{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:54:44Z","timestamp":1772906084467,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of South Africa","award":["Grant No. 118593"],"award-info":[{"award-number":["Grant No. 118593"]}]},{"name":"National Research Foundation of South Africa","award":["80NSSC21K1183"],"award-info":[{"award-number":["80NSSC21K1183"]}]},{"name":"Group on Earth Observations-Google Earth Engine Programme","award":["Grant No. 118593"],"award-info":[{"award-number":["Grant No. 118593"]}]},{"name":"Group on Earth Observations-Google Earth Engine Programme","award":["80NSSC21K1183"],"award-info":[{"award-number":["80NSSC21K1183"]}]},{"DOI":"10.13039\/100000104","name":"NASA Ecological Forecasting Team Applied Sciences Program","doi-asserted-by":"publisher","award":["Grant No. 118593"],"award-info":[{"award-number":["Grant No. 118593"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"NASA Ecological Forecasting Team Applied Sciences Program","doi-asserted-by":"publisher","award":["80NSSC21K1183"],"award-info":[{"award-number":["80NSSC21K1183"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5\u201310% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers.<\/jats:p>","DOI":"10.3390\/rs14122766","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Continuous Land Cover Change Detection in a Critically Endangered Shrubland Ecosystem Using Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0066-4371","authenticated-orcid":false,"given":"Glenn R.","family":"Moncrieff","sequence":"first","affiliation":[{"name":"Fynbos Node, South African Environmental Observation Network, Private Bag X7, Rhodes Drive, Claremont 7735, South Africa"},{"name":"Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Private Bag X3, Cape Town 7701, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","unstructured":"Brondizio, E.S., Settele, J., D\u00edaz, S., and Ngo, H.T. (2019). Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.1016\/j.cub.2019.07.063","article-title":"Recent Anthropogenic Plant Extinctions Differ in Biodiversity Hotspots and Coldspots","volume":"29","author":"Hui","year":"2019","journal-title":"Curr. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/S0169-5347(03)00070-3","article-title":"Remote sensing for biodiversity science and conservation","volume":"18","author":"Turner","year":"2003","journal-title":"Trends Ecol. Evol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1126\/science.1256014","article-title":"Sensing biodiversity","volume":"346","author":"Turner","year":"2014","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2019.02.003","article-title":"Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework","volume":"224","author":"Tang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41558-020-00956-w","article-title":"The impact of near-real-time deforestation alerts across the tropics","volume":"11","author":"Moffette","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bond, W.J. (2019). Open Ecosystems: Ecology and Evolution beyond the Forest Edge, Oxford University Press.","DOI":"10.1093\/oso\/9780198812456.001.0001"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1038\/nature13376","article-title":"Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle","volume":"509","author":"Poulter","year":"2014","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2020.05.017","article-title":"Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics","volume":"166","author":"Slingsby","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9058","DOI":"10.1073\/pnas.1416710112","article-title":"Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa","volume":"112","author":"Wilson","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/35002501","article-title":"Biodiversity hotspots for conservation priorities","volume":"403","author":"Myers","year":"2000","journal-title":"Nature"},{"key":"ref_13","unstructured":"Manning, J., and Goldblatt, P. (2012). Plants of the Greater Cape Floristic Region. 1: The Core Cape Flora, South African National Biodiversity Institute."},{"key":"ref_14","unstructured":"Skowno, A.J., Poole, C.J., and Raimondo, D.C. (2019). National Biodiversity Assessment 2018: The Status of South Africa\u2019s Ecosystems and Biodiversity. Synthesis Report, South African National Biodiversity Institute."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Skowno, A.L., Jewitt, D., and Slingsby, J.A. (2021). Rates and patterns of habitat loss across South Africa\u2019s vegetation biomes. S. Afr. J. Sci., 117.","DOI":"10.17159\/sajs.2021\/8182"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1038\/s41559-019-0906-2","article-title":"Global dataset shows geography and life form predict modern plant extinction and rediscovery","volume":"3","author":"Humphreys","year":"2019","journal-title":"Nat. Ecol. Evol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4102\/sajs.v107i3\/4.653","article-title":"The Red List of South African plants: A global first","volume":"107","author":"Raimondo","year":"2011","journal-title":"S. Afr. J. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Brummitt, N.A., Bachman, S.P., Griffiths-Lee, J., Lutz, M., Moat, J.F., Farjon, A., Donaldson, J.S., Hilton-Taylor, C., Meagher, T.R., and Albuquerque, S. (2015). Green Plants in the Red: A Baseline Global Assessment for the IUCN Sampled Red List Index for Plants. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0135152"},{"key":"ref_19","first-page":"104","article-title":"A fine-scale conservation plan for Cape lowlands renosterveld: Technical report","volume":"2","author":"Rouget","year":"2003","journal-title":"Rep. CCU"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Moncrieff, G.R. (2021). Locating and Dating Land Cover Change Events in the Renosterveld, a Critically Endangered Shrubland Ecosystem. Remote Sens., 13.","DOI":"10.3390\/rs13050834"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112167","DOI":"10.1016\/j.rse.2020.112167","article-title":"A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection","volume":"252","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111116","DOI":"10.1016\/j.rse.2019.03.009","article-title":"Continuous monitoring of land disturbance based on Landsat time series","volume":"238","author":"Zhu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1016\/j.rse.2008.07.018","article-title":"Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects","volume":"113","author":"Kennedy","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"034008","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_28","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr \u2014 Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pacheco-Pascagaza, A.M., Gou, Y., Louis, V., Roberts, J.F., Rodr\u00edguez-Veiga, P., da Concei\u00e7\u00e3o Bispo, P., Esp\u00edrito-Santo, F.D.B., Robb, C., Upton, C., and Galindo, G. (2022). Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests. Remote Sens., 14.","DOI":"10.3390\/rs14030707"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wessels, K.J., Van den Bergh, F., Roy, D.P., Salmon, B.P., Steenkamp, K.C., MacAlister, B., Swanepoel, D., and Jewitt, D. (2016). Rapid land cover map updates using change detection and robust random forest classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8110888"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/LGRS.2009.2020306","article-title":"Support vector reduction in SVM algorithm for abrupt change detection in remote sensing","volume":"6","author":"Habib","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring land-cover changes: A comparison of change detection techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2019). Self-attention for raw optical satellite time series classification. arXiv.","DOI":"10.1016\/j.isprsjprs.2020.06.006"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Webb, G.I., and Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11050523"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sefrin, O., Riese, F.M., and Keller, S. (2020). Deep learning for land cover change detection. Remote Sens., 13.","DOI":"10.3390\/rs13010078"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., Pelletier, C., Zollner, M., Lef\u00e8vre, S., and K\u00f6rner, M. (2020). BreizhCrops: A Time Series Dataset for Crop Type Mapping. arXiv.","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-1545-2020"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_41","unstructured":"Mucina, L., and Rutherford, M.C. (2006). The Vegetation of South Africa, Lesotho and Swaziland, South African National Biodiversity Institute. Strelitzia 19."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Douzas, G., Bacao, F., Fonseca, J., and Khudinyan, M. (2019). Imbalanced Learning in Land Cover Classification: Improving Minority Classes\u2019 Prediction Accuracy Using the Geometric SMOTE Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11243040"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P.R., Santos, L., Carvalho, A., and Ferreira, K. (2021). Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sens., 13.","DOI":"10.3390\/rs13132428"},{"key":"ref_45","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_46","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2016). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the 2018 IEEE winter conference on applications of computer vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111165","DOI":"10.1016\/j.rse.2019.04.018","article-title":"Improved change monitoring using an ensemble of time series algorithms","volume":"238","author":"Bullock","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Rover, J., Brown, J., Worstell, B., Howard, D., Wu, Z., Gallant, A.L., Rundquist, B., and Burke, M. (2019). Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data. Remote Sens., 11.","DOI":"10.3390\/rs11030328"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112560","DOI":"10.1016\/j.rse.2021.112560","article-title":"Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","volume":"263","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.tree.2014.02.004","article-title":"Tropical grassy biomes: Misunderstood, neglected, and under threat","volume":"29","author":"Parr","year":"2014","journal-title":"Trends Ecol. Evol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., Wang, S., Korner, M., and Lobell, D. (2020, January 14\u201319). Meta-learning for few-shot land cover classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00108"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1109\/JSTARS.2021.3052869","article-title":"Research progress on few-shot learning for remote sensing image interpretation","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vanschoren, J. (2019). Meta-learning. Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"ref_56","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A time-weighted dynamic time warping method for land-use and land-cover mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","article-title":"ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels","volume":"34","author":"Dempster","year":"2020","journal-title":"Data Min. Knowl. Discov."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:26:50Z","timestamp":1760138810000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,9]]},"references-count":58,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122766"],"URL":"https:\/\/doi.org\/10.3390\/rs14122766","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.10.26.465837","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,9]]}}}