Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data

Slagter, Bart; Reiche, Johannes; Marcos, Diego; Mullissa, Adugna G.; Lossou, Etse; Peña-Claros, Marielos; Herold, Martin

doi:10.1016/j.rse.2023.113655

Cited by 24 publications

(9 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, the approach presented here could be operationalized, upscaled, and added to existing platforms (e.g., Global Forest Watch) to complement the existing monitoring methods currently in place. Documenting the drivers of deforestation would also be possible by expanding on this approach [41].…”

Section: Discussionmentioning

confidence: 99%

Crowd-Driven Deep Learning Tracks Amazon Deforestation

McCallum,

Walker,

Fritz

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring.

show abstract

Section: Discussionmentioning

confidence: 99%

Crowd-Driven Deep Learning Tracks Amazon Deforestation

McCallum,

Walker,

Fritz

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In SSA, despite substantial efforts towards protection, small-scale agriculture remains a primary proximate driver of both deforestation and degradation [6][7][8]11,20,42,[45][46][47]. Other common drivers include subsistence extraction activities like fuelwood collection, infrastructure development, small-scale and industrial mining, and natural or anthropogenic fires, many of which are frequently found in combination [2,11,42,48,49].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Institutional, governance, conflict, and other social, political, and economic factors that influence forest change also segue into factors that influence protected areas effectiveness [18,31,61]. Conflict, for example, can have varying influences on forest cover, in some cases increasing degradation and, in other cases, reducing degradation via internal displacement and reduced pressure on a forest system [42,43,49,55,61]. Yet conflict has resulted in enforcement challenges and de-prioritization for Central African protected areas in regions of the DRC and in conflicted areas of CMR and CAR (Figure 2) [62].…”

Section: Literature Reviewmentioning

confidence: 99%

Transboundary Central African Protected Area Complexes Demonstrate Varied Effectiveness in Reducing Predicted Risk of Deforestation Attributed to Small-Scale Agriculture

Bernhard,

Shapiro,

d’Annunzio

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The forests of Central Africa constitute the continent’s largest continuous tract of forest, maintained in part by over 200 protected areas across six countries with varying levels of restriction and enforcement. Despite protection, these Central African forests are subject to a multitude of overlapping proximate and underlying drivers of deforestation and degradation, such as conversion to small-scale agriculture. This pilot study explored whether transboundary protected area complexes featuring mixed resource-use restriction categories are effective in reducing the predicted disturbance risk to intact forests attributed to small-scale agriculture. At two transboundary protected area complex sites in Central Africa, we used Google Earth Engine and a suite of earth observation (EO) data, including a dataset derived using a replicable, open-source methodology stemming from a regional collaboration, to predict the increased risk of deforestation and degradation of intact forests caused by small-scale agriculture. For each complex, we then statistically compared the predicted increased risk between protected and unprotected forests for a stratified random sample of 2 km sites (n = 4000). We found varied effectiveness of protected areas for reducing the predicted risk of deforestation and degradation to intact forests attributed to agriculture by both the site and category of protected areas within the complex. Our early results have implications for sustainable agriculture development, forest conservation, and protected areas management and provide a direction for future research into spatial planning. Spatial planning could optimize the configuration of protected area types within transboundary complexes to achieve both forest conservation and sustainable agricultural production outcomes.

show abstract

“…Este abordaje integral podría utilizarse para luchar contra el aprovechamiento ilegal. Cabe señalar que, hasta hace poco, era difícil detectar los disturbios forestales a pequeña escala causadas por el aprovechamiento selectivo utilizando imágenes de satélite; sin embargo, con el lanzamiento de nuevos sensores y el desarrollo de métodos de aprendizaje profundo, la degradación forestal puede detectarse ahora con gran detalle espacial casi en tiempo real 51,52 . Estos avances ofrecen nuevas oportunidades de utilizar detección remota para identificar aprovechamiento forestal ilegal dentro y fuera de las áreas bajo manejo designadas legalmente.…”

Section: La Necesidad De Detener El Aprovechamineto Ilegal Y La Defor...unclassified

Gestión forestal para la producción de madera y restauración del paisaje forestal en la Amazonía

Sist*,,

Peña-Claros,

Baldiviezo Calles

et al. 2023

View full text Add to dashboard Cite

Las directrices actuales para el aprovechamiento legal de madera de bosques naturales en la Amazonía (alrededor de 20 m3 ha-1 de madera aprovechada cada 15-35 años) no son sostenibles. El creciente interés por la restauración de bosques tropicales ofrece oportunidades para promover la producción de madera a través de la gestión de bosques secundarios, bosques degradados y plantaciones mixtas con especies nativas.

show abstract

Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and -2 data

Cited by 24 publications

References 58 publications

Crowd-Driven Deep Learning Tracks Amazon Deforestation

Crowd-Driven Deep Learning Tracks Amazon Deforestation

Transboundary Central African Protected Area Complexes Demonstrate Varied Effectiveness in Reducing Predicted Risk of Deforestation Attributed to Small-Scale Agriculture

Gestión forestal para la producción de madera y restauración del paisaje forestal en la Amazonía

Contact Info

Product

Resources

About