{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:14:59Z","timestamp":1775664899701,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Aerospace Center (DLR)","award":["28I02701"],"award-info":[{"award-number":["28I02701"]}]},{"name":"Federal Ministry of Food and Agriculture (BMEL)","award":["28I02701"],"award-info":[{"award-number":["28I02701"]}]},{"name":"Parliament of the Federal Republic of Germany","award":["28I02701"],"award-info":[{"award-number":["28I02701"]}]},{"DOI":"10.13039\/501100005908","name":"Federal Office for Agriculture and Food (BLE)","doi-asserted-by":"publisher","award":["28I02701"],"award-info":[{"award-number":["28I02701"]}],"id":[{"id":"10.13039\/501100005908","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks.<\/jats:p>","DOI":"10.3390\/rs14174327","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:19:01Z","timestamp":1662077941000},"page":"4327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0369-5819","authenticated-orcid":false,"given":"Jennifer","family":"Kriese","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7179-3664","authenticated-orcid":false,"given":"Thorsten","family":"Hoeser","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-6813","authenticated-orcid":false,"given":"Sarah","family":"Asam","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4538-8286","authenticated-orcid":false,"given":"Patrick","family":"Kacic","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"given":"Emmanuel Da","family":"Da Ponte","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"given":"Ursula","family":"Gessner","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"ref_1","unstructured":"Gill, E., Da Ponte, E., Insfr\u00e1n, K., and Gonz\u00e1lez, L. (2020). Atlas of the Paraguayan Chaco, DLR (German Aerospace Center). WWF (World Wildlife Fund)."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Da Ponte, E., Garc\u00eda-Calabrese, M., Kriese, J., Cabral, N., Perez de Molas, L., Alvarenga, M., Caceres, A., Gali, A., Garc\u00eda, V., and Morinigo, L. (2022). Understanding 34 Years of Forest Cover Dynamics across the Paraguayan Chaco: Characterizing Annual Changes and Forest Fragmentation Levels between 1987 and 2020. Forests, 13.","DOI":"10.3390\/f13010025"},{"key":"ref_3","unstructured":"La Republica del Paraguay (2022, August 23). Decreto N\u00ba 18831\/86\u2014Normas de Protecci\u00f3n del Medio Ambiente. Available online: https:\/\/leap.unep.org\/countries\/py\/national-legislation\/decreto-no-1883186-normas-de-proteccion-del-medio-ambiente."},{"key":"ref_4","unstructured":"Instituci\u00f3n Forestal Nacional (2022, August 23). Resoluci\u00f3n INFONA N\u00ba 1242\/2012, Available online: http:\/\/www.infona.gov.py\/application\/files\/3614\/2920\/9237\/2012_RESOLUCION_N_1242.pdf."},{"key":"ref_5","unstructured":"Instituci\u00f3n Forestal Nacional (2022, August 23). Resoluci\u00f3n INFONA N\u00ba 1001\/2019, Available online: http:\/\/www.infona.gov.py\/application\/files\/3015\/7373\/2886\/RESOLUCION_INFONA_N_1001_2019.pdf."},{"key":"ref_6","unstructured":"Ministerio de Agricultura y Ganader\u00eda de la Rep\u00fablica del Paraguay (2022, August 23). Resoluci\u00f3n S.F.N. N\u00ba 1105\/2007. Available online: https:\/\/www.fepama.org\/v1\/RESOL%20SFN%20N%201105-07.pdf."},{"key":"ref_7","first-page":"34","article-title":"Las cortinas forestales de boque nativo, son eficaces para mitigar los efectos de la expansion agricola?","volume":"3","author":"Ginzburg","year":"2012","journal-title":"Revista de la Asociacion Argentina de Ecologia de Paisajes"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.13031\/2013.31257","article-title":"Wind Barriers: A Reevaluation of Height, Spacing, and Porosity","volume":"32","author":"Borrelli","year":"1989","journal-title":"Trans. ASAE"},{"key":"ref_9","unstructured":"Emrich, A., Pokorny, B., and Sepp, C. (2000). The Significance of Secondary Forest Management for Development Policy, Deutsche Gesellschaft f\u00fcr Technische Zusammenarbeit (GTZ) GmbH."},{"key":"ref_10","unstructured":"Hillel, D. (2005). Windbreaks and Shelterbelts. Encyclopedia of Soils in the Environment, Elsevier."},{"key":"ref_11","first-page":"167","article-title":"Shape indexes for semi-automated detection of windbreaks in thematic tree cover maps from the central United States","volume":"59","author":"Liknes","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Burke, M.W., Rundquist, B.C., and Zheng, H. (2019). Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030218"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10457-014-9731-4","article-title":"Identification of windbreaks in Kansas using object-based image analysis, GIS techniques and field survey","volume":"88","author":"Ghimire","year":"2014","journal-title":"Agrofor. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Piwowar, J.M., Amichev, B.Y., and van Rees, K. (2016). The Saskatchewan Shelterbelt Inventory. Can. J. Soil Sci.","DOI":"10.1139\/CJSS-2016-0098"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10457-016-9915-1","article-title":"Remote estimation of shelterbelt width from SPOT5 imagery","volume":"91","author":"Deng","year":"2016","journal-title":"Agrofor. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s10457-013-9599-8","article-title":"Recognition of shelterbelt continuity using remote sensing and waveform recognition","volume":"87","author":"Deng","year":"2013","journal-title":"Agrofor. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"018501","DOI":"10.1117\/1.JRS.15.018501","article-title":"Hedgerow object detection in very high-resolution satellite images using convolutional neural networks","volume":"15","author":"Ahlswede","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hoeser, T., and Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sens., 12.","DOI":"10.3390\/rs12101667"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hoeser, T., Bachofer, F., and Kuenzer, C. (2020). Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review\u2014Part II: Applications. Remote Sens., 12.","DOI":"10.3390\/rs12183053"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_25","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_26","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4205","DOI":"10.1109\/JSTARS.2021.3070368","article-title":"On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID","volume":"14","author":"Long","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isprsjprs.2022.04.029","article-title":"SyntEO: Synthetic dataset generation for earth observation and deep learning\u2014Demonstrated for offshore wind farm detection","volume":"189","author":"Hoeser","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/LGRS.2018.2811754","article-title":"Learning a River Network Extractor Using an Adaptive Loss Function","volume":"15","author":"Isikdogan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kong, F., Huang, B., Bradbury, K., and Malof, J. (2020, January 1\u20135). The Synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093339"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, B., Li, X., Huang, B., Gu, E., Guo, W., and Wu, L. (2021). UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images. Remote Sens., 13.","DOI":"10.3390\/rs13244999"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10584-014-1256-3","article-title":"Assessment of rates of deforestation classes in the Paraguayan Chaco (Great South American Chaco) with comments on the vulnerability of forests fragments to climate change","volume":"127","author":"Mereles","year":"2014","journal-title":"Clim. Chang."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s10113-017-1109-5","article-title":"Deforestation and cattle expansion in the Paraguayan Chaco 1987\u20132012","volume":"17","author":"Baumann","year":"2017","journal-title":"Reg. Environ. Chang."},{"key":"ref_34","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_35","unstructured":"The European Space Agency (2022, May 20). Sentinel-1 MSI\/Cloud Masks. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/technical-guides\/sentinel-2-msi\/level-1c\/cloud-masks."},{"key":"ref_36","unstructured":"Google Developers (2022, July 04). Eath Engine Data Catalog\u2014Sentinel-2 MSI: MultiSpectral Instrument, Level-1C. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S2#bands."},{"key":"ref_37","unstructured":"Montero Loaiza, D. (2022, July 04). Awesome Spectral Indices. Available online: https:\/\/awesome-ee-spectral-indices.readthedocs.io\/en\/latest\/index.html."},{"key":"ref_38","unstructured":"Kauth, R., and Thomas, G. (July, January 29). The Tasselled-Cap\u2014A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"Scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_41","unstructured":"Spencer, K., and Imas, A. (2021, November 03). OpenSimplex Noise. Available online: https:\/\/github.com\/lmas\/opensimplex."},{"key":"ref_42","first-page":"120","article-title":"The OpenCV Library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools Prof. Program."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2022, January 07). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_46","unstructured":"Chollet, F.E.A. (2022, January 07). Keras. Available online: https:\/\/keras.io."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Padilla, R., Passos, W.L., Dias, T.L.B., Netto, S.L., and da Silva, E.A.B. (2021). A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit. Electronics, 10.","DOI":"10.3390\/electronics10030279"},{"key":"ref_50","unstructured":"Voiland, A. (2022, April 01). A Windbreak Grid in Hokkaido, Available online: https:\/\/earthobservatory.nasa.gov\/images\/146664\/a-windbreak-grid-in-hokkaido."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Berkson, E.E., VanCor, J.D., Esposito, S., Chern, G., and Pritt, M. (2019, January 15\u201317). Synthetic Data Generation to Mitigate the Low\/No-Shot Problem in Machine Learning. Proceedings of the 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR47015.2019.9174596"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shermeyer, J., Hossler, T., Van Etten, A., Hogan, D., Lewis, R., and Kim, D. (2021, January 3\u20138). RarePlanes: Synthetic Data Takes Flight. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00025"},{"key":"ref_53","first-page":"1","article-title":"Synthetic Data Augmentation Using Multiscale Attention CycleGAN for Aircraft Detection in Remote Sensing Images","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2021.02.015","article-title":"Artificial and beneficial\u2014Exploiting artificial images for aerial vehicle detection","volume":"175","author":"Weber","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","unstructured":"Sadjadi, F.A., and Mahalanobis, A. (2017, January 10\u201311). Efficient generation of image chips for training deep learning algorithms. Proceedings of the SPIE, Automatic Target Recognition XXVII, Anaheim, CA, USA."},{"key":"ref_56","first-page":"1","article-title":"DeepOWT: A global offshore wind turbine data set derived with deep learning from Sentinel-1 data","volume":"2022","author":"Hoeser","year":"2022","journal-title":"Earth Syst. Sci. Data Discuss."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:21:48Z","timestamp":1760142108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,1]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174327"],"URL":"https:\/\/doi.org\/10.3390\/rs14174327","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,1]]}}}