| Title: |
SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery |
| Authors: |
Ballère, Marie; Bouvet, Alexandre; Mermoz, Stéphane; Le Toan, Thuy; Koleck, Thierry; Bedeau, Caroline; André, Mathilde; Forestier, Elodie; Frison, Pierre-Louis; Lardeux, Cédric |
| Contributors: |
Laboratoire sciences et technologies de l'information géographique (LaSTIG); Ecole des Ingénieurs de la Ville de Paris (EIVP)-École nationale des sciences géographiques (ENSG); Institut National de l'Information Géographique et Forestière IGN (IGN)-Université Gustave Eiffel-Institut National de l'Information Géographique et Forestière IGN (IGN)-Université Gustave Eiffel; Centre National d'Études Spatiales Toulouse (CNES); Centre d'études spatiales de la biosphère (CESBIO); Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3); Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP); Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS); ONF - Direction régionale de la Guyane Cayenne; Office National des Forêts (ONF); Office National des Forêts International (ONFI) |
| Source: |
ISSN: 0034-4257. |
| Publisher Information: |
HAL CCSD; Elsevier |
| Publication Year: |
2020 |
| Subject Terms: |
Near real time deforestation; Sentinel-1; Forest alert; Optical-SAR comparison; Tropical forest; French Guiana; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [SDE.IE]Environmental Sciences/Environmental Engineering; [SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture; forestry; envir; geo |
| Description: |
International audience ; French Guiana forests cover 8 million hectares. With 98% of emerged land covered by forests, French Guiana is the area with the highest proportion of forest cover in the world. These forests are home to an exceptionally rich and diverse wealth of biodiversity that is both vulnerable and under threat due to high levels of pressure from human activity. As part of the French territory, French Guiana benefits from determined and continuous national efforts in the preservation of biodiversity and the environmental functionalities of ecosystems. The loss and fragmentation of forest cover caused by gold mining (legal and illegal), smallholder agriculture and forest exploitation, are considered as small-scale disturbances, although representing strong effects to vulnerable natural habitats, landscapes, and local populations. To monitor forest management programs and combat illegal deforestation and forest opening near-real time alerts system based on remote sensing data are required. For this large territory under frequent cloud cover, Synthetic-Aperture Radar (SAR) data appear to be the best adapted. In this paper, a method for forest alerts in a near-real time context based on Sentinel-1 data over the whole of French Guiana (83,534 km2) was developed and evaluated. The assessment was conducted for 2 years between 2016 and 2018 and includes comparisons with reference data provided by French Guiana forest organizations and comparisons with the existing University of Maryland Global Land Analysis and Discovery Forest Alerts datasets based on Landsat data. The reference datasets include 1,867 plots covering 2,124.5 ha of gold mining, smallholder agriculture and forest exploitation. The validation results showed high user accuracies (96.2%) and producer accuracies (81.5%) for forest loss detection, with the latter much higher than for optical forest alerts (36.4%). The forest alerts maps were also compared in terms of detection timing, showing systematic temporal delays of up to one year in the . |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://hal.archives-ouvertes.fr/hal-03272230/file/Ballere_2021_RSE.pdf; https://hal.archives-ouvertes.fr/hal-03272230 |
| Availability: |
https://hal.archives-ouvertes.fr/hal-03272230/file/Ballere_2021_RSE.pdf; https://hal.archives-ouvertes.fr/hal-03272230 |
| Rights: |
undefined |
| Accession Number: |
edsbas.85E17763 |
| Database: |
BASE |