Mapping of tropical dry deciduous forest and landuse in part of Vindhyan range using satellite remote sensing

Forest vegetation of Vindhyan range located in the north of G.B. Pant Sagar (dam) has been subjected to degradation due to high biotic pressure caused by the installation of thermal power plants, coal mining, heavy cattle grazing etc. In the present study Landsat TM FCC of 1∶250,000 scale was visually analysed with respect to forest vegetation types, crown density and structure along with other landuse/land cover classes. ExceptShorea robusta (Sal) andLagerstroemia parviflora (Lendia) all forest vegetation types show higher percentage of degradation and under-stocked condition with respect to their areal extent under study. Overall classification accuracy of the forest types has been found to be 88.94%. This indicates that for obtaining reliable mapping accuracy in dry deciduous areas, satellite remote sensing data of appropriate season is essential.

     

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.)     

Vegetation type discrimination on Landsat TM data in heterogeneous forested landscape of Western Ghats—Accuracy evaluation from large scale aerial photo maps

In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%.     

Growing stock estimation through stratified sampling on satellite remote sensing data

Aerial photographs are being extensively used for forest surveys i.e. forest cover type mapping, assessment of growing stock, estimation of area, vegetation studies, etc. Satellite remote sensing technology offers new possibilities and scope for achieving some of the above applications with higher accuracy and reliability. The assessment of growing stock through subjective stratification has also become possible with increase in spatial and spectral resolution. In the present study, the LANDSAT TM FCC (1966) has been visually interpreted on the basis of tonal characteristics. Stratified random sampling method has then used for determination of number of sample units for collection of ground Inventory data. Using regression equations, the volume per hectare of individual forest cover types were calculated. Satellite remote sensing data has been used for initial stratification, distribution of sample plots and calculation of area under various forest cover types. Estimate has been made for available commecial and non-commercial growing stocks in the study area.     

Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape

Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region. Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud- and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Final classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95% overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an overall 57-class accuracy of 75% was achieved despite the omission of 10 entire classes, most of which were annually disturbed or perennial crops grown on very few fields. Synthetic ground-truth data for the 2004 harvest year based on the most common landuse classes over the following 7 years classified 49 of 57 categories at an overall accuracy of 96% in a final version that included CLU polygon majority rule, default smoothing and aggregation, and forcing of urban development and forest from multi-year majority-rule.     

Landsat data in preinvestment surveys—a case study of western Nepal area

In this paper an attempt has been made to estimate the growingstock showing high (51% and up), medium (25–50%) and low (below 25%) density areas and to prepare a broad landuse and forest cover type map by analysing black and white band 5 Satellite imagery (Landsat-1) on 1:1 m scale pertaining to Western Nepal area which comprises Karnali, Bheri, Babai and Rapti Catchments covering a geograghical area of about 50,000 sq km. The information regarding location, extent and area falling in forested and non-forested categories arrived at from Landsat maps provide basic information to planners at the time of formulation of inventory design before taking up preinvestment Surveys. In developing countries where vegetation stock maps are not readily available, forest resources map based on analysis of Landsat data can be prepared in less time and at a lower cost. Such maps may be immensely used by planners at regional or national level. The technique merits further investigation.     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Role of remote sensing in demarcation of alluvial fan deposits in Tarai-Bhabar belt of Nainital District (U.P.) and the environmental impact assessment

Remotely sensed data has unique advantage aver conventional data collection techniques in the study of geomorphology, as physiographical and geo-structural parameters are mostly discernible on the imagery. In the present study an attempt has been made to identify and evaluate the process of geomorphological evolution and hydrogeological conditions, temporal changes in pattern of geomorphic elements and overall impact on environment in alluvial fan region in Nainital District using multidate satellite data from Landsat (1975, 1986) and IRS (1993), through visual interpretation technique. The landuse changes are quite prominent in alluvial fan of upper and lower zone. As a consequence of deforestation, an area of 16 sq. km. of natural forest cover has been lost over a span of 18 years (1975–1993) leading to the increase in rate of erosion as well as environmental degradation in upstream areas. The study suggests that the ground water utilization in Tarai belt without replenishment of confined aquifers and installation of more tubewells in Bhabar belt may lead to total failure of flowing wells and subsequently disturb the balanced ecosystem.     

Remote sensing for change detection in the Sunderbands,Bangladesh

Abstract

Change detection study has been made for the mangrove forest of the Sunderbans (Bangladesh) using remote sensing and other ancillary data (1933–1987). At the advent of the British rule the forest was double their present extent. Its present area is about 6000.0 Sq. Km. The productive land area has been increased from 1960. Gewa (Excoecaria agallocha)‐Sundri (Heritiera fames) cover type areas have been increased at the expense of pure Sundri type. Height classes of the forest have been changed, basal area/ha has decreased. It is observed that there is a net decrease in Sundri standing volume of the order of 40% and that of Gewa 45% over the past 25 years (1960–1985). Total timber volume for all of the species has been reduced to near about a half. Timber volume/ha and basal area/ha for Gewa have increased in the Sharankhola Island of the forest. Sundri tress are being replaced by Gewa. CIR aerial photographs have been found most suitable for cover type analysis and other change detection study. Regular monitoring of the changes of the forest using remote sensing technique has been suggested.     

Assessment and monitoring of estuarine mangrove forests of Goa using satellite remote sensing

The present study highlights the application of satellite remote sensing in the assessment and monitoring of the mangrove forests along the coastline in Goa state of India. Based on onscreen visual interpretation techniques various land use and land cover classes have been mapped and classified. An attempt has been made to analyse changes in the mangrove forest cover from 1994 to 2001 using IRS-1B LISS-II and IRS-1D LISS-III data. An increase in the mangrove vegetation in the important estuaries has been found during 1994 and 2001. During this period, the mangrove forest increased by 44.90 per cent as a result of increased protection and consequent regeneration. Plantation of mangrove species has been raised in 876 ha (1985 to 1997) by the State Forest Department¨     

基于随机森林的土地利用分类与景观格局分析

基于景观格局分析方法,分别选取8个反映景观格局类型和6个反映景观水平格局指数用于探索土地利用变化状况.实验结果表明:①基于选择合适的训练样本下,对3期遥感影像采用随机森林的监督分类方法,总体精度均在94％以上,kappa系数均为90％以上;②综合来看,研究期内建设用地为优势景观类型.研究区斑块数量增加,破碎化程度有所加...     

Role of remote sensing in residential site selection: A case study of Hisar town and its environs

To tackle the problems arising due to rapid urbanization, the urban planners need relevant data base. Since the conventional methods of data acquisition and processing ate not cost and time effective, introduction of new techniques is necessary. Application of satellite remote sensing is an alternative. Ia this paper attempt has been made to find out the usefulness of visual interpretation technique of satellite remote sensing data in the selection of new residential site. SPOT 1 HRV 1 MLA (FCC) date has been used to map existing landuse/landcover of Hisar town and its environs. Based on existing landuse/lsndcover conditions and evaluation of various suitability parameters like physiography, slope, drainage, availability of drinking water and wind direction, a new residential site has been selected. This study may be useful to the urban planners in the preparation of a comprehensive plan Df the town.     

Evaluation of remote sensing techniques for geological studies in eastern part of doon valley

A comparative study of different remote sensing techniques has been made for geological studies in the Eastern part of Doon Valley. Aerial photographs, standard Landsat T.M. FCC and Computer Campatible Tape were used to delineate lithologic units and gological structures. In all, seven lithountis could be delineated and several geological structures, e.g. folds, faults, joints etc were easily marked. The rock types are mainly quartette, phyllite, conglomerate, sandstone, shale and alluvium. Aerial photo-interpertation has proved best for large scale mapping. Landsat TM FCC has proved superior for small scale mapping. Digital analysis of Landsat TM data has increased the interpretability of geological features.     

Modelling Changes in Land Cover Patterns in Mtunzini,South Africa Using Satellite Imagery

Land is the basic resource that is needed by man in order to survive: It provides humans with living space, nutrition and energy resources. The rapid growth of the human population, climate change and pollution on a catastrophic scale has caused the quality of land resources to be compromised. Remote sensing is a useful tool in land cover change detection providing information to decision makers. The aim of this study was to evaluate land cover changes in the Mtunzini area in South Africa over the past 18 years; determine why changes have occurred and predict land cover patterns for future years. In this study a supervised classification was used to detect land cover classes of the Mtunzini area from 1992 to 2009 using four Landsat images in the time series analysis. The supervised classification had an accuracy of 80.80 % which was used to model land cover changes. Commercial sugar cane and forest plantation classes increased throughout the time series. It was estimated in the modelling procedure that bushland (42.11 %) and bare soil (35 %) would be changed to commercial sugar cane. This is indicative of the expanding agriculture sector in Mtunzini. Natural vegetation is predicted to be disturbed: 18 % of bushland and 15.07 % of dense bush are expected to be replaced by rural dwellings. This is owing to a potential increase in the rural population and a reduced local economic growth. This study highlights the need for increased vigilance of the forestry industry and commercial sugar cane farms which may be encroaching on natural vegetation and livelihoods of local residents. Strategic planning and proper management of natural vegetation types is needed as these land cover types are decreasing rapidly.     

Change detection in Sal forest in Dehradun Forest Division using remote sensing and geographical information system

The review of study site have revealed the change in vegetation cover of Sal Dense to Sal Medium and Sal Open in 6 forest Mosaics owing to biotic and abiotic conditions prevailing in the specific areas. Analysis carried out using thematic map derived from aerial photograph of 1976 and satellite data of IRS 1C LISS III False Colour Composite (FCC) of March 1999 revealed the cause for change in forest density classes. Deforestation, encroachment and agriculture have been identified as the underlying causes, which have affected some specific locations to a marked extent. There has been a progressive and remarkable change among vegetation classes from 1976 to 1999. It is evident from forest type and density map that Sal density has significantly reduced from Sal Dense 65.61 % in 1976 to Sal Dense 11.12% in the year 1999 followed by Sal Open 11.18 % and Sal Medium 18.24 %. The overall change has been estimated to be 42.11% of the total forested area.     

Monitoring Spatial and Temporal Land Use/Cover Changes; a Case Study in Western Black Sea Region of Turkey

Rapid land use/land cover changes have taken place in many cities of Turkey. Land use and land cover changes are essential for wide range of applications. In this study, Landsat TM satellite imageries date from 1987, 1993, 2000 and 2010 were used to analyse temporal and spatial changes in the Western Black Sea Region of Turkey. Zonguldak and Eregli two largest and economic important cities which have been active coal mining and iron fabric areas. Maximum Likelihood Classification technique was implemented and the results were represented in classes of open area, forest, agricultural, water, mining, urban and pollution in the sea. Urban areas on both cities increased from 1987 to 2010. The agricultural and open areas from 1987 to 2010 decreased in parallel to land use and land cover change in both cities. Meanwhile, forest areas increased continuously with about 20 % from 1987 to 2010 in both cities. As industrial activity, the coal fields doubled from 1987 to 2010.     

Monitoring the impact of coal mining and thermal power industry on landuse pattern in and around Singrauli Coalfield using Remote Sensing data and GIS

Singrauli Coalfield spreading over an area of about 300 sq km along Uttar Pradesh and Madhya Pradesh border in the central part of India, is witnessing rapid industrialisation due to a large number of open pit coal mining projects and Super Thermal Power Station (STPS). Large scale mining activities along with operation of STPS have generated a great deal of environmental stress not only on the landuse pattern but also on various ecosystems in this region. An integrated remote sensing study was conducted to assess the impact of industrialisation on landuse pattern in the area under reference. The multispectral, multi-temporal data (1975, 1986 & 1991) of LANDSAT MSS and TM duly supplemented with ground truth were studied for generation of multidate landuse maps. Data base for landuse for the years 1975, 1986 and 1991 was created using PAMAP GIS for landuse analysis and change detection for optimal utilisation, planning and management of land resources. The study has revealed that the areas under built-up land, mining and fly ash pond have increased substantially from 1975 to 1991. Loss in forest cover and agricultural land has occurred due to rapid industrialisation in this region. It has been observed from the comparison of 1986 and 1991 data that the wasteland generated due to deforestation for initiating coal mining projects has gradually been reclaimed under operation “Green Gold” launched by Northern Coalfields Ltd. Further the wastelands have been effectively utilised for establishing the STPS, townships as well as other infrastructures in this area.     

Landuse/land cover change in Mumbai-Navi Mumbai cities and its effects on the drainage basins and channels — A study using GIS

The Mumbai-Navi Mumbai cities (Bombay and New Bombay) are among the highest populated cities in the country. The population pressure has caused drastic landuse change in the last seventy years. Multi-date data from SOI topographical maps and Landsat TM digital data have been used to study the landuse change. The change has been quantified using A GIS It was observed that 55% reduction in forest/agricultural land, while a 300% increase in built-up land has taken place in the last seventy years. This has affected the natural drainage system of the cities, causing flooding during monsoons. The quantum of draînage basin area and stream length, in the ten basins which drain the area, under influence of built-up land was found by using a map overlay of the drainage network map and landuse map of 1994. The results shed light on the extent of drainage network disruption within these two neighbouring cities.     

Application of multidate satellite imagery for land use planning and management in the drought prone areas of Karnataka

Based on visual interpretation of Multidate Landsat Imagery, the spatial distribution of land use/land cover over 45,000 sq.km, spread over the three drought prone districts of Bijapur, Belgaum and Dharwar in NW Karnataka, has been mapped. The land use/land cover is classified into five Level-I and twelve Level-II classes. The pattern of change in land use/land cover during the period October, 1980 and January, 1982 has been one of decline in all the land use classes (except for agricultural use, which is more due to seasonal change) which highlight the land use/land cover changes in the drought prone area. An optimum land use plan requires that all the cropland should be zoned for cultivation while marginal lands like scrub land and mixed barren land (from the view point of cultivation) should be zoned for pasture/grazing and animal husbandary. There is a case for flexibility here, depending upon the pressure of population on land. The accuracy level of the ‘information base’ of the thematic map(s) obtained from Landsat imagery is 94 percent.