Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

Naik, Parth; Dalponte, Michele; Bruzzone, Lorenzo

doi:10.3390/rs13071282

Cited by 40 publications

(23 citation statements)

References 76 publications

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“…Many studies have analyzed the spatiotemporal variations of vegetation AGB and their responses to climate change (Stegen et al, 2011 ; Shi et al, 2015 ; Castanho et al, 2020 ; Zhou et al, 2021 ). However, most of these studies focused on grassland and forest ecosystems, and only a few were conducted on marsh ecosystems (Flannigan et al, 2000 ; Shen et al, 2016 , 2022a ; Dai et al, 2021 ; Konings et al, 2021 ; Naik et al, 2021 ; Nandy et al, 2021 ; Qin et al, 2021 ; Wang et al, 2022b ). Compared with other ecosystems, marsh ecosystems have unique environmental conditions, which may lead to different influences of climate change on AGB (Wang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

et al. 2022

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The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVImax) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVImax and AGB density (AGB density = 643.57 × NDVImax4.2474). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m2/a during 2000–2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m2. Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

et al. 2022

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“…Las aplicaciones que el uso de LiDAR tiene en la gestión de recursos naturales y en la monitorización de su evolución tras intervenciones de diversa índole, ya sean presiones o restauración, son muy numerosas (Dilts et al, 2010;Johansen et al, 2010;Guo et al, 2017;Michez et al, 2017;Laslier et al, 2019;Huylenbroeck et al, 2021) y la previsible mejora en la frecuencia de vuelos así como en las resoluciones espaciales potenciaría enormemente su uso, aumentando notablemente la eficiencia en este tipo de tareas, si bien es cierto que la consideración de datos de campo continuará siendo necesaria como validación de este tipo de técnicas remotas. Además de la utilidad de la aplicación de LiDAR para este tipo de estudios, en los últimos años la combinación de esta tecnología con las imágenes ópticas multiespectrales ha permitido en el ámbito forestal estimar la evolución de variables dendométricas y dasométricas (biomasa, carbono o estructura de la vegetación) y distinción de especies a escala individual (Chasmer et al, 2017;Fang et al, 2018;Dalponte et al, 2019;Naik et al, 2021). Su aplicación para conocer la evolución de los corredores riparios y los efectos en la vegetación bajo escenarios distintos (regulación de caudales, régimen natural, cambio climático o espacios restaurados) podría ser de gran utilidad para la toma de decisiones por parte de los gestores de dichos espacios.…”

Section: Uso De Lidar E Imágenes Para Monitorizar Corredores Ripariosunclassified

Seguimiento ambiental en riberas mediante tecnología LiDAR

Alba¹,

Fernández

Jalón³

2022

CGUV

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Las metodologías y herramientas que permitan conocer la evolución del corredor ripario tras actuaciones de restauración ecológica, son imprescindibles para monitorizar la eficiencia de las acciones desarrolladas y el cumplimiento de los objetivos del proyecto. El uso de la tecnología LiDAR, la clasificación de imágenes y el uso de herramientas SIG, facilitan el seguimiento de las acciones de restauración. En este artículo se expone una metodología de seguimiento de la evolución del corredor ripario en el marco de un proyecto de restauración para un caso de estudio que consistió en la eliminación de un azud en el río Lozoya. Este seguimiento se ha basado en cuantificar los cambios en variables dendrométricas (altura, diámetro y biomasa) y dasométricas (Fracción de Cabida Cubierta) a partir dos coberturas LiDAR PNOA, realizadas en los años 2010 y 2016, y de la clasificación de imágenes RGB PNOA. Los resultados muestran cambios en el corredor ripario asociados al crecimiento natural, aumentando el número de pies de clases medias de altura y diámetro. Por el contrario, la fracción de cabida cubierta ha disminuido pasando de valores medios en torno al 60-70% a valores entre 40-50%, como resultado de la apertura de espacios abiertos generando un ensanchamiento notable de la zona de cauce activo. Los cambios más relevantes se observaron en un cauce secundario situado en la margen derecha y en las zonas limítrofes con la lámina de agua más próxima a la antigua ubicación del azud. Finalmente, se analizaron las respuestas del corredor a la recuperación de la conectividad, así como las limitaciones y ventajas de emplear esta metodología para analizar la evolución del corredor ripario tras actuaciones de restauración ecológica.

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“…Дальнейшая обработка данных проведена с помощью широко применяемой программы ArcGIS. Для определения районов, занятых густой растительностью, рассчитан нормализованный разностный индекс растительности NDVI (Normalized Difference Vegetation Index) -наиболее распространенный для решения задач, использующих количественные оценки растительного покрова, индекс [17]: NDVI = (NIR -RED) / (NIR + RED), где NIR и RED -отражения соответственно в ближней инфракрасной и красной областях спектра.…”

Section: объекты и методы исследованияunclassified

Changes in Forested Areas of the North-Eastern Region of Azerbaijan Revealed by Satellite Images

Mamedaliyeva¹

2022

Lesnoy Zhurnal (Forestry Journal)

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Among the most valuable natural resources of any country are its forest reserves. They need to be preserved. The need for forest protection also exists in Azerbaijan, where there is less forest per inhabitant than in neighboring countries. The use of modern data acquisition and processing methods as well as geographical information system technologies has reduced research in this area to a set of standard procedures. The article describes the stages of processing satellite images available in the public domain, using the case study of the north-eastern region of Azerbaijan, in order to compile forest cover maps over a number of different years. The Landsat images obtained during the summer seasons in different years from 1987 to 2018 are studied. The images covered the territory of 5 neighboring districts in the north-east of Azerbaijan. Preliminary processing of the images included radiometric calibration and atmospheric correction and was carried out using the ENVI software and the FLAASH module. The article also shows the final processing of images using the ArcGIS program in order to determine the areas covered by forests in different years. The analysis was based on the calculation of the Normalized Difference Vegetation Index (NDVI). The index was calculated for all considered satellite images. Then sections of images with high NDVI values were highlighted, vectorized, and the areas of the resulting polygons were found. Thus, a separate thematic layer is created for each year, showing the area of forest cover that year, i.e. 3 layers in total. The data obtained were summarized in a table, from which a diagram showing the dynamics of the forest area in the region was created. The data also became the basis for a thematic electronic map of forest loss. The continuation of this process has been described. For citation: Mamedaliyeva V.M. Changes in Forested Areas of the North-Eastern Region of Azerbaijan Revealed by Satellite Images. Lesnoy Zhurnal [Russian Forestry Journal], 2022, no. 1, pp. 88–97. DOI: 10.37482/0536-1036-2022-1-88-97

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Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

Cited by 40 publications

References 76 publications

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

Seguimiento ambiental en riberas mediante tecnología LiDAR

Changes in Forested Areas of the North-Eastern Region of Azerbaijan Revealed by Satellite Images

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