Analysis of Change Detection of Birnin-Kudu Land Cover Using Image Classification And Vegetation Indices

The study utilizes Landsat-7 ETM+ based Normalized Differences Vegetation Index (NDVI) and Normalized Differences Water Index (NDWI) from 1972 to 2012 at the study area situated in Birnin Kudu, Jigawa state, in North-western Nigeria. The classified satellite data based NDVI of 1972, 1986, 2003 and 2012, including NDWI of 1986, 2003 and 2012 were used to determine land-cover change; vegetation and water body that have occurred in the study areas. This study attempts to use a comparative change detection analysis to produce the best way to quantify changes that has occurred in the study area with a lag time of 40 years (1972-2012) for NDVI and 26 years (1986-2012) for NDWI. The results of the classifications of NDVI and NDWI were displayed on satellite imagery, of which the percentage differences of change detected from variations of land cover/vegetation using NDVI of 1972-1986 is 15%, 1986-2003 is 40% and 2003-2012 is 11.6%. In the same vein, the result of percentage differences of change detected from variations of water bodies using NDWI of 1986-2003 is 0.03% and 2003-2012 is 1.5%. In the final analysis the change detected using NDVI for the period of 40 years (1972-2012) is 43.4%, while using NDWI for the periods of 26 years (1986-2012) is 1.47%. The study recommends periodic examination of land-use changes for determining various ecological and developmental consequences over time. The study area is of great environmental and economic importance having land cover rich in agricultural production and livestock grazing. Keywords: Analysis, change detection, land-cover, image classification algorithms, NDVI, NDW

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Landcover degradation analysis of Mediterranean forest by means of hyperplanes obtained from mixture linear algorithms (MLA)

The percentage alteration of the Mediterranean forest landscape is one of the primary indicators for its degradation. In this sense, the land cover abundances change analysis by using mixture linear algorithms (MLA), is presented like a good alternative to study this degradation. This research analyzes the use of two information sources like Remote Sensing (Landsat-ETM+) and Field Radiometry (GER 1500) to obtain mixture hyperplanes. These are calculated by models based on least square estimations, assuming that each pure land cover (endmember) belonging to any geographic area, behaves as a random variable which distribution function is known. The mixture hyperplanes provide spectral signatures with a suitable correlation level with regard to the supplied from remote satellite sensors once corrected, for the same geographical zone. These established hyperplanes can be used in future researches about Mediterranean forest landscape changes, because they can represent the different levels of its degradation. In this sense, it is proposed that they will feed a land cover spectral library with free accessibility