Classifying drivers of global forest loss

Science, Journal Year: 2018, Volume and Issue: 361(6407), P. 1108 - 1111

Published: Sept. 13, 2018

Global maps of forest loss depict the scale and magnitude disturbance, yet companies, governments, nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary forestry or wildfire. Using satellite imagery, we developed a classification model determine spatial attribution disturbance dominant drivers land cover use change over period 2001 2015. Our results indicate that 27% global can be attributed deforestation through for commodity production. The remaining areas maintained same 15 years; in those areas, was (26%), shifting agriculture (24%), wildfire (23%). Despite corporate commitments, rate commodity-driven has not declined. To end deforestation, companies must eliminate 5 million hectares supply chains each year.

Language: Английский

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Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(17), P. 2735 - 2735

Published: Aug. 25, 2020

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently monitor Cerrado biome. However, there is still gap of land use cover (LULC) information all Brazilian biomes country. Existing countrywide efforts map lack regularly updates high spatial resolution time-series data better understand historical dynamics, subsequent impacts country biomes. In this study, we described novel approach results achieved by multi-disciplinary network called MapBiomas reconstruct between 1985 2017 for Brazil, based on random applied Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, water. These classes were broken into two sub-classification levels leading comprehensive detailed mapping at 30 m pixel resolution. The average overall accuracy time-series, stratified sample 75,000 locations, was 89% ranging from 73 95% 33 years LULC change series revealed that lost 71 Mha vegetation, mostly cattle ranching agriculture activities. Pasture expanded 46% 2017, 172%, replacing old pasture fields. also identified 86 converted native vegetation undergoing some level regrowth. Several applications dataset are underway, suggesting reconstructing maps useful advancing science guide social, economic environmental policy decision-making processes Brazil.

Language: Английский

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Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012

Proceedings of the National Academy of Sciences, Journal Year: 2015, Volume and Issue: 113(2), P. 344 - 349

Published: Dec. 28, 2015

The mangrove forests of Southeast Asia are highly biodiverse and provide multiple ecosystem services upon which millions people depend. Mangroves enhance fisheries coastal protection, store among the highest densities carbon any globally. Mangrove have experienced extensive deforestation owing to global demand for commodities, previous studies identified expansion aquaculture as largely responsible. proportional conversion mangroves different land use types has not been systematically quantified across Asia, however, particularly in recent years. In this study we apply a combined geographic information system remote sensing method quantify key proximate drivers (i.e., replacement uses) between 2000 2012. were lost at an average rate 0.18% per year, is lower than previously published estimates. total, more 100,000 ha removed during period, with accounting 30% total forest change. rapid rice agriculture Myanmar, sustained oil palm plantations Malaysia Indonesia, additional increasing under-recognized threats ecosystems. Our highlights frontiers border states on Borneo, Indonesian Papua. To implement policies that conserve it essential consider national subnational variation uses follow deforestation.

Language: Английский

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Optical remotely sensed time series data for land cover classification: A review

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2016, Volume and Issue: 116, P. 55 - 72

Published: March 23, 2016

Accurate land cover information is required for science, monitoring, and reporting. Land changes naturally over time, as well a result of anthropogenic activities. Monitoring mapping change in consistent robust manner large areas made possible with Earth Observation (EO) data. products satisfying range science policy needs are currently produced periodically at different spatial temporal scales. The increased availability EO data—particularly from the Landsat archive (and soon to be augmented Sentinel-2 data)—coupled improved computing storage capacity novel image compositing approaches, have resulted annual, large-area, gap-free, surface reflectance data products. In turn, these support development annual that can both informed constrained by detection outputs. inclusion time series process provides on class stability informs logical transitions (both temporally categorically). this review, we present issues opportunities associated generating validating time-series products, identify methods suited incorporating other inputs characterization.

Language: Английский

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The global distribution and trajectory of tidal flats

Nature, Journal Year: 2018, Volume and Issue: 565(7738), P. 222 - 225

Published: Dec. 19, 2018

Language: Английский

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Tropical forests are a net carbon source based on aboveground measurements of gain and loss

Science, Journal Year: 2017, Volume and Issue: 358(6360), P. 230 - 234

Published: Sept. 29, 2017

Forests out of balance Are tropical forests a net source or sink atmospheric carbon dioxide? As fundamental question as that is, there still is no agreement about the answer, with different studies suggesting it anything from sizable to modest source. Baccini et al. used 12 years MODIS satellite data determine how aboveground density woody, live vegetation has changed throughout entire tropics on an annual basis. They find are source, losses owing deforestation and reductions in within standing being double gains resulting forest growth. Science , this issue p. 230

Language: Английский

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From hotspot to hopespot: An opportunity for the Brazilian Atlantic Forest

Perspectives in Ecology and Conservation, Journal Year: 2018, Volume and Issue: 16(4), P. 208 - 214

Published: Oct. 1, 2018

New remote sensing data on vegetation cover and restoration opportunities bring hope to the Brazilian Atlantic Forest, one of hottest 36 global biodiversity hotspots. Available estimates remaining in biome currently range from 11% 16%. However, our new land-cover map, prepared at highest resolution ever (5 m), reveals a current 28%, or 32 million hectares (Mha) native vegetation. Simultaneously, we found 7.2 Mha degraded riparian areas, which 5.2 least must be restored before 2038 by landowners for legislation compliance. Restoring existing legal debt could increase Forest up 35%. Such effort, if well planned implemented, reduce extinction processes increasing connectivity remnants rising total above critical threshold established different taxonomic groups. If undertaken, this process can adaptive climate change boost sustainable development most populous Brazil, turning it into hopespot.

Language: Английский

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Land-cover classification with high-resolution remote sensing images using transferable deep models

Remote Sensing of Environment, Journal Year: 2019, Volume and Issue: 237, P. 111322 - 111322

Published: Nov. 27, 2019

Language: Английский

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Global declines in human‐driven mangrove loss

Global Change Biology, Journal Year: 2020, Volume and Issue: 26(10), P. 5844 - 5855

Published: July 12, 2020

Global mangrove loss has been attributed primarily to human activity. Anthropogenic hotspots across Southeast Asia and around the world have characterized ecosystem as highly threatened, though natural processes such erosion can also play a significant role in forest vulnerability. However, extent of threats not fully quantified at global scale. Here, using Random Forest-based analysis over one million Landsat images, we present first 30 m resolution maps drivers from 2000 2016, capturing both human-driven stressors. We estimate that 62% losses between 2016 resulted land-use change, through conversion aquaculture agriculture. Up 80% these occurred within six Asian nations, reflecting regional emphasis on enhancing for export support economic development. Both anthropogenic declined slower declines caused an increase their relative contribution total area. attribute decline regionally dependent combination increased conservation efforts lack remaining mangroves viable conversion. While restore protect appear be effective decadal timescales, emergence presents immediate challenge coastal adaptation. anticipate our results will inform decision-making restoration initiatives by providing locally relevant understanding causes loss.

Language: Английский

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GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

Earth system science data, Journal Year: 2021, Volume and Issue: 13(6), P. 2753 - 2776

Published: June 15, 2021

Abstract. Over past decades, a lot of global land-cover products have been released; however, these still lack map with fine classification system and spatial resolution simultaneously. In this study, novel 30 m for the year 2015 (GLC_FCS30-2015) was produced by combining time series Landsat imagery high-quality training data from GSPECLib (Global Spatial Temporal Spectra Library) on Google Earth Engine computing platform. First, were developed applying rigorous filters to CCI_LC (Climate Change Initiative Global Land Cover) MCD43A4 NBAR (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, local adaptive random forest model built each 5∘×5∘ geographical tile using multi-temporal spectral texture features corresponding data, GLC_FCS30-2015 product containing types generated tile. Lastly, validated three different validation systems (containing details) 44 043 samples. The results indicated that achieved an overall accuracy 82.5 % kappa coefficient 0.784 level-0 (9 basic types), 71.4 0.686 UN-LCCS (United Nations Cover Classification System) level-1 (16 LCCS 68.7 0.662 level-2 (24 types). comparisons against other (CCI_LC, MCD12Q1, FROM_GLC, GlobeLand30) provides more details than CCI_LC-2015 MCD12Q1-2015 greater diversity FROM_GLC-2015 GlobeLand30-2010. They also showed best 59.1 GlobeLand30-2010 75.9 %. Therefore, it is concluded first dataset 16 as well 14 detailed regional types) high at m. in paper are free access https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).

Language: Английский

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