Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data

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Video in TIB AV-Portal: Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data

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Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data
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Mapping anthropogenic forest disturbances has largely been focused on distinct delineations of events of deforestation using optical satellite images. In the tropics, frequent cloud cover and the challenge of quantifying forest degradation remain problematic. In this study, we detect processes of deforestation, forest degradation and successional dynamics, using long-wavelength radar (L-band from ALOS PALSAR) backscatter. We present a detection algorithm that allows for repeated disturbances on the same land, and identifies areas with slow- and fast-recovering changes in backscatter in close spatial and temporal proximity. In the study area in Madre de Dios, Peru, 2.3% of land was found to be disturbed over three years, with a false positive rate of 0.3% of area. A low, but significant, detection rate of degradation from sparse and small-scale selective logging was achieved. Disturbances were most common along the tri-national Interoceanic Highway, as well as in mining areas and areas under no land use allocation. A continuous spatial gradient of disturbance was observed, highlighting artefacts arising from imposing discrete boundaries on deforestation events. The magnitude of initial radar backscatter, and backscatter decrease, suggested that large-scale deforestation was likely in areas with initially low biomass, either naturally or since already under anthropogenic use. Further, backscatter increases following disturbance suggested that radar can be used to characterize successional disturbance dynamics, such as biomass accumulation in lands post-abandonment. The presented radar-based detection algorithm is spatially and temporally scalable, and can support monitoring degradation and deforestation in tropical rainforests with the use of products from ALOS-2 and the future SAOCOM and BIOMASS missions.
Electric power distribution Magnetic core Emissionsvermögen Radar Force Crystal structure Aerodynamics Climate change Roll forming Stream bed Satellite Optics Speckle imaging Electric power distribution Wolkengattung Electronic component White Radar Engine knocking Climate Aerodynamics Video Signal (electrical engineering) Electronic media Relative dating
Typesetting Energiesparmodus Speckle imaging Bauxitbergbau Patch antenna Energiesparmodus Optics Effects unit Superheterodyne receiver Bauxitbergbau Year Radar Year Backscatter Radar Signal (electrical engineering) Orbital period Mass Satellite
Apparent magnitude Bauxitbergbau Satellite Hydraulic accumulator
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play in this media apps technical office and I'm presenting no you up to date on mapping the spatial distribution and dynamics of forests in including deforestation so far the contagion in special worth dynamic using that like imaging radar data tropical forests really importance of biodiversity for human welfare and regulating climate however because of market failures we don't value these forest properly leading excessive deforestation around the world and at about 17 to send forces to emissions which is driving climate change we have programs developing now around the world and the remainder of reducing emissions from deforestation and degradation to tackle this problem however to do so it's a core component is monitoring so we need to get images of the forest changing over time optical images the forest so often obscured by clouds and smoke however with radar the signal penetrates the cloud and the smoke and what's more interacts with the structure of the forest itself and this allows us to measure change along the gradient from partially degraded forest white fruits completely deforested site is located in the highly by a diverse region of my data deals in bed you know where land is allocated to specific users in the form of different land concessions with we'll
radar images from the als false satellite we were able to look at each patch of land repeatedly every year we designed an algorithm that identifies areas which are still recovering radar backscatter change that is areas where the radar signal remained consistently changed them back to the base year 2007 for a period of 2 years or more it then identifies serious fast-recovering radar backscatter change that is areas where the readout signal changes only for a period of 1 year the 2 types of changes fast and slow recovery and we then combined to produce an old for assistance mass for the study area we find that areas with different land use allocations different rates of forest disturbances conservation areas so more fast-recovering changes while mining areas some more slowly recovering changes but scattered changes falls
disturbances show that the type of the land use carried out effects radar signal recovery distant areas and forests recover faster while distant areas for mining recover slower finally we found that the
readout signal recovery depended on the area of land distant large area disturbances showed a larger magnitude backscattered dropped suggesting that these and deforestation however these also
recover by Fatah fast suggesting that biomass accumulation both disturbance is an important successional forced what we also use very exciting because it shows you use satellite realities that text deforestation and forest that relations traditionally sometimes used all schools are like these which samples from how politicians you can also see the forest degradation those of us who we using collecting violates
the see further assess how much variation
in the test that concludes with a statement that this in Europe launching new sideways in the near future in terms of reasonable his house to the success of the software used here it's you know going south of the island to the future and always with those of like last year in space agency that kind of mean but here should use all these news outlets so enabled this much better than