Background： Estimation of tree diversity at broader scale is important for conservation planning. Tree diversity should be measured and understood in terms of diversity and evenness, two integral components to descri...Background： Estimation of tree diversity at broader scale is important for conservation planning. Tree diversity should be measured and understood in terms of diversity and evenness, two integral components to describe the structure of a biological community. Variation of the tree diversity and evenness with elevation, topographic relief, aspect, terrain shape, slope, soil nutrient, solar radiation etc. are well documented. Methods： Present study explores the variation of tree diversity （measured as Shannon diversity and evenness indices） of Majella National Park, italy with five available forest types namely evergreen oak woods, deciduous oak woods, blacWaleppo pine stands, hop-hornbeam forest and beech forest, using satellite, environmental and field data. Results： Hop-hornbeam forest was found to be most diverse and even while evergreen Oak woods was the lowest diverse and even. Diversity and evenness of forest types were concurrent to each other i.e. forest type which was more diverse was also more even. As a broad pattern, majority portion of the study area belonged to medium diversity and high evenness class. Conclusions： Satellite images and other GIS data proved useful tools in monitoring variation of tree diversity and evenness across various forest types. Present study findings may have implications in prioritizing conservation zones of high tree diversity at Majella.展开更多
We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised clas...We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.展开更多
文摘Background： Estimation of tree diversity at broader scale is important for conservation planning. Tree diversity should be measured and understood in terms of diversity and evenness, two integral components to describe the structure of a biological community. Variation of the tree diversity and evenness with elevation, topographic relief, aspect, terrain shape, slope, soil nutrient, solar radiation etc. are well documented. Methods： Present study explores the variation of tree diversity （measured as Shannon diversity and evenness indices） of Majella National Park, italy with five available forest types namely evergreen oak woods, deciduous oak woods, blacWaleppo pine stands, hop-hornbeam forest and beech forest, using satellite, environmental and field data. Results： Hop-hornbeam forest was found to be most diverse and even while evergreen Oak woods was the lowest diverse and even. Diversity and evenness of forest types were concurrent to each other i.e. forest type which was more diverse was also more even. As a broad pattern, majority portion of the study area belonged to medium diversity and high evenness class. Conclusions： Satellite images and other GIS data proved useful tools in monitoring variation of tree diversity and evenness across various forest types. Present study findings may have implications in prioritizing conservation zones of high tree diversity at Majella.
文摘We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP.
