5 research outputs found

    √Čvaluation de l'impact du changement climatique sur la d√©foliation de l'√©pinette noire par la tordeuse des bourgeons de l'√©pinette

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    Les mod√®les √©cologiques actuels pr√©voient de profonds effets des changements climatiques sur les r√©gimes de perturbations naturelles des for√™ts. La tordeuse des bourgeons de l'√©pinette (Choristoneura fumiferana) (TBE) est le principal insecte d√©foliateur dans l'est de l'Am√©rique du Nord. Les √©pid√©mies de TBE ont un impact majeur sur la structure et la fonction de la for√™t bor√©ale canadienne puisque la d√©foliation entra√ģne une diminution de la croissance des arbres, une augmentation de la mortalit√© et une baisse de la productivit√© foresti√®re. Les √©pid√©mies de TBE sont devenues plus s√©v√®res au cours du dernier si√®cle √† cause des changements climatiques; cependant, nous savons peu de choses sur la mani√®re dont l'effet int√©gr√© du climat et du TBE modifie la croissance des esp√®ces h√ītes. Nous √©valuons ici comment l‚Äôinteraction entre le climat et la gravit√© de l'√©pid√©mie affecte la croissance de l'√©pinette noire (Picea mariana) pendant l'√©pid√©mie de TBE qui a eu lieu entre 1968-1988 et 2006-2017. Nous avons compil√© des s√©ries dendrochronologiques (2271 arbres), des donn√©es de s√©v√©rit√© de l'√©pid√©mie (estim√©e par la d√©foliation a√©rienne observ√©e) et des donn√©es climatiques pour 164 sites au Qu√©bec, Canada. Nous avons utilis√© un mod√®le lin√©aire √† effets mixtes pour d√©terminer l'impact des param√®tres climatiques, de la d√©foliation cumulative (des cinq ann√©es pr√©c√©dentes) et de leur effet coupl√© sur la croissance en surface terri√®re. √Ä la gravit√© maximale de l'√©pid√©mie, la croissance en surface terri√®re de l'√©pinette noire a √©t√© r√©duite de 14 √† 18 % sur les cinq ann√©es en raison de l'effet TBE. Cette croissance a √©t√© affect√©e par le climat : des temp√©ratures minimales estivales pr√©c√©dentes plus √©lev√©es et un indice d'humidit√© climatique estival plus √©lev√© ont r√©duit la croissance de 11 % et 4 % respectivement. En revanche, l'effet n√©gatif de la d√©foliation a √©t√© att√©nu√© de 9% pour une temp√©rature minimale plus √©lev√©e au printemps pr√©c√©dent et de 7% pour une temp√©rature maximale plus √©lev√©e l'√©t√© pr√©c√©dent. Cette √©tude am√©liore notre compr√©hension des effets combin√©s de la TBE et du climat et aide √† pr√©voir les dommages futurs caus√©s par cet insecte dans les peuplements forestiers afin de soutenir la gestion durable des for√™ts. Nous recommandons √©galement que les projections des √©cosyst√®mes dans la for√™t bor√©ale incluent plusieurs classes de d√©foliation de la TBE et plusieurs sc√©narios climatiques

    Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

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    With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance

    Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

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    Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes

    Combining Water Indices for Water and Background Threshold in Landsat Image

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    Accurate and frequent update of surface water has been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from background based on a threshold value. Generally threshold is a fixed value but can be challenging in case of shades, hills, forest and urban areas. In such case, determination of threshold is done manually which is subjective and areal variation. In order to widen the difference between water and background with easier threshold selection, a combination of multiple water indices can be used. This could widen the gap between classes and the threshold sensitivity can be minimized. In this study, we summed Normalized Difference Water Index (NDWI), Modified NDWI, Water Ratio Index and Normalized Difference Vegetation Index to form a new raster and explore the efficiency of thresholding compared to individual indices on Landsat 8 Operational Land Imager (OLI) image of Nepal. The combined index showed much better separation of water with background and can be further used for automated binary classification of surface water. The process could be very useful in mapping surface water accurately
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