{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:31:13Z","timestamp":1768714273136,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"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>Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R2 = 0.84 and RMSE = 41.2 t\u00b7ha\u22121 (35.1%), parametrized for an AGB range of 4 to 350 t\u00b7ha\u22121. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub\u2013tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition.<\/jats:p>","DOI":"10.3390\/rs13132488","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"2488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Woody Above-Ground Biomass Estimation on Abandoned Agriculture Land Using Sentinel-1 and Sentinel-2 Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8434-7527","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Bucha","sequence":"first","affiliation":[{"name":"National Forest Centre\u2014Forest Research Institute, T. G. Masaryka 22, 960 01 Zvolen, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-9625","authenticated-orcid":false,"given":"Juraj","family":"Pap\u010do","sequence":"additional","affiliation":[{"name":"Department of Theoretical Geodesy and Geoinformatics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Radlinsk\u00e9ho 11, 810 05 Bratislava, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8379-5635","authenticated-orcid":false,"given":"Ivan","family":"Sa\u010dkov","sequence":"additional","affiliation":[{"name":"National Forest Centre\u2014Forest Research Institute, T. G. Masaryka 22, 960 01 Zvolen, Slovakia"}]},{"given":"Jozef","family":"Pajt\u00edk","sequence":"additional","affiliation":[{"name":"National Forest Centre\u2014Forest Research Institute, T. G. Masaryka 22, 960 01 Zvolen, Slovakia"}]},{"given":"Maro\u0161","family":"Sedliak","sequence":"additional","affiliation":[{"name":"National Forest Centre\u2014Forest Research Institute, T. G. Masaryka 22, 960 01 Zvolen, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-8542","authenticated-orcid":false,"given":"Ivan","family":"Barka","sequence":"additional","affiliation":[{"name":"National Forest Centre\u2014Forest Research Institute, T. G. Masaryka 22, 960 01 Zvolen, Slovakia"}]},{"given":"J\u00e1n","family":"Feranec","sequence":"additional","affiliation":[{"name":"Institute of Geography, Slovak Academy of Sciences, \u0160tef\u00e1nikov\u00e1 49, 814 73 Bratislava, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","unstructured":"FAO, UN. (2016). Global Forest Resources Assessment 2015, FAO UN. [2nd ed.]."},{"key":"ref_2","unstructured":"FAO, UN. (2010). Global Forest Resources Assessment 2010, FAO UN. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1007\/s10021-008-9146-z","article-title":"Cross-border Comparison of Post-socialist Farmland Abandonment in the Carpathians","volume":"11","author":"Kuemmerle","year":"2008","journal-title":"Ecosystems"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"045024","DOI":"10.1088\/1748-9326\/8\/4\/045024","article-title":"Agricultural land change in the Carpathian ecoregion after the breakdown of socialism and expansion of the European Union","volume":"8","author":"Griffiths","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_5","first-page":"45","article-title":"Abandonment of Agricultural Land","volume":"50","author":"Midriak","year":"2016","journal-title":"\u017divotn\u00e9 Prostr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"035035","DOI":"10.1088\/1748-9326\/8\/3\/035035","article-title":"Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data","volume":"8","author":"Alcantara","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kozak, J., Zi\u00f3\u0142kowska, E., Vogt, P., Dobosz, M., Kaim, D., Kolecka, N., and Ostafin, K. (2018). Forest-Cover Increase Does Not Trigger Forest-Fragmentation Decrease: Case Study from the Polish Carpathians. Sustainability, 10.","DOI":"10.3390\/su10051472"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kolecka, N. (2018). Height of Successional Vegetation Indicates Moment of Agricultural Land Abandonment. Remote Sens., 10.","DOI":"10.3390\/rs10101568"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sa\u010dkov, I., Barka, I., and Bucha, T. (2020). Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data. Remote Sens., 12.","DOI":"10.3390\/rs12244189"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Goga, T., Feranec, J., Bucha, T., Rusn\u00e1k, M., Sa\u010dkov, I., Barka, I., Kopeck\u00e1, M., Pap\u010do, J., O\u0165ahe\u013e, J., and Szatm\u00e1ri, D. (2019). A Review of the Application of Remote Sensing Data for Abandoned Agricultural Land Identification with Focus on Central and Eastern Europe. Remote Sens., 11.","DOI":"10.3390\/rs11232759"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1111\/j.1365-2486.2010.02333.x","article-title":"Post-Soviet farmland abandonment, forest recovery, and carbon sequestration in western Ukraine","volume":"17","author":"Kuemmerle","year":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s41976-019-00020-y","article-title":"Mapping Urbanization Trends in a Forested Landscape Using Google Earth Engine","volume":"2","author":"Zurqani","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TGRS.1995.8746034","article-title":"Radar backscatter and biomass saturation: Ramifications for global biomass inventory","volume":"33","author":"Imhoff","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1016\/j.rse.2010.03.018","article-title":"L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest","volume":"115","author":"Sandberg","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"53","article-title":"Forest biomass retrieval approaches from earth observation in different biomes","volume":"77","author":"Quegan","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, X., Ziniti, B., Torbick, N., and Ducey, M.J. (2018). Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091424"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1109\/JSTARS.2018.2814825","article-title":"Temporal survey of P- and L-band polarimetric backscatter in boreal forests","volume":"11","author":"Monteith","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A review of radar remote sensing for biomass estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hawry\u0142o, P., and W\u0119\u017cyk, P. (2018). Predicting Growing Stock Volume of Scots Pine Stands Using Sentinel-2 Satellite Imagery and Airborne Image-Derived Point Clouds. Forests, 9.","DOI":"10.3390\/f9050274"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., H\u00e4me, T., and Praks, J. (2017). Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests. Remote Sens., 9.","DOI":"10.3390\/rs9100999"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s00035-011-0095-3","article-title":"Allometric equations for biomass assessment of subalpine dwarf shrubs","volume":"121","author":"Elzein","year":"2011","journal-title":"Alp Bot."},{"key":"ref_25","first-page":"49","article-title":"S\u00fastava \u010desko-slovensk\u00fdch objemov\u00fdch tabuliek drev\u00edn","volume":"37","year":"1991","journal-title":"Lesn\u00edcky \u010casopis"},{"key":"ref_26","first-page":"155","article-title":"Nov\u00e9 metodick\u00e9 postupy na kvantifik\u00e1ciu m\u0155tveho dreva a jeho zlo\u017eiek v lesn\u00fdch ekosyst\u00e9moch","volume":"56","year":"2010","journal-title":"Lesn\u00edcky \u010casopis"},{"key":"ref_27","unstructured":"Pajt\u00edk, J., Kon\u00f4pka, B., and \u0160ebe\u0148, V. (2018). Mathematical Biomass Models for Young Individuals of Forest Tree Species in the Region of the Western Carpathians, National Forest Centre."},{"key":"ref_28","unstructured":"Po\u017egaj, A., Chovanec, D., Kurjatko, S., and Babjak, M. (2003). \u0160trukt\u00fara a Vlastnosti Dreva, Priroda. (In Slovak)."},{"key":"ref_29","unstructured":"Marklund, L.G. (1987). Biomass Functions for Norway Spruce (Picea abies (L.) Karst.) in Sweden, Department of Forest Survey, Swedish University of Agricultural Sciences. Report No.43."},{"key":"ref_30","first-page":"54","article-title":"Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy","volume":"1","author":"Efron","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4070","DOI":"10.1109\/JSTARS.2020.3008096","article-title":"Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping","volume":"13","author":"Jacob","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","unstructured":"National Forest Centre, Slovakia (2020, March 20). Forest GIS. Available online: http:\/\/gis.nlcsk.org\/lgis\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0378-1127(00)00460-6","article-title":"Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests","volume":"146","author":"Ketterings","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3552","DOI":"10.1109\/TGRS.2018.2885683","article-title":"Ratio-Based Multitemporal SAR Images Denoising: RABASAR","volume":"57","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"016008","DOI":"10.1117\/1.JRS.12.016008","article-title":"Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data","volume":"12","author":"Laurin","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"063588","DOI":"10.1117\/1.JRS.6.063588","article-title":"Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach","volume":"6","author":"Kumar","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/36.917903","article-title":"The seasonal behavior of interferometric coherence in boreal forest","volume":"39","author":"Koskinen","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/36.843016","article-title":"Effects of environmental conditions on boreal forest classification and biomass estimates with SAR","volume":"38","author":"Ranson","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4001","DOI":"10.1109\/TGRS.2009.2023906","article-title":"Signatures of ALOS PALSAR L-Band Backscatter in Swedish Forest","volume":"47","author":"Santoro","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ackermann, N. (2015). Growing Stock Volume Estimation in Temperate Forested Areas Using a Fusion Approach with SAR Satellites Imagery, Springer.","DOI":"10.1007\/978-3-319-13138-2"},{"key":"ref_41","first-page":"126","article-title":"Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems","volume":"66","author":"Mura","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/2150704X.2017.1295479","article-title":"Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem","volume":"8","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_44","unstructured":"Hoscilo, A., Lewandowska, A., Zi\u00f3lkowski, D., Stere\u0144czak, K., Lisa\u0144czuk, M., Schmullius, C., and Pathe, C. (2018, January 22\u201327). Forest Aboveground Biomass Estimation Using a Combination of Sentinel-1 and Sentinel-2 Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12040-016-0692-z","article-title":"Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass","volume":"125","author":"Sinha","year":"2016","journal-title":"J. Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-015-0021-x","article-title":"Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania","volume":"10","author":"Mauya","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Schlund, M., and Davidson, M.W.J. (2018). Aboveground Forest Biomass Estimation Combining L- and P-Band SAR Acquisitions. Remote Sens., 10.","DOI":"10.3390\/rs10071151"},{"key":"ref_48","first-page":"43","article-title":"N\u00e1rodn\u00e1 inventariz\u00e1cia a monitoring lesov Slovenskej republiky 2015\u20132016","volume":"65","year":"2017","journal-title":"Lesn\u00edcke \u0160t\u00fadie"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2488\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:02Z","timestamp":1760163842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":48,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132488"],"URL":"https:\/\/doi.org\/10.3390\/rs13132488","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}