Impact Assessment of Watershed Programme Through Remote Sensing and Geographical Information System

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

Assessment of Groundwater Recharge Through Rainfall and Water Harvesting Structures in Jamka Microwatershed Using Remote Sensing and GIS

Water harvesting works had been conducted at Jamka micro-watershed of Saurashtra region of Gujarat in India for augmenting artificial groundwater recharge in hard rock aquifers of the semi arid region. In present study groundwater recharge of Jamka micro-watershed was estimated. The natural groundwater recharge through rainfall in the study area was estimated using empirical equations and the artificial groundwater recharge through water harvesting structures which was estimated using remote sensing and GIS. The area under submergence due to water harvesting structures is estimated using remote sensing images. The groundwater recharge in study area was also estimated using water table fluctuation method and compared with total recharge through rainfall and water harvesting structures. The natural groundwater recharge through rainfall in the study area was found varying from 11 to 16 per cent of annual rainfall. The total groundwater recharge in the study area was estimated 390.29?ha?m, in which the contribution of recharge through water harvesting structures was about 38.53%; this revealed that the water harvesting structures played an important role in increasing the groundwater recharge in the region.     

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.     

Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve,Hong Kong,using hyperspectral data and high-resolution data

Mangrove species compositions and distributions are essential for conservation and restoration efforts. In this study, hyperspectral data of EO-1 HYPERION sensor and high spatial resolution data of SPOT-5 sensor were used in Mai Po mangrove species mapping. Objected-oriented method was used in mangrove species classification processing. Firstly, mangrove objects were obtained via segmenting high spatial resolution data of SPOT-5. Then the objects were classified into different mangrove species based on the spectral differences of HYPERION image. The classification result showed that in the top canopy, Kandelia obovata and Avicennia marina dominated Mai Po Marshes Natural Reserve, with area of 196.8 ha and 110.8 ha, respectively, Acanthus ilicifolius and Aegiceras corniculatum were mixed together and living at the edge of channels with an area of 11.7 ha. Additionally, mangrove species shows clearly zonations and associations in the Mai Po Core Zone. The overall accuracy of our mangrove map was 88% and the Kappa confidence was 0.83, which indicated great potential of using hyperspectral and high-resolution data for distinguishing and mapping mangrove species.     

Mapping the Extent of Agroforestry Area in Different Altitudes of Tehri District,North Western Himalaya,India Through GIS and Remote Sensing Data

Assessment of area under agroforestry in Tehri district of North Western Himalaya, Uttarakhand, India has been done using GIS and remote sensing technology. The study district characterized by hilly terrain with varying elevations from 288 m to more than 2800 m and generally gentle slopes, valleys, flat land covers and agricultural terraces. High-resolution satellite imageries (spatial resolution 5.8 m) were used in this study for land uses and land covers classification. According to unsupervised classification, highest area was found under forest class (65.22%) followed by cropland (20.41%). Considerable area was also found under snow cover (9.45%) in the district. Area under agroforestry was estimated to be 5572.26 ha (1.53%) by this method, whereas it was estimated to be 7029.06 ha (1.93%) by supervised classification. Estimated cropland area comes out to be about 20.0%. An accuracy of 86.5% was found in this classification for agroforestry class. Highest area under agroforestry of 3707.36 ha was obtained in 1200–2000 m elevations followed by 2231.26 ha in 288–1200 m elevations. Negligible area was found on high elevation zones of more than 2800 m. The major agroforestry systems of dominated by Grewia oppositifolia (Bhimal), Celtis australis (Kharik) and Quercus leucotrichophora (Banj) were identified and mapped and remaining systems were grouped as others class. Estimated area under G. oppositifolia, C. australis and Q. leucotrichophora based systems come out to be 2330.82, 1456.80 and 1129.10 ha, respectively. These systems are multiple usufructs are food, fuelwood, fodder, fiber and small timber. It has been observed from the accuracy assessment that the estimates of area under agroforestry obtained under this study are reliable.     

Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed,Chitwan, Nepal

The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R² range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management systems.     

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.     

Zoning and monitoring dominant mangrove communities of a part of The Marine National Park, Gulf of Kachchh

Mangroves of the Marine National Park constitute the second largest patch of mangroves in Gujarat, extending up to 11,000 ha, comprising six species of mangroves. Earlier studies carried out using remote sensing data pertained to baseline data generation and mapping and monitoring the mangroves (density-wise) of the Park from 1975 to 1993. Using IRS IC/ID LISS III data (1998–2001) supported by ground data, the distribution of different mangrove communities in the Park has been attempted. Amongst various image-processing techniques, band ratioing followed by supervised classification gave the best result (classification accuracy was 92%).Avicennia community is the most dominant community accounting for more than 70% of the area. TheRhizophora community occupies the inward margins of the creeks and theCeriops community is present in the interior regions. The ecotone between the marsh and mangrove communities has been identified as the transitional mangroves (Avicennia alba, Sueada), representing the transition from the less saline mangrove to the highly saline marsh community. The zoning of the mangroves has also helped in assessing the diversity of the region. Based on the richness of species, three areas, namely Bhains Bid, North-east Dide Ka Bet and South-east Chhad Island have been identified as highly diverse (most suitable area for preservation).     

Estimation of the Aerial Cover of Seagrasses of Lakshadweep Islands (India) Using Indian Remote Sensing Satellite (IRS P6 LISS IV)

Seagrasses ecosystems are fragile yet highly productive ecosystems of the world showing declining trend throughout the world due to natural and anthropogenic pressures. Effective conservation and management plan is thus required to protect these resources, to aid with conservation need mapping and monitoring of seagrasses using high resolution remote sensing data is very much required. Hence, the present study was made to record the seagrass aerial cover in the Lakshadweep islands using IRS P6 LISS IV satellite data. The suitability of LISS IV sensor for seagrass mapping was tested for the first time with an overall accuracy of 73.16%. The study found an area of 2590.2?ha of seagrasses in Lakshadweep islands with 1310.8?ha and 1279.4?ha dense and sparse seagrass cover respectively. The study recommends the use of LISS IV data for mapping the shallow water seagrasses, as mapping efficiency increases nearly 4 times more than the LISS III data, as the former (LISS IV) picks up the small patches of seagrasses and delineates the coral and reef vegetation patches from seagrass class.     

Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes

Abstract

This study demonstrates the integration of landscape aesthetic quality and probable urban growth patterns in urban landscape modelling. This was performed using SLEUTH as a scenario-based urban growth model in Gorgan City of Iran. Future urbanization was predicted under developing three different scenarios including historical, managed and aesthetically sound urban growth up to the year 2030. Multi-Layer Perceptron neural network model was conducted for mapping the aesthetic suitability of the study area. The aesthetic suitability layer was used in the third scenario of SLEUTH model as the excluded layer to protect the scenic patches in future. The results showed that by correct implementation of urban growth policies, 323 ha in the second scenario and 650 ha in the third scenario would be saved. This integrated model would help the planners for a better management of urban landscapes as a Spatial Decision Support System.     

Habitat Suitability Assessment of <Emphasis Type="Italic">Ardeotis nigriceps</Emphasis> (Vigors) in Great Indian Bustard Sanctuary,Maharashtra (India) Using Remote Sensing and GIS

Ardeotis nigriceps, commonly known as Great Indian Bustard (GIB), is a Critically Endangered, Evolutionary Distinct and Globally Threatened (EDGE) and endemic species to the Indian subcontinent. GIB is under tremendous threat in its last strongholds and sliding inextricably towards extinction. The GIB sanctuary in Maharashtra (India) is one of the last refuges of the bird constituting an area of 8496 km² spread over in seven talukas of Solapur and Ahemednagar districts. Major portion of the sanctuary (94.3 %) consists of privately owned lands under a variety of economic vocations and large number of villages and townships. In view of the legal restrictions relating to Protected Area under the Wildlife (Protection) Act of India 1972, the inhabitants of villages and townships faced a very difficult situation regarding use of their lands, development of properties and deriving benefits from planned local and regional development. This created conflict between local people and the forest department over the use of land, which necessitated the rationalization of the sanctuary. The objective of the present study was to map the suitable habitat of GIB in GIB Wildlife Sanctuary as an input for the realignment of the GIB Sanctuary by identifying areas that are important for the GIB. Main parameters considered for the habitat suitability assessments are, habit and habitat of GIB, slope, minimum patch size and disturbance sources. Based on the criteria derived for the ecological and biological requirements of GIB, binary deductive habitat suitability modeling has been done using remote sensing and GIS and prioritized the potential habitats of GIB. The net area of important suitable habitat of GIB in GIB sanctuary is 2304.99 km² out of 8496.44 km². The output of the present study has been used as an input by the committee (set by Honorable Supreme court of India) on rationalization of the GIB Sanctuary and the sanctuary has been rationalized with an area of 1222 km².     

Geostatistical and visualization analysis of crop suitability for diversification in sub-mountain area of Punjab,North-West India

This study presents a Geographic Information System (GIS)-based geostatistical and visualization analysis of crop suitability in two blocks of sub-mountain area of Punjab under diversification programme. It combines the limitation approach of land capability classification, productivity potential evaluation procedure and crop suitability evaluation framework of FAO. Two blocks from the sub mountain Siwalik region of Punjab viz., Mahalpur and Garhshankar were selected. This study evaluates the capabilities of the study area for traditional crops like wheat, paddy and maize, and recently introduced crops like sugarcane, sunflower, pea, rapeseed-mustard, potatoes and kinnow for agricultural diversification. The suitability of the crops has been worked out at the village level. About 35–40 per cent of total area mostly in Siwallik hills is not fit for growing any type of crop. Sandy texture, uneven topography, moderately steep slopes and excessive drainage are responsible for unsuitability of this area. The GIS based suitability analysis for traditional crops as well as for new crops, under diversification of agriculture has been undertaken. The geostatistical analysis points towards suitability of relatively large areas for new crops like sunflower, potato, pea (green) and sugarcane. Forty three and 14 per cent of total area has been found highly suitable and suitable respectively for growing green pea - a cash crop. Thirty three per cent of total area is suitable for growing kinnow fruit. The success of diversification programme is subject to logical government policy in terms of providing cold storage, food processing facility and marketing infrastructure.     

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.     

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water.     

Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed,Nepal

Integration of WorldView-2 satellite image with small footprint airborne LiDAR data for estimation of tree carbon at species level has been investigated in tropical forests of Nepal. This research aims to quantify and map carbon stock for dominant tree species in Chitwan district of central Nepal. Object based image analysis and supervised nearest neighbor classification methods were deployed for tree canopy retrieval and species level classification respectively. Initially, six dominant tree species (Shorea robusta, Schima wallichii, Lagerstroemia parviflora, Terminalia tomentosa, Mallotus philippinensis and Semecarpus anacardium) were able to be identified and mapped through image classification. The result showed a 76% accuracy of segmentation and 1970.99 as best average separability. Tree canopy height model (CHM) was extracted based on LiDAR’s first and last return from an entire study area. On average, a significant correlation coefficient (r) between canopy projection area (CPA) and carbon; height and carbon; and CPA and height were obtained as 0.73, 0.76 and 0.63, respectively for correctly detected trees. Carbon stock model validation results showed regression models being able to explain up to 94%, 78%, 76%, 84% and 78% of variations in carbon estimation for the following tree species: S. robusta, L. parviflora, T. tomentosa, S. wallichii and others (combination of rest tree species).     

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.     

Impact Assessment of GIS Based Land Resource Inventory Towards Optimizing Agricultural Land Use Plan in Dandakaranya and Easternghats Physiographic Confluence of India

GIS based land resource inventory (LRI) with fine resolution imagery is considered as most authentic tool for soil resource mapping. Soil resource mapping using the concept of soil series in a smaller scale limits its wide application and also its impact assessment for crop suitability is controversial. In this study, we attempted to develop LRI at large scale (1:10,000 scale) at block level land use planning (LUP) in Dandakaranya and Easternghats physiographic confluence of India. The concept of land management unit was introduced in this endeavour. The impact assessment of LRI based LUP was exercised to develop efficient crop planning with best possible management practices. The study area comprised six landforms with slope gradient ranging from very gentle (1–3%) to steep slopes (15–25%). The very gently sloping young alluvial plains occupied maximum areas (19.95% of TGA). The single cropped (paddy) land appears to dominate the land use systems (40.0% of TGA). Thirty three landscape ecological units were resulted by GIS-overlay. Eighteen soils mapping units were generated. The area was broadly under two soil orders (Inceptisols and Alfisols); three great group (Haplaquepts, Rhodustalfs and Endoaquepts) and ten soil series. Crop suitability based impact assessment of LRI based LUP revealed that average yield of different crops increased by 39.2 and 14.5% in Kharif (rainy season) and Rabi (winter) seasons respectively and annual net returns by 83.4% for the cropping system, compared to traditional practices. Productivity and net returns can be increased several folds if customized recommended practices are adopted by the farmers. Informations generated from the study emphasized the potentiality of LRI towards optimizing LUP and exhibited an ample scope to use the methodology as a tool to assess in other physiographic regions in India and abroad.     

Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]_Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]_Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI_{1310,1720,730}: R² = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]_Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.     

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal,South Africa

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.     

The dynamics in the distribution of mangrove forests in Pichavaram,South India – perception by user community and remote sensing

This study aims to monitor the forest cover of Pichavaram mangroves, South India over a period of 40 years using remote sensing, and to record the status of mangroves as perceived by the local community. Out of 1471 ha of total reserved forest area, mangroves occupy 906 ha. The remote sensing maps show that there was a loss of 471 ha from 1970 to 1991 and a gain of 531 ha in 2011. Nearby 20 hamlets depend on mangroves for their livelihood. A village survey conducted at Pichavaram shows that more than 90% of the local community is well aware of the prevailing species, their importance especially after the 2004 tsunami and the impact of management practices, increased rainfall and contribution of local community in the recent increased area of mangroves. The same can be noticed from the high-resolution IKONOS image showing the artificial canal network in the restored region and from rainfall records.