Publication: How can remote sensing help monitor tropical moist forest degradation?-A systematic review
How can remote sensing help monitor tropical moist forest degradation?-A systematic review
| dc.contributor.author | Dupuis, C. | |
| dc.contributor.author | Lejeune, P. | |
| dc.contributor.author | Michez, A. | |
| dc.contributor.author | Fayolle, A. | |
| dc.date.accessioned | 2022-01-23T18:57:12Z | |
| dc.date.available | 2022-01-23T18:57:12Z | |
| dc.identifier.uri | https://open.fsc.org/handle/resource/897 | |
| dc.title | How can remote sensing help monitor tropical moist forest degradation?-A systematic review | en |
| dcterms.abstract | In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth's species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders. | en |
| dcterms.accessRights | Public | |
| dcterms.accessRights | Open access | |
| dcterms.bibliographicCitation | Dupuis, C., Lejeune, P., Michez, A. and Fayolle, A., 2020. How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review. Remote Sensing, 12(7), p.1087. | en |
| dcterms.issued | 2019 | |
| dcterms.language | en | |
| dcterms.license | CC-BY-4.0 | en |
| dcterms.type | Journal Article | |
| dspace.entity.type | Publication | |
| is.availability.fullText | Full text available | |
| is.contributor.funderType | Private funds (NGOs, companies, VSS self-funded etc) | |
| is.coverage.geographicLevel | Region | |
| is.evaluation.collection | Mapping e.g. remote sensing | |
| is.evaluation.dataSource | Geospatial data layers | |
| is.evidenceSubType | Synthesis paper - systematic review | |
| is.evidenceType | Synthesis paper | |
| is.focus.products | Other forestry and logging | |
| is.focus.sdg | SDG 13 - Climate Action | |
| is.focus.sectors | Agriculture | |
| is.focus.sectors | Forestry | |
| is.focus.sustainDimension | Environmental | |
| is.focus.sustainIssue | Forests and other ecosystems | |
| is.focus.sustainLens | Innovation / innovative solutions | |
| is.focus.sustainOutcome | Deforestation and forest protection | |
| is.focus.systemElement | MandE outcomes and impacts | |
| is.focus.systemElement | MandE performance monitoring | |
| is.identifier.code | Impacts | |
| is.identifier.doi | https://doi.org/10.3390/rs12071087 | |
| is.identifier.fscdoi | http://dx.doi.org/10.34800/fsc-international408 | |
| is.identifier.schemeType | Voluntary Sustainability Standards | |
| is.item.reviewStatus | Peer reviewed | |
| is.journalName | Remote Sensing |