{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T18:20:57Z","timestamp":1786040457009,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,5,23]],"date-time":"2017-05-23T00:00:00Z","timestamp":1495497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely detection of forest disturbance is often accompanied by many false detections, and existing approaches for reducing false detections either compromise the temporal accuracy or amplify the omission error for forest disturbances. Here, we propose to use a set of space-time features to reduce false detections. We first detect potential forest disturbances in the Landsat time series based on two consecutive negative anomalies, and subsequently use space-time features to confirm forest disturbances. A probability threshold is used to discriminate false detections from forest disturbances. We demonstrated this approach in the UNESCO Kafa Biosphere Reserve located in the southwest of Ethiopia by detecting forest disturbances between 2014 and 2016. Our results show that false detections are reduced significantly without compromising temporal accuracy. The user\u2019s accuracy was at least 26% higher than the user\u2019s accuracies obtained when using only temporal information (e.g., two consecutive negative anomalies) to confirm forest disturbances. We found the space-time features related to change in spatio-temporal variability, and spatio-temporal association with non-forest areas, to be the main predictors for forest disturbance. The magnitude of change and two consecutive negative anomalies, which are widely used to distinguish real changes from false detections, were not the main predictors for forest disturbance. Overall, our findings indicate that using a set of space-time features to confirm forest disturbances increases the capacity to reject many false detections, without compromising the temporal accuracy.<\/jats:p>","DOI":"10.3390\/rs9060515","type":"journal-article","created":{"date-parts":[[2017,5,23]],"date-time":"2017-05-23T11:41:20Z","timestamp":1495539680000},"page":"515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series"],"prefix":"10.3390","volume":"9","author":[{"given":"Eliakim","family":"Hamunyela","sequence":"first","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &amp; Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-4349","authenticated-orcid":false,"given":"Johannes","family":"Reiche","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &amp; Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7923-4309","authenticated-orcid":false,"given":"Jan","family":"Verbesselt","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &amp; Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Herold","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University &amp; Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2011.10.030","article-title":"Continuous monitoring of forest disturbance using all available Landsat imagery","volume":"122","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.04.002","article-title":"Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data","volume":"135","author":"Xin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classi fi cation of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive re fl ectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hamunyela, E., Verbesselt, J., De Bruin, S., and Herold, M. (2016). Monitoring deforestation at sub-annual scales as extreme events in Landsat data cubes. Remote Sens., 8.","DOI":"10.3390\/rs8080651"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.3390\/rs70504973","article-title":"1 A bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection","volume":"7","author":"Reiche","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"34008","DOI":"10.1088\/1748-9326\/11\/3\/034008","article-title":"Humid tropical forest disturbance alerts using Landsat data","volume":"11","author":"Hansen","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2015.11.006","article-title":"Using spatial context to improve early detection of deforestation from Landsat time series","volume":"172","author":"Hamunyela","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D., and Herold, M. (2017). Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sens. Environ., submitted.","DOI":"10.1016\/j.rse.2017.10.034"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2015.03.015","article-title":"Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia","volume":"107","author":"Dutrieux","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2008.2005977","article-title":"Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances","volume":"47","author":"Vermote","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2015.02.012","article-title":"Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series","volume":"161","author":"Devries","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1016\/j.rse.2008.06.016","article-title":"Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States","volume":"113","author":"Huang","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1038\/ngeo873","article-title":"African exception to drivers of deforestation","volume":"3","author":"Fisher","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_16","first-page":"318","article-title":"Performance of vegetation indices from Landsat time series in deforestation monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"044039","DOI":"10.1088\/1748-9326\/8\/4\/044039","article-title":"National-scale estimation of gross forest aboveground carbon loss: A case study of the Democratic Republic of the Congo","volume":"8","author":"Tyukavina","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ecoinf.2013.03.004","article-title":"Detection and attribution of large spatiotemporal extreme events in Earth observation data","volume":"15","author":"Zscheischler","year":"2013","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2903","DOI":"10.1080\/01431160110096791","article-title":"Space-time dynamics of deforestation in Brazilian Amaz\u00f4nia","volume":"23","author":"Alves","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.3390\/f5102464","article-title":"Combining satellite data and community-based observations for forest monitoring","volume":"5","author":"Pratihast","year":"2014","journal-title":"Forests"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.3390\/land3031137","article-title":"Fuelwood savings and carbon emission reductions by the use of improved cooking stoves in an afromontane forest, Ethiopia","volume":"3","author":"Dresen","year":"2014","journal-title":"Land"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Devries, B., Pratihast, A.K., Verbesselt, J., Kooistra, L., and Herold, M. (2016). Characterizing forest change using community-based monitoring data and Landsat time series. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0147121"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"150","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_24","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third Earth Resource Technology Satellite (ERTS) Symposium, Greenbelt, MD, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface re fl ectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1029\/2001GL013206","article-title":"A spatio-temporal approach for global validation and analysis of MODIS aerosol products","volume":"29","author":"Ichoku","year":"2002","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0038-092X(00)00152-3","article-title":"Spatio-temporal variability of solar energy across a region: A statistical modelling approach","volume":"70","author":"Glasbey","year":"2001","journal-title":"Sol. Energy Vol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"20729","DOI":"10.1029\/1999JC900167","article-title":"On the use of high-resolution satellite data to describe the spatial and temporal variability of sea surface temperatures in the New Zealand region","volume":"104","author":"Uddstrom","year":"1999","journal-title":"J. Geophys. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5243","DOI":"10.1080\/01431160903131000","article-title":"Sampling designs for accuracy assessment of land cover","volume":"30","author":"Stehman","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/01431161.2010.541950","article-title":"Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change","volume":"3","author":"Stehman","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1016\/j.rse.2010.07.010","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync\u2014Tools for calibration and validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2014.10.001","article-title":"Fusing Landsat and SAR time series to detect deforestation in the tropics","volume":"156","author":"Reiche","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2015.03.001","article-title":"Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series","volume":"169","author":"Grogan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/s130100021","article-title":"Mobile devices for community-based REDD+ monitoring: A case study for Central Vietnam","volume":"13","author":"Pratihast","year":"2012","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pratihast, A.K., Devries, B., Avitabile, V., Bruin, S. De, and Herold, M. (2016). Design and implementation of an interactive web-based near real-time forest monitoring system. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150935"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/514168a","article-title":"Beyond sharing Earth observations","volume":"514","author":"See","year":"2014","journal-title":"Nature"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TGRS.2011.2171495","article-title":"Joint processing of Landsat and ALOS-PALSAR data for forest mapping and monitoring","volume":"50","author":"Lehmann","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.rse.2014.09.034","article-title":"SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system","volume":"156","author":"Lehmann","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/515\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:36:43Z","timestamp":1760207803000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,23]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060515"],"URL":"https:\/\/doi.org\/10.3390\/rs9060515","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,23]]}}}