A machine learning approach to map tropical selective logging
Open accessPublished version of a preprint
Abstract
Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m3 ha−1). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (<15 m3 ha−1). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML can be used with remote sensing imagery to pinpoint changes in forest cover, or proxies for deforestation such as smoke from fires set to clear vegetation, as well as to differentiate selective cutting from clearcutting.
“ML can be used with remote sensing imagery to pinpoint changes in forest cover [241,329,369], or proxies for deforestation such as smoke from fires set to clear vegetation [505], as well as to differentiate selective cutting from clearcutting [342,487].”
From Tackling climate change with machine learning (2023) · cites this paper as [342]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704 · see citing works
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Bello, U. D., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,906 · see citing works
Good practices for estimating area and assessing accuracy of land change
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.02.015 · Cited by 3,134 · see citing works
Landsat-8: Science and product vision for terrestrial global change research
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D. L., Irons, J. R., Johnson, D. M., Kennedy, R. E., et al. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.02.001 · Cited by 2,723 · see citing works
Primary forests are irreplaceable for sustaining tropical biodiversity
Gibson, L., Lee, T. M., Koh, L. P., Brook, B. W., Gardner, T. A., Barlow, J., Peres, C. A., Bradshaw, C. J. A., Laurance, W. F., Lovejoy, T. E., et al. (2011) · Nature · doi:10.1038/nature10425 · Cited by 2,147 · see citing works
Statistical signal processing : detection, estimation, and time series analysis
Scharf, L. L., & Demeure, C. J. (1991) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1,869 · see citing works
Deforestation driven by urban population growth and agricultural trade in the twenty-first century
DeFries, R. S., Rudel, T., Uriarte, M., & Hansen, M. (2010) · Nature Geoscience · doi:10.1038/ngeo756 · Cited by 1,413 · see citing works
An assessment of deforestation and forest degradation drivers in developing countries
Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., Angelsen, A., & Romijn, E. (2012) · Environmental Research Letters · doi:10.1088/1748-9326/7/4/044009 · Cited by 1,366 · see citing works
Large-scale impoverishment of Amazonian forests by logging and fire
Nepstad, D. C., Verssimo, A., Alencar, A. A. C., Nobre, C. A., Lima, E., Lefebvre, P., Schlesinger, P., Potter, C., Moutinho, P. R. S., Mendoza, E., et al. (1999) · Nature · doi:10.1038/19066 · Cited by 1,341 · see citing works
Long-term decline of the Amazon carbon sink
Brienen, R. J. W., Phillips, O. L., Feldpausch, T. R., Gloor, E., Baker, T. R., Lloyd, J., Lopez-Gonzalez, G., Monteagudo-Mendoza, A., Malhi, Y., Lewis, S. L., et al. (2015) · Nature · doi:10.1038/nature14283 · Cited by 1,211 · see citing works
Selective Logging in the Brazilian Amazon
Asner, G. P., Knapp, D., Broadbent, E. N., Oliveira, P. J. C., Keller, M., & Silva, J. N. M. (2005) · Science · doi:10.1126/science.1118051 · Cited by 1,051 · see citing works
Tropical forests are a net carbon source based on aboveground measurements of gain and loss
Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., & Houghton, R. A. (2017) · Science · doi:10.1126/science.aam5962 · Cited by 904 · see citing works
Increasing human dominance of tropical forests
Lewis, S. L., Edwards, D. P., & Galbraith, D. R. (2015) · Science · doi:10.1126/science.aaa9932 · Cited by 866 · see citing works
Self-citationIndian Hedgehog: A Mechanotransduction Mediator in Condylar Cartilage
Tang, G. H., Rabie, A. B. M., & Hägg, U. (2004) · Journal of Dental Research · doi:10.1177/154405910408300516 · Cited by 819 · see citing works
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., et al. (2016) · Remote Sensing · doi:10.3390/rs8010070 · Cited by 685 · see citing works
Global forest loss disproportionately erodes biodiversity in intact landscapes
Betts, M. G., Wolf, C., Ripple, W. J., Phalan, B., Millers, K. A., Duarte, A., Butchart, S. H. M., & Levi, T. (2017) · Nature · doi:10.1038/nature23285 · Cited by 637 · see citing works
Does REDD+ Threaten to Recentralize Forest Governance?
Phelps, J., Webb, E., & Agrawal, A. (2010) · Science · doi:10.1126/science.1187774 · Cited by 621 · see citing works
Rodriguez-Galiano, V. F., Chica‐Olmo, M., Abarca-Hernández, F., Atkinson, P. M., & Jeganathan, C. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.12.003 · Cited by 593 · see citing works
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Muñoz-Marı́, J. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2139193 · Cited by 557 · see citing works
A Contemporary Assessment of Change in Humid Tropical Forests
Asner, G. P., Rudel, T. K., Aide, T. M., DeFries, R., & Emerson, R. (2009) · Conservation Biology · doi:10.1111/j.1523-1739.2009.01333.x · Cited by 458 · see citing works
Greenhouse gas emissions from tropical forest degradation: an underestimated source
Pearson, T. R. H., Brown, S., Murray, L. T., & Sidman, G. (2017) · Carbon Balance and Management · doi:10.1186/s13021-017-0072-2 · Cited by 434 · see citing works
Maintaining ecosystem function and services in logged tropical forests
Edwards, D. P., Tobias, J. A., Sheil, D., Meijaard, E., & Laurance, W. F. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.07.003 · Cited by 428 · see citing works
Self-citationSasaki, N., & Putz, F. E. (2009) · Conservation Letters · doi:10.1111/j.1755-263x.2009.00067.x · Cited by 419 · see citing works
A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance
Roy, D. P., Zhang, H. K., Ju, J., Gomez-Dans, J. L., Lewis, P., Schaaf, C., Sun, Q., Li, J., Huang, H., & Kovalskyy, V. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.01.023 · Cited by 402 · see citing works
Júnior, C. M. D. S., Roberts, D. A., & Cochrane, M. A. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2005.07.013 · Cited by 382 · see citing works
Thresholds of Logging Intensity to Maintain Tropical Forest Biodiversity
Buřivalová, Z., Sekercioglu, C. H., & Koh, L. P. (2014) · Current Biology · doi:10.1016/j.cub.2014.06.065 · Cited by 325 · see citing works
Conceptualizing Forest Degradation
Ghazoul, J., Buřivalová, Z., Garcia‐Ulloa, J., & King, L. A. (2015) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2015.08.001 · Cited by 324 · see citing works
Tropical Forest Management and Conservation of Biodiversity: an Overview
Putz, F. E., Blate, G. M., Redford, K. H., Fimbel, R. A., & Robinson, J. G. (2001) · Conservation Biology · doi:10.1046/j.1523-1739.2001.00018.x · Cited by 322 · see citing works
Humid tropical forest disturbance alerts using Landsat data
Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., & Moore, R. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/3/034008 · Cited by 303 · see citing works
An Operational Framework for Defining and Monitoring Forest Degradation
Thompson, I. D., Guariguata, M. R., Okabe, K., Bahamóndez, C., Nasi, R., Heymell, V., & Sabogal, C. (2013) · Ecology and Society · doi:10.5751/es-05443-180220 · Cited by 302 · see citing works
Automated mapping of tropical deforestation and forest degradation: CLASlite
Asner, G. P., Knapp, D. E., Balaji, A., & Paez-Acosta, G. (2009) · Journal of Applied Remote Sensing · doi:10.1117/1.3223675 · Cited by 293 · see citing works
Improved Tropical Forest Management for Carbon Retention
Putz, F., Zuidema, P., Pinard, M., Boot, R., Sayer, J., Sheil, D., Sist, P., Elias, & Vanclay, J. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060166 · Cited by 286 · see citing works
Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon
Júnior, C. M. D. S., Siqueira, J. V., Sales, M., Fonseca, A. V., Ribeiro, J. N. D. A., Numata, I., Cochrane, M. A., Barber, C., Roberts, D. A., & Barlow, J. (2013) · Remote Sensing · doi:10.3390/rs5115493 · Cited by 286 · see citing works
Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. H., & Herold, M. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.10.034 · Cited by 279 · see citing works
Economic, Socio-Political and Environmental Risks of Road Development in the Tropics
Alamgir, M., Campbell, M. J., Sloan, S., Goosem, M., Clements, G. R., Mahmoud, M. I., & Laurance, W. F. (2017) · Current Biology · doi:10.1016/j.cub.2017.08.067 · Cited by 275 · see citing works
CANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES
Asner, G. P., Keller, M., Pereira, R. M., Zweede, J., & Silva, J. N. M. (2004) · Ecological Applications · doi:10.1890/01-6019 · Cited by 223 · see citing works
Remote sensing of selective logging in Amazonia
Asner, G. P., Keller, M., Pereira, R. M., & Zweede, J. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00326-1 · Cited by 214 · see citing works
Assessment of tropical forest degradation by selective logging and fire using Landsat imagery
Matricardi, E. A. T., Skole, D. L., Pedlowski, M. A., Chomentowski, W. H., & Fernandes, L. C. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.01.001 · Cited by 195 · see citing works
Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit
Morfitt, R. A., Barsi, J. A., Levy, R., Markham, B. L., Micijevic, E., Ong, L., Scaramuzza, P. L., & Vanderwerff, K. (2015) · Remote Sensing · doi:10.3390/rs70202208 · Cited by 168 · see citing works
Asner, G. P., Keller, M., & Silva, J. N. M. (2004) · Global Change Biology · doi:10.1111/j.1529-8817.2003.00756.x · Cited by 166 · see citing works
Fake legal logging in the Brazilian Amazon
Brancalion, P. H. S., de Almeida, D. R. A., Vidal, E., Molin, P. G., Sontag, V. E., de Souza, S. E. X. F., & Schulze, M. (2018) · Science Advances · doi:10.1126/sciadv.aat1192 · Cited by 165 · see citing works
Pan-tropical monitoring of deforestation
Achard, F., DeFries, R., Eva, H. D., Hansen, M. C., Mayaux, P., & Stibig, H. J. (2007) · Environmental Research Letters · doi:10.1088/1748-9326/2/4/045022 · Cited by 160 · see citing works
Quantifying mortality of tropical rain forest trees using high‐spatial‐resolution satellite data
Clark, D. B., Castro, C. S., Alvarado, L. D. A., & Read, J. M. (2003) · Ecology Letters · doi:10.1046/j.1461-0248.2003.00547.x · Cited by 133 · see citing works
Improved timber harvest techniques maintain biodiversity in tropical forests
Bicknell, J. E., Struebig, M. J., Edwards, D. P., & Davies, Z. G. (2014) · Current Biology · doi:10.1016/j.cub.2014.10.067 · Cited by 130 · see citing works
Self-citationReduced‐Impact Logging as a Carbon‐Offset Method
Putz, F. E., & Pinard, M. A. (1993) · Conservation Biology · doi:10.1046/j.1523-1739.1993.7407551.x · Cited by 122 · see citing works
An alternative approach for detecting and monitoring selectively logged forests in the Amazon
Júnior, C. M. D. S., & Barreto, P. (2000) · International Journal of Remote Sensing · doi:10.1080/014311600211064 · Cited by 116 · see citing works
Impacts of tropical selective logging on carbon storage and tree species richness: A meta-analysis
Martin, P. A., Newton, A. C., Pfeifer, M., Khoo, M., & Bullock, J. M. (2015) · Forest Ecology and Management · doi:10.1016/j.foreco.2015.07.010 · Cited by 116 · see citing works
Herold, M., & Johns, T. (2007) · Environmental Research Letters · doi:10.1088/1748-9326/2/4/045025 · Cited by 110 · see citing works
New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study
Huang, X., Lu, Q., Zhang, L., & Plaza, A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2308192 · Cited by 106 · see citing works
Achard, F., Boschetti, L., Brown, S., Brady, M., DeFries, R., Grassi, G., Herold, M., Mollicone, D., Mora, B., Pandey, D., et al. (2012) · Socio-Environmental Systems Modeling · Cited by 102 · see citing works
Estimation of tropical forest structure from SPOT-5 satellite images
Santiago, M., Ricker, M., & de Jong, B. (2010) · International Journal of Remote Sensing · doi:10.1080/01431160903095460 · Cited by 96 · see citing works
Higginbottom, T. P., Symeonakis, E., Meyer, H., & van der Linden, S. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.02.010 · Cited by 89 · see citing works
Temporal Decay in Timber Species Composition and Value in Amazonian Logging Concessions
Richardson, V. A., & Peres, C. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159035 · Cited by 83 · see citing works
Mapping forest degradation in the Amazon region with Ikonos images
Júnior, C. M. D. S., & Roberts, D. A. (2004) · International Journal of Remote Sensing · doi:10.1080/0143116031000101620 · Cited by 69 · see citing works
Shimabukuro, Y. E., Beuchle, R., Grecchi, R. C., & Achard, F. (2014) · Remote Sensing Letters · doi:10.1080/2150704x.2014.967880 · Cited by 60 · see citing works
França, F. M., Frazao, F. S., Korasaki, V., Louzada, J., & Barlow, J. (2017) · Biological Conservation · doi:10.1016/j.biocon.2017.10.014 · Cited by 58 · see citing works
Self-citationAlerts of forest disturbance from MODIS imagery
Hammer, D., Kraft, R., & Wheeler, D. (2014) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2014.04.011 · Cited by 51 · see citing works
Multi‐temporal assessment of selective logging in the Brazilian Amazon using Landsat data
Matricardi, E. A. T., Skole, D. L., Cochrane, M. A., Pedlowski, M. A., & Chomentowski, W. H. (2006) · International Journal of Remote Sensing · doi:10.1080/01431160600763014 · Cited by 43 · see citing works
Information transmission, modulation, and noise : a unified approach to communication systems
Schwartz, M. (1970) · CERN Document Server (European Organization for Nuclear Research) · Cited by 41 · see citing works
Recovery Of Forest Structure And Spectral Properties After Selective Logging In Lowland Bolivia
Broadbent, E. N., Zarin, D. J., Asner, G. P., Peña‐Claros, M., Cooper, A., & Littell, R. C. (2006) · Ecological Applications · doi:10.1890/1051-0761(2006)016[1148:rofsas]2.0.co;2 · Cited by 35 · see citing works
Beekhuizen, J., & Clarke, K. (2010) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2010.01.005 · Cited by 28 · see citing works
On false discovery rate thresholding for classification under sparsity
Neuvial, P., & Roquain, É. (2012) · The Annals of Statistics · doi:10.1214/12-aos1042 · Cited by 25 · see citing works
ON FALSE DISCOVERY RATE THRESHOLDING FOR CLASSIFICATION UNDER SPARSITY
Neuvial, P., Roquain, É., Pierre, U., & Curie, M. (2013) · Cited by 25 · see citing works
Kumar, S. S., Roy, D. P., Cochrane, M. A., Júnior, C. M. D. S., Barber, C. P., & Boschetti, L. (2014) · International Journal of Wildland Fire · doi:10.1071/wf13106 · Cited by 17 · see citing works
Fisher, B., Edwards, D. P., & Wilcove, D. S. (2013) · Forest Policy and Economics · doi:10.1016/j.forpol.2013.05.006 · Cited by 16 · see citing works
Self-citationForest Resources of the Mackenzie River Basin, Northwest Territories
Robinson, J. M. (1960) · Polar Record · doi:10.1017/s0032247400051135 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Storey, J., Scaramuzza, P., Schmidt, G., Barsi, J., 2005. Landsat 7 scan line corrector-off gap-filled product development. In: PECORA 16 Conference Proceedings, pp. 23-27.
Cited by
Sorted by citations
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Remote sensing of forest degradation: a review
Gao, Y., Skutsch, M., Paneque‐Gálvez, J., & Ghilardi, A. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/abaad7 · Cited by 255 · see citing works
Lima, T. A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V. C., & Achard, F. (2019) · Remote Sensing · doi:10.3390/rs11080961 · Cited by 120 · see citing works
Kaselimi, M., Voulodimos, A. S., Daskalopoulos, I., Doulamis, N. D., & Doulamis, A. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3144791 · Cited by 121 · see citing works
Zhao, F., Sun, R., Zhong, L., Meng, R., Huang, C., Zeng, X., Wang, M., Li, Y., & Wang, Z. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112822 · Cited by 113 · see citing works
Explaining the Principles to Practices Gap in AI
Schiff, D. S., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056286 · Cited by 93 · see citing works
How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review
Dupuis, C., Lejeune, P., Michez, A., & Fayolle, A. (2020) · Remote Sensing · doi:10.3390/rs12071087 · Cited by 84 · see citing works
Substantial and increasing global losses of timber-producing forest due to wildfires
Bousfield, C. G., Lindenmayer, D. B., & Edwards, D. P. (2023) · Nature Geoscience · doi:10.1038/s41561-023-01323-y · Cited by 83 · see citing works
Self-citationDeep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2
Isaienkov, K., Yushchuk, M., Khramtsov, V., & Seliverstov, O. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3034186 · Cited by 65 · see citing works
Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series
Marzo, T. D., Dirk Pflugmacher, A., Baumann, M., Lambin, É. F., Gasparri, I., & Kuemmerle, T. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102310 · Cited by 78 · see citing works
Detecting tropical selective logging with C-band SAR data may require a time series approach
Hethcoat, M. G., Carreiras, J. M. B., Edwards, D. P., Bryant, R. G., & Quegan, S. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112411 · Cited by 72 · see citing works
Self-citationFrom forest to finished products: The contribution of Industry 4.0 technologies to the wood sector
Molinaro, M., & Orzes, G. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103637 · Cited by 61 · see citing works
Khankeshizadeh, E., Mohammadzadeh, A., Moghimi, A., & Mohsenifar, A. (2022) · Earth Science Informatics · doi:10.1007/s12145-022-00885-6 · Cited by 56 · see citing works
Global drivers of forest loss at 1 km resolution
Sims, M. J., Stanimirova, R., Raichuk, A., Neumann, M., Richter, J., Follett, F., MacCarthy, J., Lister, K., Randle, C., Sloat, L., et al. (2025) · Environmental Research Letters · doi:10.1088/1748-9326/add606 · Cited by 50 · see citing works
Rex, F. E., Silva, C. A., Côrte, A. P. D., Klauberg, C., Mohan, M., Cardíl, A., da Silva, V. S., de Almeida, D. R. A., Garcı́a, M., Broadbent, E. N., et al. (2020) · Remote Sensing · doi:10.3390/rs12091498 · Cited by 41 · see citing works
A novel multi-hazard risk assessment framework for coastal cities under climate change
Laiño, E., Toledo, I., Aragonés, L., & Iglesias, G. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176638 · Cited by 34 · see citing works
Dupuis, C., Fayolle, A., Bastin, J., Latte, N., & Lejeune, P. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113828 · Cited by 31 · see citing works
Kamarulzaman, A. M. M., Jaafar, W. S. W. M., Maulud, K. N. A., Saad, S. N. M., Omar, H., & Mohan, M. (2022) · Forests · doi:10.3390/f13010048 · Cited by 31 · see citing works
Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico
Gao, Y., Quevedo, A., Szantoi, Z., & Skutsch, M. (2019) · Geocarto International · doi:10.1080/10106049.2019.1661032 · Cited by 22 · see citing works
Remote sensing of selective logging in tropical forests: current state and future directions
Jackson, C. M., & Adam, E. (2020) · iForest - Biogeosciences and Forestry · doi:10.3832/ifor3301-013 · Cited by 21 · see citing works
Latterini, F., Camarretta, N., & Watt, M. S. (2025) · Forest Ecology and Management · doi:10.1016/j.foreco.2025.123175 · Cited by 19 · see citing works
Improving sustainable tropical forest management with voluntary carbon markets
Bomfim, B., Pinagé, E. R., Emmert, F., & Kueppers, L. M. (2022) · Plant and Soil · doi:10.1007/s11104-021-05249-5 · Cited by 19 · see citing works
Lima, T. A., Beuchle, R., Griess, V. C., Verhegghen, A., & VOGT, P. R. (2020) · Landscape Ecology · doi:10.1007/s10980-020-01080-y · Cited by 18 · see citing works
Pinagé, E. R., Keller, M., Peck, C. P., Longo, M., Duffy, P. A., & Csillik, O. (2023) · Carbon Balance and Management · doi:10.1186/s13021-023-00221-5 · Cited by 19 · see citing works
Mapping pervasive selective logging in the south-west Brazilian Amazon 2000–2019
Hethcoat, M. G., Carreiras, J. M. B., Edwards, D., Bryant, R. G., Peres, C. A., & Quegan, S. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/aba3a4 · Cited by 17 · see citing works
Self-citationDSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images
Jiang, J., Xing, Y., Wei, W., Yan, E., Xiang, J., & Mo, D. (2022) · Remote Sensing · doi:10.3390/rs14195046 · Cited by 17 · see citing works
Global risk of wildfire across timber production systems
Bousfield, C. G., Morton, O., Lindenmayer, D. B., Pellegrini, A. F. A., Hethcoat, M. G., & Edwards, D. P. (2025) · Nature Communications · doi:10.1038/s41467-025-59272-6 · Cited by 24 · see citing works
Self-citationNational forest carbon harvesting and allocation dataset for the period 2003 to 2018
Wang, D., Ren, P., Xia, X., Fan, L., Qin, Z., Chen, X., & Yuan, W. (2024) · Earth system science data · doi:10.5194/essd-16-2465-2024 · Cited by 15 · see citing works
Monitoring monthly tropical humid forest disturbances with Planet NICFI images in Cameroon
Zhang, Y., Wang, X., Li, X., Du, Y., & Atkinson, P. M. (2023) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2023.109676 · Cited by 14 · see citing works
Automatic deforestation driver attribution using deep learning on satellite imagery
Ramachandran, N., Irvin, J., Sheng, H., Johnson-Yu, S., Story, K. T., Rustowicz, R., Ng, A. Y., & Austin, K. G. (2024) · Global Environmental Change · doi:10.1016/j.gloenvcha.2024.102843 · Cited by 14 · see citing works
Oliveira, A. H. M., Matricardi, E. A. T., Aragão, L. E. O. C., Felix, I. M., Chaves, J. H., Magliano, M. M., de Oliveira, J. M. B., Vieira, T. A., dos Santos, L. E., Reis, L. P., et al. (2024) · Remote Sensing · doi:10.3390/rs16234557 · Cited by 14 · see citing works
Trustworthy Predictive Algorithms for Complex Forest System Decision-Making
Rana, P., & Varshney, L. R. (2021) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2020.587178 · Cited by 14 · see citing works
A comparative approach of methods to estimate machine productivity in wood cutting
Lopes, I. L. E., Araújo, L. A., Miranda, E. N., Bastos, T. A., Gomide, L. R., & Castro, G. P. (2021) · International Journal of Forest Engineering · doi:10.1080/14942119.2021.1952520 · Cited by 13 · see citing works
Pacheco-Angulo, C., Plata-Rocha, W., Serrano, J., Vilanova, E., Monjardin-Armenta, S., González, A., & Camargo, C. (2021) · Remote Sensing · doi:10.3390/rs13081435 · Cited by 12 · see citing works
Tian, Y., Zhao, F., Meng, R., Sun, R., Zhang, Y., Shen, Y., Wang, B., Liu, J., & Li, M. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114775 · Cited by 12 · see citing works
Hethcoat, M. G., Carreiras, J. M. B., Bryant, R. G., Quegan, S., & Edwards, D. P. (2022) · Remote Sensing · doi:10.3390/rs14010179 · Cited by 12 · see citing works
Self-citationJackson, C. M., & Adam, E. (2022) · Environmental Conservation · doi:10.1017/s0376892922000339 · Cited by 12 · see citing works
Tan, Y., Sun, K., Wei, J., Gao, S., Cui, W. H., Duan, Y., Liu, J., & Zhou, W. (2024) · Remote Sensing · doi:10.3390/rs16244736 · Cited by 10 · see citing works
Riparian reserves protect butterfly communities in selectively logged tropical forest
Montejo‐Kovacevich, G., Marsh, C. J., Smith, S. H., Peres, C. A., & Edwards, D. P. (2022) · Journal of Applied Ecology · doi:10.1111/1365-2664.14162 · Cited by 10 · see citing works
Self-citationYuan, S., & Wang, X. (2023) · Forests · doi:10.3390/f14081513 · Cited by 9 · see citing works
França, F. M., Beiroz, W., Andretti, C. B., da Costa, T. V. V., Vaz‐de‐Mello, F. Z., Silveira, J. M., & Louzada, J. (2024) · Environmental Conservation · doi:10.1017/s0376892923000334 · Cited by 9 · see citing works
Self-citationStoddart, J., de Almeida, D. R. A., Silva, C. A., Görgens, E. B., Keller, M., & Valbuena, R. (2022) · Remote Sensing · doi:10.3390/rs14040933 · Cited by 9 · see citing works
Sparing old-growth maximises conservation outcomes within selectively logged Amazonian rainforest
Cerullo, G. R., França, F. M., Finch, T., Erm, P., Griffiths, H. M., Louzada, J., Bousfield, C. G., Massam, M. R., Peres, C. A., Barlow, J., et al. (2023) · Biological Conservation · doi:10.1016/j.biocon.2023.110065 · Cited by 8 · see citing works
Self-citationSelective logging and shifting agriculture may help maintain forest biomass on the Yucatán peninsula
Gómez, I. U. H., & Ellis, E. A. (2023) · Land Degradation and Development · doi:10.1002/ldr.4789 · Cited by 8 · see citing works
Hu, Y., Wang, Z., Zhang, Y., & Dian, Y. (2022) · Remote Sensing · doi:10.3390/rs14194987 · Cited by 7 · see citing works
Tropical and subtropical remote sensing： Needs， challenges， and opportunities
Hui, L., & ZHANG, H. (2021) · National Remote Sensing Bulletin · doi:10.11834/jrs.20210237 · Cited by 7 · see citing works
Drone Aerial Image Identification of Tropical Forest Tree Species using the Mask R-CNN
Hamzah, R., & Noor, M. F. M. (2022) · International Journal of Innovative Computing · doi:10.11113/ijic.v12n2.381 · Cited by 7 · see citing works
Aquino, C., Mitchard, E. T. A., McNicol, I. M., Carstairs, H., Burt, A., Vilca, B. L. P., Mayta, S., & Disney, M. I. (2024) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.414 · Cited by 6 · see citing works
Каштан, В., & Гнатушенко, В. В. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-16203-9_37 · Cited by 6 · see citing works
Braga, D., Aragão, L. E. O. C., Leão, H., Osborn, F., Wagner, F. H., Cabral, B. F., Dutra, D. J., Izidoro, B., Anderson, L. O., Chaves, J. H., et al. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2603696 · Cited by 5 · see citing works
Wahab, M. A. A., Salwana, E., Nayan, N. M., & Rahman, H. (2021) · Malaysian Journal of Computer Science · doi:10.22452/mjcs.sp2021no2.2 · Cited by 5 · see citing works
Estimation of the accuracy of cloud masking algorithms using Sentinel-2 and PlanetScope data
Tarasov, A. V. (2020) · Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa · doi:10.21046/2070-7401-2020-17-7-26-38 · Cited by 5 · see citing works
Rotation Equivariant Deforestation Segmentation and Driver ClassificationPreprint
Mitton, J., & Murray‐Smith, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13097 · Cited by 5 · see citing works
Ren, H., Ren, C., Wang, Z., Jia, M., Wen-sen, Y., Liu, P., & Xia, C. (2023) · Remote Sensing · doi:10.3390/rs15225426 · Cited by 4 · see citing works
Mapping low-intensity selective logging across the Peruvian Amazon
Bousfield, C. G., Edwards, D. P., Hethcoat, M. G., Zumaeta, L. E. C., Mañuico, E. A., Minhuey, A., & Bryant, R. G. (2026) · Environmental Research Letters · doi:10.1088/1748-9326/ae3787 · Cited by 2 · see citing works
Self-citationForkuo, G. O., Picchio, R., Proto, A. R., & Borz, S. A. (2026) · Current Forestry Reports · doi:10.1007/s40725-026-00275-x · Cited by 2 · see citing works
Ramachandran, V. S. (2019) · Applied Ecology and Environmental Research · doi:10.15666/aeer/1704_95919607 · Cited by 2 · see citing works
WORLD COMMERCIAL SPACE MARKET: POSITIONING OF COUNTRIES AND SEGMENTS OF THE SATELLITE INDUSTRY
Savelyeva, I., Владимирович, К. Д., Pravdina, N., & Dzenzelyuk, N. (2022) · Вестник Южно-Уральского государственного университета. Серия: Экономика и менеджмент · doi:10.14529/em220115 · Cited by 2 · see citing works
Enhancing well log curve synthesis with selective attention long short-term memory network
Zhou, Y., & Li, H. (2024) · Acta Geophysica · doi:10.1007/s11600-024-01367-7 · Cited by 2 · see citing works
Tarasov, A. V. (2020) · Vestnik SSUGT (Siberian State University of Geosystems and Technologies) · doi:10.33764/2411-1759-2020-25-3-201-213 · Cited by 2 · see citing works
Brazilian Forest Dataset: A new dataset to model local biodiversity
Rios, R. A., Rios, T. N., Palma, G. R., & de Mello, R. F. (2021) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2021.1871972 · Cited by 2 · see citing works
Rakova, B., & Winter, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12387 · Cited by 2 · see citing works
Efficient selective attention LSTM for well log curve synthesisPreprint
Zhou, Y., & Li, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3280815/v1 · Cited by 2 · see citing works
Jackson, C. M., Adam, E., Atif, I., & Mahboob, M. A. (2023) · Geomatics · doi:10.3390/geomatics3010014 · Cited by 1 · see citing works
Sabattini, J. A., Sabattini, R. A., Muzzachiodi, N., Treisse, I., & Penco, R. (2024) · Revista de teledetección: Revista de la Asociación Española de Teledetección · doi:10.4995/raet.2024.20831 · Cited by 1 · see citing works
Zhang, Y., Wang, X., Li, X., Zhao, W., Zhong, X., Yu, B., Du, Y., & Atkinson, P. M. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3621653 · Cited by 1 · see citing works
Escalona, K., Valencia-Calvo, J., Olivar, G., & Olave, V. A. S. (2026) · Geomatics · doi:10.3390/geomatics6030045 · Cited by 1 · see citing works
The Role of the Biomass Mission in Carbon Cycle Science and Politics
Quegan, S., Toan, T. L., Chave, J., Reichstein, M., Saatchi, S. S., Shugart, H., & Williams, M. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554964 · Cited by 1 · see citing works
Self-citationSubhahan, D. A., & Kumar, C. N. S. V. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-233534 · Cited by 1 · see citing works
RetractedAvaliação espaço-temporal da exploração seletiva de madeiras no estado do Pará, Brasil
de Souza, J. V., Matricardi, E. A. T., Pedlowski, M. A., Miguel, É. P., & Pereira, R. S. (2024) · Ciência Florestal · doi:10.5902/1980509871255 · Cited by 1 · see citing works
Forest Degradation Derived by a Newly Developed Sentinel-1 Change Detection Approach
Langner, A., & Carboni, S. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554574 · Cited by 1 · see citing works
Classification of Deforestation Factors in 6G Satellite Forest Images
Li, Y., Sun, Y., Meng, X., & Xi, L. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-36011-4_17
Capdevilla, D. A. G., Guzmán, J. A. P., & Reyes, C. Y. (2023) · FACE Revista de la Facultad de Ciencias Económicas y Empresariales · doi:10.24054/face.v21i3.1143
(2023) · Earth system science data · doi:10.5194/essd-2023-309-rc1
(2023) · Earth system science data · doi:10.5194/essd-2023-309-rc2
(2023) · Earth system science data · doi:10.5194/essd-2023-309-rc3
Wang, D. (2024) · Earth system science data · doi:10.5194/essd-2023-309-ac1
Li, A., Kato, T., Li, H., Nguyen, L. D., Hayashi, M., Avtar, R., & Nakaji, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5399964
A Comprehensive Quantitative Model for Ethical AI Risk Assessment: EU Act on Artificial Intelligence
Sarkar, S., Sunheriya, N., Giri, J. P., Al-Qawasmi, K. E., & Chadge, R. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-89771-9_10
Yang, D., Tai, X., He, Y., Liu, T., Li, S., & Katabuchi, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33803-8.00003-2
Role of Artificial Intelligence in Weed Detection and Prevention
Arunkumar, K., Shanmugam, L., & Sandhya, M. (2026) · doi:10.1002/9781394310630.ch15
Li, A., Kato, T., Li, H., Nguyen, L. D., Hayashi, M., Avtar, R., & Nakaji, T. (2026) · Forest Ecology and Management · doi:10.1016/j.foreco.2026.123692
de Oliveira, M. A. F., Reis, M. S., Sant’Anna, S. J. S., & Escada, M. I. S. (2026) · Land · doi:10.3390/land15071130
Utilizing AI and Spatial Computing to Map Deforestation and Inform Regulatory Engagements
Silva, L. (2026) · Unveiling seven continents yearbook journal · doi:10.65326/u7y.specsdg10002
Mapping selective logging with Sentinel-1 image time series and a novel transformer-based model
Huang, X., Maretto, R. V., Fonseca, L. M. G., Stein, A., & Persello, C. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105567
A review of forest innovation: Technologies and sustainable practices for environmental conservation
Garg, S., & Arora, P. (2026) · Next Sustainability · doi:10.1016/j.nxsust.2026.100546
Twenty core challenges to global wood security in the Anthropocene
Bousfield, C. G., Meeks, D., Cerullo, G. R., Betts, M. G., Miao, X., Adams, M. A., Pérez, G., Saiya, H. G., Bryant, R. G., Weiss, J., et al. (2026) · Trends Open · doi:10.1016/j.treopn.2026.08.021
Self-citationDas, N., & Andrew, K. (2026) · Discover Informatics · doi:10.1007/s44564-026-00029-8 · Cited by 1 · see citing works
- Authors
- Hethcoat, M. G., Edwards, D. P., Carreiras, J. M. B., Bryant, R. G., França, F. M., & Quegan, S.
- Affiliations
- Grantham Centre for Sustainable Futures (GB)
- University of Sheffield (GB)
- Universidade Federal do Pará (BR)
- Corresponding author
- Hethcoat, M.G.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 221
- Pages
- 569-582
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2897077652
- Cited by
- 91
- References
- 72
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 11 · 2023: 16 · 2022: 16 · 2021: 14 · 2020: 10 · 2019: 4
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Remote Sensing of Environment (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Explore Bristol Research (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- Grantham Centre for Sustainable Futures
- NERC (PELD-RAS 441659/2016-0)
- CNPq (NE/P004512/1)
- CAPES (BEX5528/13-5)
- CNPq-PELD (403811/2012-0)
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Keywords
- selective logging, tropical forest monitoring, Landsat imagery, random forest classification, Rondônia, Pará, illegal logging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
