Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Mapping of habitats with relevance for nature conservation involves the identification of patches of target habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limiting the necessary ground reference to a small sample of target habitats would greatly reduce and therefore support the field mapping effort. We thus aim to answer in this study the question: can semi-automated remote sensing methods help to map such patches without the need of ground references from sites not relevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiency of large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map four habitat types across a patchy landscape of 1000 km² near Munich, Germany. This task was conducted using the low number of 125 ground reference points only along with easily available multi-seasonal RapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectance due to different phenological development across space, continuous transitions between the habitats and the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simple and affordable approach can thus be recommended for a first survey of previously unmapped areas, as a tool for identifying potential gaps in existing habitat inventories and as a first check for changes in the distribution of habitats.     

多分类SVM主动学习及其在遥感图像分类中的应用

针对应用传统分类器和被动学习的方法,难以满足遥感图像处理实际应用的要求这一困境,提出了一种新的基于多分类SVM的主动学习方法,与被动学习的随机选择不同,主动学习是在少量标记类别的初始训练样本集基础上,通过反复迭代主动学习的方式,得到最有利SVM分类器性能的样本为支持向量。研究表明,这种方法直接避免了大量的计算,可有效地减少样本训练时需要标记样本的数目,并取得较为理想的分类效果。     

利用对象和支持向量机的遥感信息提取方法探讨

土地利用/覆被专题信息的快速、高效、准确提取是遥感图像处理研究的重要方向。传统的遥感分类方法常依靠像元的光谱值,未充分利用影像的空间信息。本文将面向对象影像分割和支持向量机方法相结合,复合光谱和纹理信息,建立了Object-SVM分类模型,并与面向对象的模糊函数和基于像元的SVM方法相比较,探寻区域尺度土地利用/覆被信息提取方法。结果显示,Object-SVM模型有效地提高了遥感图像的分类精度和分类效率,对于区域尺度影像的快速、准确、客观的信息提取具有实际意义。     

Landscape ecological planning in a Basaltic terrain, central India, using Remote sensing and GIS techniques

In the present study, detailed field survey in conjunction with remotely sensed (IRS-1D, LISS-III) data is of immense help in terrain analysis and landscape ecological planning at watershed level. Geomorphologically summit crust, table top summits, isolated mounds. plateau spurs, narrow slopes, plateau side drainage floors, narrow valleys and main valley floor were delineated. The soil depth ranges from extremely shallow in isolated mounds to very deep soils in the lower sectors. Very good, good, moderate, poor and very poor groundwater prospect zones were delineated. By the integrated analysis of slope, geomorphology. soil depth, land use/land cover and groundwater prospect layers in GIS. 29 landscape ecological units were identified. Each landscape ecological unit refers to a natural geographic entity having distinctive properties of slope, geomorphology. soil depth, land use/ land cover and groundwater prospects. The landscape ecological stress zone mapping of the study area has been carried out based on the analysis and reclassification of tandscape ecological units. The units having minimum ecological impact in terms of slope, geomorphology, soil depth and land use/land cover were delineated under very low stress landscape ecological zones. The units having maximum ecological stress in the form of very high slopes, isolated mounds, table top summits and summit crust, extremely shallow soils, waste lands and very poor groundwater prospects were delineated into very high stress landscape ecological zones. The integrated analysis of remotely sensed data and collateral data in GIS environment is of immense help in evaluation of landscape ecological units and landscape ecological stress zones. The delineated landscape ecological stress zones in the watershed have been recommended for landscape ecological planning for better utilization of natural resources without harming the natural geo-ecosystem of the area.     

Remote sensing and GIS application in the detection of environmental degradation indicators

The main aim of this research is to highlight the environment change indicators during the last 20 years in a representa-tive area of the southern part of Iraq(Basrah Province was taken as a case) to understand the main causes which led to widespread environment degradation phenomena using a 1:250000 mapping scale.Remote sensing and GIS’s 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 land,sand land,urban area,unused land,and water bodies.Supervised classification and Normalized Difference Vegetation Index(NDVI),Normalized Difference Build-up Index(NDBI),Normalized Difference Water Index(NDWI),Normalized Difference Salinity In-dex(NDSI),and Topsoil Grain Size Index(GSI) were adopted in this research and used respectively to retrieve its class boundary.The results showed a clear deterioration in vegetative cover(514.9 km2) and an increase of sand dune accumulations(438.6 km2),accounting for 10.1,and 10.6 percent,respectively,of the total study area.In addition,a decrease in the water bodies’ area was de-tected(228.9 km2).Sand area accumulations had increased in the total study area,with an annual increasing expansion rate of(33.7 km2·yr·1) during the thirteen years covered by the study.It is therefore imperative that Iraqi government undertake a series of pru-dent actions now that will enable to be in the best possible position when the current environmental crisis ultimately passes.     

Check dam positioning by prioritization of micro-watersheds using SYI model and morphometric analysis — Remote sensing and GIS perspective

Sediment Yield Index (SYI) model and results of morphometric analysis have been used to prioritize watersheds and to locate sites for checkdam positioning in Tarafeni watershed in Midnapur district. West Bengal. Various thematic maps such as land use/land cover, slope, drainage, soil etc. were prepared from 1RS ID LISS III digital data, SOI toposheets of 1:50,000 scale and other reference maps. Morphometric parameters such as bifurcation ratio (R_b). drainage density (D_d), texture ratio (T), length of overland flow (L_o), stream frequency (F_u), compactness coefficient (C_c), circularity ratio (R_c), elongation ratio (E_r), shape factor (B_s) and form factor (R_f) were computed. Automated demarcation of prioritization of micro-watersheds was done by using GIS overlaying technique by assigning weight factors to all the identified features in each thematic map and ranks were assigned to the morphometric parameters. Five categories of priority viz., very high, high, medium, low and very low, were given to all the watersheds in both the methods. Sixty-two micro-watersheds using SYI method and twenty-three micro-watersheds using morphometric have been prioritized as very high priority. Final priority map was prepared by considering the commonly occurred very high-prioritized micro-watersheds in both SYI model and morphometric analysis. Twenty-four suitable sites were identified for check dam construction in 21 highly prioritized watersheds. It is proved that integrated study of SYI model and morphometric analysis yield good result in prioritization of watersheds.     

Corridor analysis in Rajaji-Corbett elephant reserve — A Remote sensing and GIS approach

This paper deals with corridor analysis in Rajaji-Corbett Elephant Reserve in the Shivaliks of Uttaranchal state. Efforts were made to detect changes in the state of forest covervis- à -vis the status of corridors during the three periods i.e. 1967. 1986 and 1998 using remote sensing and GIS. TheERDAS Imagine digital image processing andArcView GIS software packages were used for this purpose. Temporal satellite imagery and ground observations in the Rajaji-Corbett Elephant Reserve revealed forest loss, degradation and disturbances in the corridor areas, hindering elephant movement and restricting them to forests islands. Motichur-Chilla corridor, despite being a highly favoured habitat for the elephants was found to be highly threatened followed by Kotdwar and Ramnagar corridors. Construction of wide bridges across Kunao-Chilla Canal, recreation of corridors through reforestation, reduction of all kinds of pressures in the corridor areas and providing higher protection to corridors are recommended.     

An assessment of biodiversity hotspots using remote sensing and GIS

This study focuses on the assessment of the status of world’s remaining closed forests (WRCF), population distribution, and protected areas in global biodiversity hotspots using remote sensing and Geographic Information System (GIS). Conservation International (CI) has identified 25 eco-regions, called biodiversity hotspots that are especially rich in endemic species and are particularly threatened by human activities. This study uses globally consistent and comprehensive geo-spatial data sets generated using rerriote sensing and other sources, and the application of GIS layering methods. The consistent data set has made it possible to identify and quantify relationships between the WRCF, human population, and protected areas in biodiversity hotspots. It is expected that such information will provide a scientific basis for biodiversity hotspots management and assist in policy formulations at the national and international levels.     

Soil resource assessment and mapping using remote sensing and GIS

In this study digital image processing for physiographic analysis and soil resource mapping of Solani watershed was carried out using satellite remote sensing data and GIS. Digital image processing of satellite data facilitated in accurately delineating and identifying various soil mapping units. The physiography of the study area is mainly influenced by denudational and colluvial processes in the upper part and by sedimentation processes in the lower part. Topography of the land and nature of parent material along with the time factor seemed to have played a vital role in the genesis of soils. Majority of the mapping units are Typic Haplustepts with Entisols and Inceptisols being the major soil orders. The soils of the Siwalik hills experiences severe erosion, which prevents the maturation of soil profile. The present study demonstrated that satellite remote sensing and GIS is a valuable tool for physiographic analysis and soil resource mapping.     

利用影像相关分析的遥感变化检测

针对利用单一光谱特征进行影像相关分析在遥感变化检测应用中效果较差的问题,提出了一种将纹理特征与光谱特征相结合的相关系数计算模型。应用中在对多时相影像进行相同尺度棋盘分割的基础上,先计算各对应分割窗口内的相关系数,再以其中心点的坐标和相关系数值作为一个特征点,在三维空间中进行插值处理得到整个区域的相关系数空间分布图;进一步通过密度分割处理提取变化信息。本文以两期GF-1影像数据进行了变化信息提取试验,结果表明,采用组合特征相关系数的变化检测结果明显优于单一光谱相关系数的变化检测结果。组合相关系数的应用研究为利用影像相关分析方法从高分影像中提取变化信息提供了一种新的思路。     

Mapping and characterization of inland wetlands using remote sensing and GIS

Sustainable management of wetland ecosystem is necessary as it serves the important functions such as food storage, water quality maintenance and providing habitat for different species of wildlife. Hence, an inventory of wetlands in any given area is a pre-requisite for their conservation and management. A study has been carried out to delineate the wetlands of east Champaran district of Bihar, India, using IRS ID LISS III data. The data for the pre and post monsoon seasons have been analysed and the wetlands have been qualitatively characterized based on the turbidity and aquatic vegetation status. The extent of water logging problem in the study area was inferred from the seasonal variation of waterspread during both the seasons. The three categories of wetlands (ponds/lakes, water logged areas and oxbow lakes) have been identified. From the analysis, it has been observed that the inland wetlands constitute 2.7% of the study area, of which 1.8% is subjected to water logging. Thus, this study highlights the usefulness of remotely sensed data for wetland mapping, seasonal monitoring and characterization.     

Mapping of active tectonics intensity zones using remote sensing and GIS

The Bundi-Indergarh sector in southeast Rajasthan is characterized by folded and faulted Vindhyan rocks that are exposed as NE-SW trending long parallel ridges. The sector is separated from older rocks by the Great Boundary Thrust and is traversed by younger cross faults at several localities. The thematic maps of geomorphology, slope, vegetation index and morphotectonic parameters of Bundi-Indergarh sector have been prepared using IRS ID L1SS III and WiFS and, Landsat ETM digital data. These theme are integrated in GIS environment to assess the neotectonic potential in the area. The neotectonic potential map of the sector has been generated that indicates relative potential values as high (55–85), medium (35–55) and low (5–35) on 100-point scale. The observed four high potential zones in the area are located at the intersection of NE-SW and NW-SE lineaments. The study brings out methodology for assessing active tectonic potential of the area.     

基于遥感与GIS的中国湖泊形态分析

基于2010年Landsat TM/ETM数据,结合Google Earth影像和其他资料,采用面向对象的分类方法,以30 m的分割尺度对图像进行分割,并通过比值指数、归一化差值水体指数及谱间关系等指数来提取水体信息,将水体分为河渠、湖泊及水库坑塘3类,并对分类结果进行精度验证,最终得到中国湖泊空间分布信息;以此为基础,计算出反映湖泊形态的景观指数(如形状指数、近圆形指数及分维数等),并结合湖泊的面积和湖泊岸线长度来说明湖泊的基本状况及其形状的复杂程度。结果表明:利用面向对象分类方法可以得到较高的分类精度,3种类型水体分类的总精度为93%,湖泊分类精度达到90%以上;在中国内陆,面积大于1.0 km2的自然湖泊2 477个,总面积约77 934.72 km2;在5个湖区中,青藏高原湖区的湖泊面积最大,占湖泊总面积的54.34%,其形状指数和分维数均值为最小,湖泊形态比较简单;东部平原湖区降雨充沛,水系发达,其形状指数和分维数均值最大,湖泊岸线更加曲折,形态复杂多变;通过分析得出,形状指数与分维数有明显的相关性(R2=0.95),湖泊岸线分维数值越高,其形状指数越大,岸线越复杂。     

基于PCA的变化向量分析法遥感影像变化检测

为实现对土地覆盖变化的遥感监测,研究了一种基于不同年份单时相遥感数据提取差异影像、自动确定变化阈值提取变化区域的新方法。以南通市Landsat8 OLI影像为例,对2期影像分别进行主成分分析(principal component analysis,PCA);取前3个主分量进行变化向量分析(change vector analysis,CVA),构造变化检测差异影像,并与传统PCA法和CVA法构造的差异影像进行对比;对3景差异影像分别用传统全局阈值法和局部最小错分概率法自动确定阈值,分别提取变化区域,得到6景变化区域图。利用目视解译样点进行精度评价的结果表明,改进后的基于PCA的CVA法提取的变化区域总体精度可达92.78%,Kappa系数可达0.842 6,证明使用该方法可有效地进行不同年份单时相遥感数据的变化检测。     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge.     

Sustainability assessment of sodic land reclamation using remote sensing and GIS

Sodicland reclamation in the Indo-Gangetic plains is being done on a large scale in the states of Uttar Pradesh, Punjab and Haryana in India. However, in certain areas, the reclamation has been reported to be unsustainable and the soils are reverting back to sodicity condition. A study was conducted in one of the reclamation sites of Etawah district for sustainability assessment of sodic land reclamation using remote sensing, Geographic Information system (GIS) and ancillary ground information. Multitemporal satellite data were used for delineation of reclaimed sodiclands and reverted sodic land. Field survey was conducted to find out the various causative factors. Groundwater level information and detailed field survey data were analysed in GIS environment. Results showed that in the reclamation site covering 3,905 ha. in 57 villages of the district, about 27 per cent of reclaimed lands were reverted to sodicity. High water table condition, improper drainage, nearness to canal (within 500 m), and hard pan in the sub-soil were found to be the reasons for unsustainability of reclamation.     

Remote sensing and GIS application for land quality assessment for coffee growing areas of Karnataka

An assessment of land quality was carried out for coffee-growing areas of Karnataka using satellite image, toposheets and soil studies. The investigation focused on monitoring soil processes that control the land quality using satellite data in order to identify the land qualities that are ideal for coffee-growing; to identify the status of land qualities of coffee-growing areas using satellite imageries, toposheets and soil resource maps and to characterise land quality using soil studies in selected areas. The land quality was characterised using climatic data, terrain analysis and soil attributes. From the study it was observed that coffee growing lands of Gabbugal and Kelagur have best land qualities and coffee-growing areas of Balur and Nellikkad have moderate land qualities. Satellite image and aerial photographs were successfully used for monitoring the land quality and its changes in these areas. For optimum utilization of available natural resources on a sustainable basis, timely and reliable information on soils regarding their nature, extent and spatial distribution along with their potential and limitations is very important. The efficiency and accuracy of data are improved when remote sensing data products such as aerial photographs and satellite image are used.     

Jerdon’s courser habitat analysis using Remote sensing

Jerdon’s courser (Cursorius bitorquatus) considered as lost bird is found in Sri Lankamalleswaram Sanctuary, Cuddapah District, Andhra Pradesh. It is listed in Red data book as endangered bird. Analysis of satellite imagery of 1989, 1996, 1998 and ground information have revealed an improvement of Jerdon courser’s habitat after declaration of area as sanctuary in 1988. Large open grounds show a decrease, white small open grounds surrounded by scrub plants offering protection and food cover increase. Analysis of satellite imagery shows core niche covering 4 x 3 km area and larger area of sanctuary (7.3 x 13.1 km) having various land cover classes. Comparison of satellite imagery from 1989 to 1998 shows degradation of larger area of sanctuary where birds are not seen.     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning. Comparing with the results produced only by Bayes classification, the overall accuracy increased by 11% and the accuracy of some classes, such as garden and forest, increased by about 30%. The results indicate that inductive learning can resolve spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdividing some classes with the discovered knowledge.