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

Abstract

Abstract. Consistent forest loss estimates are important to understand the role of forest loss and deforestation in the global carbon cycle, for biodiversity studies, and to estimate the mitigation potential of reducing deforestation. To date, most studies have relied on optical satellite data and new efforts have greatly improved our quantitative knowledge on forest dynamics. However, most of these studies yield results for only a relatively short time period or are limited to certain countries. We have quantified large-scale forest loss over a 21-year period (1990-2010) in the tropical biomes of South America using remotely sensed vegetation optical depth (VOD). This passive microwave satellitebased indicator of vegetation water content and vegetation density has a much coarser spatial resolution than optical data but its temporal resolution is higher and VOD is not impacted by aerosols and cloud cover. We used the merged VOD product of the Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave Imager (SSM/I) observations, and developed a change detection algorithm to quantify spatial and temporal variations in forest loss dynamics. Our results compared reasonably well with the newly developed Landsat-based Global Forest Change (GFC) maps, available for the 2001 onwards period (r 2 = 0.90 when comparing annual countrylevel estimates). This allowed us to convert our identified changes in VOD to forest loss area and compute these from 1990 onwards. We also compared these calibrated results to PRODES (r 2 = 0.60 when comparing annual state-level estimates). We found that South American forest exhibited substantial interannual variability without a clear trend during For a large part, these trends were driven by changes in Brazil, which was responsible for 56 % of the total South American forest loss area over our study period according to our results. One of the key findings of our study is that while forest loss decreased in Brazil after 2005, increases in other countries partly offset this trend suggesting that South American forest loss as a whole decreased much less than that in Brazil.

Recent global and regional trends in burned area and their compensating environmental controls

“…Our results indicate that such a shift towards coarser fuels contributed to the decline in burned area because they have a dampening effect on fire (Kahiu and Hanan 2018). On the other hand, vegetation trends in grasslands in southern Africa (increasing VOD) and in forests in South America (decreasing VOD with increasing FAPAR) indicate increases in herbaceous biomass and reductions in woody vegetation cover (Andela et al 2013, Marle et al 2016 which promotes fire spread and contributes to positive trends in burned area. This is consistent with earlier findings that pasture burning increases burned area after deforestation in the Amazon (Cano-Crespo et al 2015).…”

Fire and deforestation dynamics in Amazonia (1973–2014)

“…First, we compared our results to net forest loss based on passive-microwave satellite observations of vegetation optical depth (VOD), which has shown to be sensitive to changes in biomass density [Liu et al, 2015]. The VOD-based deforestation data set we used here [van Marle et al, 2016] provides annual estimates of net forest loss, which is the net result of deforestation, forest degradation, and regrowth within a grid cell, on a 0.25°× 0.25°resolution for all of South America from 1990 to 2010. Interannual VOD changes over the region agreed reasonably with the Global Forest Change data set derived from Landsat, during the overlapping period since 2001 [Hansen et al, 2013].…”

“…This forest loss data set captures mainly large-scale forest losses and in areas with many small-scale degradation events, VOD-based forest loss tends to overestimate, which could explain why the VOD-based interannual variability provides overall much higher values in 2003 to 2006 compared to the visibility-based fire emissions. Also, regrowth within a pixel is observed which can offset some of the signal [van Marle et al, 2016].…”

“…Satellite-based forest loss data sets show substantial interannual variability (IAV) over the 1990s and 2000s and an overall decrease after 2005 as a result of reduced deforestation rates in Brazil due to conservation policies, namely, stricter law enforcements and expansion of the governmental protection of the Amazon area [Nepstad et al, 2009;Hansen et al, 2013]. Although forest loss has decreased in Brazil, neighboring Argentina, Paraguay, and Bolivia show an increase over the 2000s [Chen et al, 2013;van Marle et al, 2016]. Nowadays, deforestation in the Amazon often goes hand in hand with fire, emitting particulate matter into the atmosphere, which reduces air quality and can affect human health [Johnston et al, 2012;Marlier et al, 2012].…”

An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa

“…Large-scale monitoring of vegetation properties is crucial to understand water, carbon and energy cycles. The Normalized Difference Vegetation Index (NDVI, Tucker, 1979) computed from space-borne observations at visible and infrared wavelengths has been widely used since the 1980s to study vegetation changes and their implications on animal ecology (Pettorelli et al, 2005(Pettorelli et al, , 2011, global fire emissions (van der Werf et al, 2010), deforestation and urban development (Esau et al, 2016), global patterns of land-atmosphere carbon fluxes (Jung et al, 2011) and the vegetation response to climate (Herrmann et al, 2005) and extreme events such as droughts (Vicente-Serrano et al, 2013). NDVI is sensitive to the abundance of chlorophyll and therefore to the photosynthetically active biomass (which includes herbaceous vege-tation and the leaves of trees) but insensitive to wood mass.…”