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

Abstract

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.

Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010)

“…This data set has a higher temporal, but coarser spatial resolution than Landsat, and is also sensitive to aerosols and cloud cover. Other vegetation data sets that can capture vegetation dynamics are for example the observations based on long-wavelength radar backscatter (Joshi et al, 2015), where deforestation, forest degradation and the follow-up vegetation cover could be captured, and those based on observations from the SeaWinds Ku-band scatterometer (Frolking et al, 2012), which have been shown to capture gross forest loss in the Tropics. Also lidar data can be used to estimate forest biomass, and can thus capture vegetation dynamics (Mitchard et al, 2012).…”

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

“…Understanding the processes of LUCC is of paramount importance towards more sustainable land management and will aid global initiatives, such as reducing emissions from deforestation and forest degradation (REDD+) [9,10]. However, quantifying LUCC remains a challenge, partly since the dynamics and trajectories of change are complex and fast-evolving [3,11] and partly since robust methods for analyses are still in development for many LUCC processes.…”

“…Remote sensors operate on a variety of basic physical principles, recording the electromagnetic properties of a land surface (either the energy reflected (optical sensors), emitted (thermal infrared or passive microwave sensors) or scattered (active radar sensors)) and, hence, provide a variety of information on land properties. However, considerable challenges to mapping LUCC using remote sensing data persist; the data are not always uniquely linked to land cover and are ambiguously related to land use, hence commonly requiring the use of heuristic, empirical, e.g., [11,27], or physically-based models [28] to infer land properties. Further, land use information must often be inferred based on integration with ground-knowledge or user interpretation [27,29].…”

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…Although a footprint size of 25 m will not enable the high resolution mapping that we are familiar with from small footprint LiDAR, the scientific objectives of the mission include modelling finer scale structure [133]. A combination of Landsat, EnMAP, FLEX, HySPIRI and GEDI LiDAR will combine structural and spectral information and improve the modelling, prediction and understanding of FH [134][135][136][137][138][139]. Examples of the main findings from LiDAR studies in forest analysis are given in Table 6.…”

“…Estimation of ST/STV of FH [14,16,138,139] Implementation of multi-sensor RS in modelling approaches improves the discrimination, quantification and accuracy when estimating ST/STV. [197] Multiple platforms for a given sensor type, e.g., the forthcoming Radarsat Constellation of three platforms provides potential for more frequent data acquisition.…”