{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T13:28:38Z","timestamp":1790515718597,"version":"4.1.0"},"reference-count":67,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,4]],"date-time":"2017-03-04T00:00:00Z","timestamp":1488585600000},"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>Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg\u00b7ha\u22121. The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture\u2013AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture\u2013AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km2 in the WG, with a mean relative per-pixel propagated error &lt;20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.<\/jats:p>","DOI":"10.3390\/rs9030228","type":"journal-article","created":{"date-parts":[[2017,3,9]],"date-time":"2017-03-09T06:56:43Z","timestamp":1489042603000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Inverting Aboveground Biomass\u2013Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery"],"prefix":"10.3390","volume":"9","author":[{"given":"Sourabh","family":"Pargal","sequence":"first","affiliation":[{"name":"AMAP Lab, IRD, INRA, CIRAD, CNRS, University of Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rakesh","family":"Fararoda","sequence":"additional","affiliation":[{"name":"National Remote Sensing Centre, Hyderabad 500037, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gopalakrishnan","family":"Rajashekar","sequence":"additional","affiliation":[{"name":"National Remote Sensing Centre, Hyderabad 500037, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-3865","authenticated-orcid":false,"given":"Natesan","family":"Balachandran","sequence":"additional","affiliation":[{"name":"Institut Fran\u00e7ais de Pondich\u00e9ry, UMIFRE 21 CNRS-MAEDI, Puducherry 605001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"R\u00e9jou-M\u00e9chain","sequence":"additional","affiliation":[{"name":"AMAP Lab, IRD, INRA, CIRAD, CNRS, University of Montpellier, 34000 Montpellier, France"},{"name":"Institut Fran\u00e7ais de Pondich\u00e9ry, UMIFRE 21 CNRS-MAEDI, Puducherry 605001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5323-3866","authenticated-orcid":false,"given":"Nicolas","family":"Barbier","sequence":"additional","affiliation":[{"name":"AMAP Lab, IRD, INRA, CIRAD, CNRS, University of Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandra","family":"Jha","sequence":"additional","affiliation":[{"name":"National Remote Sensing Centre, Hyderabad 500037, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rapha\u00ebl","family":"P\u00e9lissier","sequence":"additional","affiliation":[{"name":"AMAP Lab, IRD, INRA, CIRAD, CNRS, University of Montpellier, 34000 Montpellier, France"},{"name":"Institut Fran\u00e7ais de Pondich\u00e9ry, UMIFRE 21 CNRS-MAEDI, Puducherry 605001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5084-1367","authenticated-orcid":false,"given":"Vinay","family":"Dadhwal","sequence":"additional","affiliation":[{"name":"National Remote Sensing Centre, Hyderabad 500037, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Couteron","sequence":"additional","affiliation":[{"name":"AMAP Lab, IRD, INRA, CIRAD, CNRS, University of Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,4]]},"reference":[{"key":"ref_1","unstructured":"Carlowicz, M. 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