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  1. Article ; Online: Climatic conditions modulate the effect of spruce budworm outbreaks on black spruce growth

    Subedi, Anoj / Marchand, Philippe / Bergeron, Yves / Morin, Hubert / Girona, Miguel Montoro

    Agricultural and Forest Meteorology. 2023 Aug., v. 339 p.109548-

    2023  

    Abstract: Current ecological models predict profound climate change-related effects on the natural disturbance regimes of forests. Spruce budworm (Choristoneura fumiferana) (SBW) is the principal insect defoliator in eastern North America, and SBW outbreaks have a ...

    Abstract Current ecological models predict profound climate change-related effects on the natural disturbance regimes of forests. Spruce budworm (Choristoneura fumiferana) (SBW) is the principal insect defoliator in eastern North America, and SBW outbreaks have a major impact on the structure and function of the Canadian boreal forest, as defoliation leads to decreased tree growth, increased mortality, and lower forest productivity. SBW outbreaks have become more severe over the last century with the changing climate; however, little is known about how climate fluctuations affect the growth of SBW host species during the outbreak period. Here we evaluate how climate and outbreak severity combined to affect black spruce (Picea mariana) growth during the SBW outbreak that occurred between 1968–1988 and 2006–2017. We compiled dendrochronological series (2271 trees), outbreak severity (estimated by observed aerial defoliation), and climate data for 164 sites in Québec, Canada. We used a linear mixed effect model to determine the impacts of climatic parameters, cumulative defoliation (of the previous five years), and their coupled effect on basal area growth. At maximum outbreak severity, basal area growth of black spruce was reduced by 14%–18% over five years. This outbreak growth response was affected by climate: warmer previous summer minimum temperatures and a higher previous summer climate moisture index further decreased growth by 11% and 4%, respectively. In contrast, a preceding year's warmer spring minimum temperatures (9%) and summer maximum temperatures (7%) attenuated the negative SBW effect. This study adds knowledge to our landscape-level understanding of combined insect–climate effects and helps predictions of future SBW-related damage to forest stands to bolster sustainable forest management. We also recommend that projections of boreal forest ecosystems include several classes of SBW defoliation and multiple climatic scenarios in future simulations.
    Keywords Choristoneura fumiferana ; Picea mariana ; boreal forests ; climate ; defoliating insects ; defoliation ; dendrochronology ; hosts ; meteorological data ; meteorology ; models ; mortality ; spring ; summer ; sustainable forestry ; tree growth ; Quebec ; Climate change ; Disturbances ; Dendroecology ; Ecological modeling ; Forest management
    Language English
    Dates of publication 2023-08
    Publishing place Elsevier B.V.
    Document type Article ; Online
    ZDB-ID 409905-9
    ISSN 0168-1923
    ISSN 0168-1923
    DOI 10.1016/j.agrformet.2023.109548
    Database NAL-Catalogue (AGRICOLA)

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  2. Article ; Online: Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal.

    Acharya, Tri Dev / Subedi, Anoj / Lee, Dong Ha

    Sensors (Basel, Switzerland)

    2019  Volume 19, Issue 12

    Abstract: 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 ... ...

    Abstract 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.
    Language English
    Publishing date 2019-06-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s19122769
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal.

    Acharya, Tri Dev / Subedi, Anoj / Lee, Dong Ha

    Sensors (Basel, Switzerland)

    2018  Volume 18, Issue 8

    Abstract: 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 ... ...

    Abstract 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.
    Language English
    Publishing date 2018-08-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s18082580
    Database MEDical Literature Analysis and Retrieval System OnLINE

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