected Areas- a Case Study

Protected areas are established to conserve unique features and biodiversity of the nature. Accordingly, wherever has one of the natural, ecological and/or cultural values it should be considered a protected area. Kave-Deh No-hunting Area is located on extremely east of Tehran Province in an area of 94,961 ha. Due to rich and diverse land cover, distinctive wildlife species, and unique monuments the area was selected as a case study to examine the possibility of its promotion to the protected area using Spatial Multi Criteria Evaluation (SMCE) Method. For this purpose, the relevant criteria were identified by Delphi method. After finalization of the most important criteria by Delphi panelists, the map layers were prepared at the scale of 1:100000, in the environment of GIS Software. Afterwards, the map layers were divided into factors and constraints of which factors were standardized by S-shaped membership functions of fuzzy logic. The dimensionless factor maps were weighted using Analytical Hierarchy Process (AHP) Method in the environment of Expert Choice Software. Subsequently, a mathematical equation was extracted to conduct the land suitability analysis. The Weighted Linear Combination (WLC) Method was applied to overlay the map layers and obtain the final ‘nature conservation’ land use map. The final land suitability map showed that 34,687 ha of whole study area (equal to 37 %) have the potentiality for promotion to the protected area.     

Vegetation cover classification in Sariska National Park and surroundings

Appraisal of spatial distribution of vegetation types is an important aspect for wildlife habitat suitability and ecological studies. Remote sensing provides quick, accurate and cost and time effective methods for vegetation cover mapping. In the present study Landsat MSS data was digitally classified into various land use/forest type classes. Forested land was about 52 per cent of the study area. Four forest types, namelyAnogeissus pendula, Boswellia serra ta, mixedAnogeissus-Butea and mixed Acacia-Zizyphus occupied 28.47 percent, 6.60 percent, 18.60 percent and 9.70 percent of the forested land, respectively. The area under National Park was 51.28 percent of total study area. About 61 percent of the Park area was under tree-covered vegetation. Overall accuracies for classified and smoothened-classified images were 89.37 percent and 91.96 percent, respectively. The vegetation of the area is controlled by topography and edaphic factors.     

Land suitability analysis for cereal production in Himachal Pradesh (India) using Geographical Information System

Land suitability analysis is prerequisite for sustainable agriculture and it plays a pivotal role in the niche based agricultural planning in mountain regions. In this paper different parameters viz. climatic (precipitation and temperature), topographic (elevation), soil type and land cover/land use have been used in order to perform land suitability evaluation for cereals food-grain crops in Himachal Pradesh using Geographic Information System (GIS). The suitability analysis was performed by digital processing of geo-referenced data (elevation, climate, soil and landcover) and calculating potential production areas by combining different types of geographical data through decision rules framed for each crop in ArcView spatial analyst. Suitable areas have been delineated for cereal crops in the form of land suitability maps. In comparison to the actual area under cereal crops, the possibility of further expansion under each cereal crop was determined. These discriminated areas appear suitable for growing these crops and can be harnessed efficiently for achieving long term sustainability and food security.     

Satellite data and geographic information systems for fire and resource management in the Canadian arctic

Abstract

A method of analyzing remotely sensed data, a geographic information system, and an intelligent fire management system have been developed to provide integrated resource data for fire and other resources management. Natural and cultural features were digitized from 1:50,000 topographic maps using a geographic information system (GIS) to cover the 29 communities below the tree line in the western Canadian Arctic. Landsat Thematic Mapper data covering the same area were classified into land cover or fuel types. Detailed information on each fire such as location, area burned, date of discovery, fire number, fire zone, fire class and source of ignition was obtained and added to each map sheet as attribute data. A generalized vegetation cover map using NOAA AVHRR data was also obtained. The Intelligent Fire Management Information System (IFMIS) integrates relational data bases, geographic information display, and expert systems. It also has a spatial analysis procedure for forest fire preparedness planning. Linking the weather to the forest fuels through the Fire Weather Index system (FWI) and the Fire Behaviour Prediction System (FBPS), fire danger and fire behaviour are calculated and displayed, cell‐by‐cell. Values‐at‐risk and fire suppression resources are used in the dispatching and planning component of the system. The planning component allows the user to evaluate the coverage of fire suppression resources under the prevalent forecast fire behaviour conditions. Through the integration of data from the above systems, a set of maps were created which were used to analyze fire behaviour potential, identify fire hazards, and provide a basis for settlement protection strategies within the context of other land use activities such as wildlife harvesting and recreational activities.     

Importance of landscape features and Earth observation derived habitat maps for modelling amphibian distribution in the Alta Murgia National Park

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless, for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscape features (i.e., habitat mosaics) may have an important role for modelling amphibian distribution.There were different aims in this analysis. Firstly, we compared the effectiveness of the information provided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat taxonomy named General Habitat Category (GHC), which is based on the concept of biological forms of dominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing validated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earth observation data according to ecological expert rules involving concepts related to spatial and temporal relationships among LC/LU and habitat classes.Then, we investigated the importance for amphibians of the landscape surrounding ponds within the Italian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are important for the presence/absence of amphibians in this area, and identified which classes are more important for amphibians. The results obtained can provide useful indications to management strategies aiming at the conservation of amphibians within the study area. An information-theoretic approach was adopted to assess whether GHC maps allow to improve the performance of species distribution models. We used the Akaike's Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LU categories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categories can better explain the distribution of frogs, compared to LC/LU classes.     

Geospatial modelling for goral habitat evaluation

This study attempts to evaluate the habitat suitability of Chilla Sanctuary of Rajaji National Park for goral(Nemorhaedus goral) spread over 25859 sq km area. The IRS-1B false colour imagery, Survey of India topographical maps and ground observations were used to generate the spatial information on the extent of forest cover, waterholes, slope, settlements and the road network in and around the Sanctuary. The geospatial modelling was attempted using Arc/Info geographic information system (GIS). While proximity to waterholes, open forests with intermittent grasslands and steep slopes (30 degree or more) were considered as favourable conditions, the proximity to roads/settlements and flat terrain was taken as unfavourable condition. The results of this study showed that about 14 per cent area of the Sanctuary is highly to moderately suitable for goral. An additional 5 per cent area becomes available to goralif gujjars (tribals living inside sanctuary) are settled outside the sanctuary. Besides highlighting the endangered condition of the habitat for goral, this study demonstrates potential of remote sensing and GIS techniques for wildlife habitat suitability evaluation.