Forest cover and landuse mapping in Karbi Anglong and North Cachar Hills districts of assam using landsat MSS data

Karbi Anglong and North Cachar Hills districts of Assam are endowed with rich and diverse vegetation resources. Increased human pressure due to shifting cultivation and raw material extraction for industrial purposes are heavily altering the forested landscape. The present study deals with mapping of forest types in the two districts using LANDSAT-MSS digital data. The maps thus generated provide spatial distribution of bioclimatic vegetation types. Supervised maximum likelihood classification has been performed using training sets collected during field work. The spectral behaviour of vegetation types have been studied for optimising classification scheme. The classification accuracy of classes mapped has been calculated.     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)     

Glacial mapping using landsat thematic mapper data: A case study in parts of gangotri glacier, NW himalaya

Glacial mapping is difficult and hazardous because of the remoteness and inaccessibility of the terrain. In this context, remotely-sensed data from satellites provide valuable information on glaciers and the associated landforms. It is important to note that judicious selection of spectral bands is critical in mapping the glacial features. Glacial landforms in parts of Gangotri glacier, NW Himalaya, have been delineated using selected bands of Landsat Thematic Mapper Data. Digital image processing of Landsat data has helped in identifying the major features of the Gangotri glacier such as accumulation and ablation zones, and glacial moraines. The study shows that Thematic Mapper bands 4, 5 and 7 are more useful in snow mapping because of their distinct spectral discriminability in identifying the glacial features.     

Remote sensing applications in the study of landuse and soils of aeolian cover op the western part of Haryana State

Multispectral and multitemporal landsat-FCC of 1:250,000 and transparancies on 1:1000,000 scales of bands 4, 5, 6 and 7 were interepreted to identify and delineate the areas under varying intensities of dunal activity in the western part of Haryana State comprising an area of 12610 sq. km. CCTs of some representative areas were also interpreted on the Multispectral Interactive Data Analysis System. Field checks were made to correlate the laboratory interpretation and the ground truth. The study areas were differentiated into the following units: (1) Sandy desert zone: No cultivation on dune tops except some shrubs, (2) Aeolian cover with sandy hummocks: low intensity cultivation, (3) Plain with aeolian cover: moderately cultivated, (4) Plain: Moderately to intensively cultivated. The major soils in association were Typic Torripsamments/Ustipsamments/Ustorthents/Ustifluvents/Calciorthids and Natric Camborthids.     

Detecting and mapping slums using open data: a case study in Kenya

ABSTRACT

The worldwide slum population currently stands at over one billion, with substantial growth expected in the coming decades. Traditionally, slums have been mapped using information derived mainly from either physical indicators using remote sensing data, or socio-economic indicators using census data. Each data source on its own provides only a partial view of slums, an issue further compounded by data poverty in less-developed countries. To overcome such issues, this paper explores the fusion of traditional with emerging open data sources and data mining tools to identify additional indicators that can be used to detect and map the presence of slums, map their footprint, and map their evolution. Towards this goal, we develop an indicator database for slums using open sources of physical and socio-economic data that can be used to characterize slum settlements. Using this database, we then leverage data mining techniques to identify the most suitable combination of these indicators for mapping slums. Using three cities in Kenya as test cases, results show that the fusion of these data can improve the mapping accuracy of slums. These results suggest that the proposed approach can provide a viable solution to the emerging challenge of monitoring the growth of slums.     

Morphometric analysis of a drainage basin using aerial photographs: A case study of Khairkuli basin,district Dehradun,U.P.

Remote sensing techniques using satellite images and aerial photographs are convenient tools in morphometric analysis of a drainage basin. In the present study morphometric parameters of Khairkuli drainage basin, district Dehradun, are worked out using aerial photographs. The parameters worked out include bifurcation ratio, stream length, form factor, circulatory ratio, elongation ratio, drainage density, constant of channel maintenance and stream frequency. Hypsometric relations of drainage basin are also presented. Relation between cumulative stream length and the stream order establishes that the ratio between cumulative stream length Σ ₁ ^u Σ ₁ ^nu Lu and the stream order u is constant throughout the. successive orders of a drainage basin suggesting that geometrical similarity is preserved in the basins of increasing order. The morphometric parameters computed suggest that the area is covered by resistant permeable rocks (with fracture and karstic porosities) and vegetative cover, the drainage network is affected by tectonic disturbances. The peak flows generated from the basin are likely to be moderately high and of short duration.     

Forest cover monitoring using remote sensing and GIS — A case study in Dhaulkhand range of Rajaji National Park,Uttar pradesh

Rajaji National Park in U.P. is a protected area where large number of nomad population live within the park area. Their dependence on the forest for cattle rearing and firewood has caused degradation of the forests. Proximity to settlements outside the park further adds to the problems. In the present study, forest cover and river, bed changes have been attempted by using temporal aerospace data of the year 1960 and 1993. Subsequently, PAMAP GIS package has been used for the change detection analysis. The study indicates that the land cover changes are mainly due to biotic factors. Some of the important changes in forest cover are: (i) transformation of mixed forest to scrub forest in 67 ha (ii) the sal mixed and mixed sal forest categories have replaced sal forest in 262 ha (iii) significant increase in forest plantation i.e. 2075 ha in the year 1960 to 3793 ha in 1993 (iv) eighteen times increase in Chirpine area, it increased from 13 ha in 1960 to 230 ha in 1993. The land cover changes in 6663 ha (45%) out of 14962 ha of the study area. The consequential changes in the river beds due to the change in the forest cover was also analyzed. During this period river beds with boulders have increased by 87 ha. These river bed changes include lengthening and broadening of river and change in river course.     

Landsat mss data for land use and land cover inventory and mapping: A case study of Andhra Pradesh

This study deals with the technique of remote sensing and how far it helps in the rapid study of geographical phenomena especially land use within a very short time and accurate manner. It evaluates how well data from the Landsat - Multispectral Scanner (MSS) could be used to detect, identify and delineate land use features within the Andhra Pradesh State. The main objective was to prepare a small scale land use map from satellite imagery showing the broad distribution of land use patterns to serve as a base for monitoring land use change.     

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban–rural landscape in the Brazilian Amazon

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.     

Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.     

NDSI与NDFSI结合的山区林地积雪制图方法

积雪是冰冻圈的重要组成部分,因其在可见光波段的高反射率、低导热率的特性以及大面积的覆盖,成为全球辐射平衡的重要决定因子。在中纬度的干旱和半干旱山区,季节性的冰雪融水是春季河川径流的主要补给水源,山区积雪分布的变化对融雪期河流径流量的波动具有重要影响。当前的积雪产品在下垫面为山区林地时会低估积雪面积,从而影响了山区水文过程模拟的精度。本文基于Landsat OLI影像,采用归一化差值积雪指数NDSI和归一化差值林地积雪指数NDFSI相结合的方法,对春季融雪期的阿尔泰山区泰加林地进行积雪识别,并采用海拔高度、温度、以及对应的高分数据对提取结果进行了定量分析。结果表明,采用NDSI进行积雪识别时,山区林地的积雪会被大量漏分;对林地像元采用NDFSI阈值法可以区分林地中是否有积雪分布。NDSI和NDFSI相结合的积雪识别方法操作简单,不需要提供森林分布图等辅助数据,可以有效提高山区林地复杂环境下积雪制图的精度。     

Soil survey of command area for planning of water management: A case study of an area in Hardoi district,U.P.

Development of irrigation potential for increasing agricultural production is the main aim of Command Area Development. This involves interaction amongst various factors; the soil, the crop, the climate, the topograpny and others. Water management in Command Area requires careful planning, based upon adequate data regarding soils, land use and topography. It is essential to make the best possible use of the available water without creating waterlogging, salinity and alkalinity problems in the command area. The use of aerial photographs is a well recognized expedient, convenient and economical method of data collection, which appreciably suits the requirement of the survey of Command Areas. As large areas have to be surveyed for data collection, the best way to accomplish this is by taking up survey of command area in different levels: reconnaissance, semi-detailed and detailed. The reconnaissance and semi-detailed survey would enable us to identify problem areas. Detailed survey are needed in these areas for taking up reclamation measures. A case study of soil survey carried out in the Sharda Sahayak Command Area of a part of Hardoi District is given as an example to illustrate the use of aerial photographs for planning of water management practices.     

Glacier and glacial lake classification for change detection studies using satellite data: a case study from Baspa basin,western Himalaya

Using high-resolution Google Earth^TM images in conjunction with Landsat images, the glaciers and lakes in the Baspa basin are classified to explore the recent changes. A total number of 109 glaciers (187 ± 3.7 km²) are mapped and subsequently classified as compound valley glaciers, simple valley glaciers, cirques, niches, glacieretes and ice aprons. The compound and simple valley glaciers contribute 67.1 ± 1.3% and 19.8 ± 0.3% to the total glacier cover of the basin. Similarly, a total number of 129 glacial lakes (0.360 ± 0.007 km²) are identified. From 1976 to 2011, the compound valley glaciers have lost a small area of 10.3 ± 0.03% at a rate of 0.41 ± 0.002 km² a^-1, whereas the niche glaciers have lost higher area of 40.1 ± 0.001% at a rate of 0.04 ± 0.0001 km² a^-1. Change detection of two benchmark glacial lakes revealed a progressive expansion during recent decades. The Baspa Bamak proglacial lake has expanded from 0.020 ± 0.0004 km² (2000) to 0.069 ± 0.001 km² (2011). Due to the complete loss of source ice, another glacial lake has expanded from 0.09 ± 0.001 km² (1994) to 0.10 ± 0.002 km² (2011). During the study period, the mean annual temperature that is T_avg, T_min and T_max have increased significantly at the 95% confidence level by 1.5 ^oC (0.070 °C a^-1), 1.8 ^oC (0.076 °C a^-1) and 1.6 ^oC (0.0071 °C a^-1) from 1985 to 2008. However, the precipitation has decreased significantly from 1976 and 1985 to 2008.     

Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints

We propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen’s k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps.     

Assessing image processing techniques for geological mapping: a case study in Eljufra,Libya

Various image processing techniques were experimented with in this study to evaluate their efficiency for geological mapping in the Eljufra area of northwest Libya. Remote sensing data including multi-spectral optical Landsat Enhanced Thematic Mapper (ETM+), Synthetic Aperture Radar (ERS-2 SAR) and Digital Elevation Models (DEMs) extracted from the Shuttle Radar Topography Mission (SRTM) data were used to trace different lithological units as well as extracting geological lineaments in the study area. The study area is located in an arid environment mostly devoid of any vegetation. Most lithological and structural units are distinguishable based on their topographic form and spectral properties. Fusion of ETM+ and ERS-2 images was experimented with to further identify lithological units. Shaded relief techniques were implemented to enhance terrain perspective views and to extract geological lineaments. The results discriminated different rock units and modified formation boundaries and revealed new geological lineaments. Nine rock units were identified and plotted in the new geological map defined by the new boundaries. The dominant lineaments tend to run in the NNW-SSE and NNE-SSW directions. Analysis and interpretation of the lineaments provided information about the tectonic evolution of the study area.     

Radar speckle reduction and derived texture measures for land cover/use classification: a case study

This study examined the appropriateness of radar speckle reduction for deriving texture measures for land cover/use classifications. Radarsat-2 C-band quad-polarised data were obtained for Washington, DC, USA. Polarisation signatures were extracted for multiple image components, classified with a maximum-likelihood decision rule and thematic accuracies determined. Initial classifications using original and despeckled scenes showed despeckled radar to have better overall thematic accuracies. However, when variance texture measures were extracted for several window sizes from the original and despeckled imagery and classified, the accuracy for the radar data was decreased when despeckled prior to texture extraction. The highest classification accuracy obtained for the extracted variance texture measure from the original radar was 72%, which was reduced to 69% when this measure was extracted from a 5 × 5 despeckled image. These results suggest that it may be better to use despeckled radar as original data and extract texture measures from the original imagery.     

Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models

Land cover monitoring using digital Earth data requires robust classification methods that allow the accurate mapping of complex land cover categories. This paper discusses the crucial issues related to the application of different up-to-date machine learning classifiers: classification trees (CT), artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). The analysis of the statistical significance of the differences between the performance of these algorithms, as well as sensitivity to data set size reduction and noise were also analysed. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land cover categories in south Spain. Overall, statistically similar accuracies of over 91% were obtained for ANN, SVM and RF. However, the findings of this study show differences in the accuracy of the classifiers, being RF the most accurate classifier with a very simple parameterization. SVM, followed by RF, was the most robust classifier to noise and data reduction. Significant differences in their performances were only reached for thresholds of noise and data reduction greater than 20% (noise, SVM) and 25% (noise, RF), and 80% (reduction, SVM) and 50% (reduction, RF), respectively.     

Image processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district,U.P., India

The present study is based on digital analysis of IRS 1C/1D LISS-III and IRS P6 LISS-IV images for identification of lineaments in Meja Thermal Power Plant site in Allahabad district. In spite of all limitations due to anthropogenic activities of stone quarrying and criss cross unmetalled roads, almost non-existent vegetation indicators and soil moisture, the efforts were made to identify and map a number of lineaments in the study area and these were subsequently verified in the field. Most of the lineaments identified and mapped are not having any major displacement along them or are not displacing or truncating any major rock unit. Field investigations have revealed that there is no drastic change in the trend of rocks along the lineaments mapped in the study area. On the basis of digital analysis of IRS P6 LISS-IV data NE–SW and NW–SE trending lineaments have been identified and mapped. Some NNE–SSW trending lineaments have also been mapped. All the lineaments demarcated in this area still need to be correlated with the seismic data of the area by superimposing the microseismicity data over lineaments.     

Aerial remote sensing application in hydrogeological and geomorphic mapping of a rocky terrain: A case study from Uttar Pradesh

Ground water prospecting in hard rock areas of Uttar Pradesh is quite difficult, owing to the geological and geomorphic controls of sub-surface strata on the occurrence and movement of ground water resources, and, therefore, it requires systematic investigations, mainly with the aid of remotely sensed and data, which undoubtedly provide realistic information on the hydrogeological conditions, geomorphologic features, drainage patterns etc. of any rocky area. In this paper, an endeavour has been made to locate ground water possibilities in the drought-affected, rocky terrain of the southern Mirzapur district of Uttar Pradesh, covering part of block Muirpur which is mainly characterised by the varied geological formations, including Archean complex, pre-Vindhyan matasediments and Gondwana formation, through the application of air photointerpretation technique, primarily based on the geologic/geomorphologic approach. The hydro-investigation in the area was carried out using panchromatic aerial photos of 1∶60,000 scale, chiefly to identify the drainage patterns, major geomorphic geo structural and lithological units; as these may control the ground water regime of the area, Further. the present study was also ably supported with limited field survey and hydrogeological data, for achieving reliable results.     

Distribution analysis of salt affected soils under canal and non-canal command area in a part of Etah district,U. P., using remote sensing technique

A World Bank-aided project on sodic land reclamation in Uttar Pradesh is being executed by U.P. Bhumi Sudhar Nigam, Lucknow, and Remote Sensing Applications Centre, U.P., Lucknow has the responsibility of sodic land mapping for the execution of land reclamation programme at the cadastral level. Sodic lands are mainly concentrated in the Gangetic alluvial plains but the problem of sodicity is particularly acute in the canal-irrigated areas. A study of the distribution pattern of sodic lands in canal and noncanal command areas in a reclamation site (covering 60 villages out of which sodic lands were mapped in 51 villages) of Etah district in Uttar Pradesh, indicates that 18.39 per cent area of the canal command villages was barren sodic which was 13.41 per cent of the total geographical area of the site (15417 ha), however, 11.69 per cent area was recorded to be barren sodic in the non-canal command villages which was only 3.16 per cent of the geographical area of the site. The results of soil chemical analysis indicate that barren sodic lands of canal command area are saline-sodic with higher concentration of soluble salts (pH₂ >8.5, EC₂ >4 dSm⁻¹), however, those of non-canal command area are sodic (pH₂ >8.5, EC₂ <4 dSm⁻¹). The post-monsoon ground level in the canal-irrigated areas was in the critical and semicritical zone (< 3.0 mbgl) whereas it was well below the semi-critical zone in the non-canal command area, which indicates that the high ground water level is a major factor to higher the area under sodicity.