Land use/land cover change dynamics along the estuarine complex of Mandovi–Zuari in the coastal state of Goa, India is studied by the application of multi-temporal remote sensing data. This tropical estuarine region has faced significant changes in the last few decades due to diverse pressures from various sectors, mainly of anthropogenic origin. Spatial and temporal changes in the LU/LC classes have been evaluated using satellite datasets from Landsat MSS, TM and IRS-LISS III for the years 1973, 1989, 2001 and 2011. Considering a spectrally heterogeneous landscape of the region, the LU/LC changes are identified by using principal component analysis (PCA) as a pre-classification step, followed by the implementation of a combination of supervised and unsupervised classification called hybrid classification. The application of PCA has proved to be suitable to successfully identify discrete land surface classes for this study area. An overall accuracy of 95.56 % (kappa = 0.9556), 92.93 % (kappa = 0.92), 84.64 % (kappa = 0.85) and 86.36 % (kappa = 0.85) has been obtained for the year 2011, 2001, 1989 and 1973, respectively. The study reveals a gradual increase in built-up area due to urbanization corresponding to a decrease in agricultural land and a considerable loss in beach area. Changes in the mangroves are also assessed within this extent as Goa has significant pockets of mangroves which are located mainly along the estuarine complexes of Mandovi and Zuari (along with the Cumbarjua canal complex). The results highlight an increase in mangrove coverage within the study extent by 1,585.8 ha from 1973 to 2011. The mangrove areal extent along Mandovi has increased from 149.45 to 1,059.77 ha and from 142.95 to 676.12 ha along Zuari (including Cumbarjua canal). Increasing awareness and the steps taken by the forest department to provide suitable environment for the growth of mangroves are the main reasons for this increment.

Assessment of the land use/land cover (LU/LC) and mangrove changes along the Mandovi–Zuari estuarine complex of Goa, India

The rapid advent in geoinformation technologies, such as Earth Observation (EO) and Geographical Information Systems (GIS), has made it possible to observe and monitor the Earth’s environment on variable geographical scales and analyze those changes in both time and space. This study explores the synergistic use of Landsat EO imagery and Support Vector Machines (SVMs) in obtaining Land Use/Land Cover (LULC) mapping and quantifying its spatio-temporal changes for the municipality of Mandra–Idyllia, Attica Region, Greece. The study area is representative of typical Mediterranean landscape in terms of physical structure and coverage of species composition. Landsat TM (Thematic Mapper) images from 1993, 2001 and 2010 were acquired, pre-processed and classified using the SVMs classifier. A total of nine basic classes were established. Eight spectral band ratios were created in order to incorporate them in the initial variables of the image. For validating the classification, in-situ data were collected for each LULC type during several field surveys that were conducted in the area. The overall classification accuracy for 1993, 2001 and 2010 Landsat images was reported as 89.85%, 91.01% and 90.24%, respectively, and with a statistical factor (K) of 0.96, 0.89 and 0.99, respectively. The classification results showed that the total extent of forests within the studied period represents the predominant LULC, despite the intense human presence and its impacts. A marginal change happened in the forest cover from 1993 to 2010, although mixed forest decreased significantly during the studied period. This information is very important for future management of the natural resources in the studied area and for understanding the pressures of the anthropogenic activities on the natural environment. All in all, the present study demonstrated the considerable promise towards the support of geoinformation technologies in sustainable environmental development and prudent resource management.

/pdf/quantifying-land-cover-changes-in-a-mediterranean-1m9t01gfr0.pdf

Quantifying Land Cover Changes in a Mediterranean Environment Using Landsat TM and Support Vector Machines

Information about conifer understory within deciduous-dominated mixed-wood stands would increase the possible range of management options for these complex communities, yet cost-effective and accur...

Mapping conifer understory within boreal mixedwoods from Landsat TM satellite imagery and forest inventory information

In Pakistan in spite of few attempts for mapping land-cover types, satellite remotely sensed data has not been used extensively; and its potential is not being explored for providing information on mapping vegetation cover in general and ecological communities in particular. In this study, we used SPOT(Systeme Pour l'Observation de la Terre) multispectral (XS) satellite sensor data in visible and near infrared portion of the light spectrum as a surrogate for distribution of ecological vegetation groups defined by the classification and ordination methods (the most commonly used multivariate techniques used in floristic composition classification in vegetation ecology) and non-vegetation classes. The results indicate that classification of vegetation groups based on species composition identified using classification and ordination techniques to some extent resemble to those groups classified using SPOT XS data with least accuracy in comparison to non-vegetation classes which were more homogenous and spectrally separable and were classified more accurately in comparison. Two classification models i.e. supervised maximum likelihood and fuzzy supervised classification showed similar overall level of accuracies. The possibilities of lower classification accuracies and difficulties of classifying ecological communities based on the species composition using remotely sensor data are discussed.

Spatial distribution of ecological communities using remotely sensed data

Une methode de cartographie de la vegetation basee sur l'utilisation de donnees multidates Landsat-TM et de donnees topographiques est presentee. Vingt-huit classes de vegetation et de couvert, calquees sur un systeme de classification base sur la photo-interpretation generalement utilise pour la cartographie de la vegetation, ont ete testees et evaluees dans une region du sud de la Suede. La methode utilise une approche stratifiee ou l'information de la carte topographique est integree pour creer des masques pour la classification dans un contexte ou seulement un nombre limite de classes pertinentes est alloue en cours du processus de calcul. Cette technique permet de reduire le nombre potentiel de classifications erronees entre les classes. Le choix des sites d'entrainement et des dates d'enregistrement est d'une importance primordiale pour obtenir de bons resultats. Les resultats ont ete evalues utilisant 840 points choisis aleatoirement et une comparaison a ete effectuee avec une carte de vegetation de la region derivee de la photo-interpretation. Cet article decrit les etapes principales de la methode proposee et on discute du contenu thematique obtenu avec la methode basee sur les donnees satellitaires comparativement a la methode basee sur la photo-interpretation.

Mapping Boreal Vegetation Using Landsat-TM and Topographic Map Data in a Stratified Approach

Landsat Thematic Mapper data, collected over central Michigan in the U.S.A., in October 1982, were digitally analysed to determine qualitatively and quantitatively their utility and potential to classify nine natural resources categories (e.g. red pine, jack pine, scotch pine, low conifers, hardwoods, grassland, water, wetland and other). Supervised classification with a maximum likelihood decision rule was employed to 22 especially selected single-, two-, three-, four- and six-band combinations (thermal band was excluded). Analysis of the six-band combination indicated an overall classification accuracy of 92.4 per cent. The producer's classification accuracy of individual categories was 79.7 per cent (scotch pine), 80.7 per cent (lowland conifers), 80.8 per cent (red pine), 88.7 per cent (jack pine), 92.8 per cent (wetland), 96.3 per cent (grassland), 96.6 per cent (hardwoods), 78.5 per cent (other) and 1000 per cent (water). After aggregation of pine categories the accuracy of the new category...

The utility of digital Thematic Mapper data for natural resources classification

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The utility of digital Thematic Mapper data for natural resources classification