{"id":153737,"date":"2023-08-15T16:27:41","date_gmt":"2023-08-15T14:27:41","guid":{"rendered":"https:\/\/uniavisen.dk\/en\/event\/phd-defence-jaime-caballer-revenga-ign\/"},"modified":"2023-08-15T16:27:41","modified_gmt":"2023-08-15T14:27:41","slug":"phd-defence-jaime-caballer-revenga-ign","status":"publish","type":"event","link":"https:\/\/uniavisen.dk\/en\/event\/phd-defence-jaime-caballer-revenga-ign\/","title":{"rendered":"PhD defence: Jaime Caballer Revenga (IGN)"},"content":{"rendered":"

Supervisor:<\/strong>
\nProfessor Thomas Friborg, IGN, UCPH, DK<\/p>\n

Assessment committee:<\/strong>
\nProfessor Signe Normand, Dept. of Biology, Aarhus University, DK
\nProfessor Ioannis N. Athanasiadis, Wageningen University, NL
\nAssociate Professor Guy Schurgers (chair), IGN, UCPH, DK<\/p>\n

Abstract<\/strong>
\nCroplands and forests are key agents in the global carbon (C) cycle and their response to a warming\u00a0climate is largely uncertain as different land management practices and plant functional types (PFT)\u00a0may result in diverging feedback mechanisms to the local impacts of climate change (CC). Moreover,\u00a0accurate quantification of C stocks and of turnover values across ecosystems and scales remains challenging\u00a0beyond well-studied ecosystems that require ground-based instrumentation and long-term flux
\nmonitoring, and can only provide point-based estimates, therefore missing the two-dimensional (2D)\u00a0spatial variability.<\/p>\n

In conventionally managed croplands, a short growing season determines the yearly C uptake so\u00a0that the seasonal net primary productivity (NPP) quickly translates to built-up biomass within ca.\u00a090-120 days. The photoassimilated C is then allocated to physiological and structural functions within\u00a0the plant, therefore becoming the build up of biomass a decent proxy for the ecosystem sink efficiency.\u00a0Being aware of it, here, we aimed at measuring the biomass contained in the standing part of cereal crops\u00a0and, concurrently, surveying the three-dimensional (3D) structure of the ecosystem (Paper I). To that\u00a0end, we employed an unmanned aerial vehicle (UAV) carrying a light detection and ranging (LiDAR)\u00a0sensor. We then examined to what extent the total plant-mediated C accounts for the seasonal NPP\u00a0at the ecosystem scale, where the preliminary findings revealed favorable levels of agreement between\u00a0the LiDAR-based method and the eddy-covariance technique (Paper III).<\/p>\n

In forests, the growing season spans a longer time (April-November), but the yearly NPP does not\u00a0generally translate into structural growth that is measurable by discrete-beam LiDAR sensors. Therefore,\u00a0when investigating forests (Paper II), our approach focused on (i) understanding which community\u00a0processes mediate individual tree growth and (ii) testing whether a machine learning framework could\u00a0quantify how (and how much) forest structure partially conditions individual tree growth. To test this,\u00a0we conducted supervised regression experiments to predict individual tree biomass, in which we introduced\u00a0information about the spatial context of the individual tree subject as a distinguishing factor\u00a0between pairs of regression experiments. The results showed that context-aware regressions consistently\u00a0outperformed context-unaware regressions by up to 9.1% of root-mean-squared error (RMSE).\u00a0Remarkably, we encoded context using entirely dataset-native methods\u2014i.e., without using ancillary\u00a0data streams or expert-based assessments\u2014so that the methods developed can potentially be applied\u00a0to other regions and scales.<\/p>\n

As our methods draw on near-field remote sensing (RS) techniques, we have focused solely on the\u00a0above-ground component of biomass (i.e., AGB) and have not considered the below-ground aspects.\u00a0Therefore, developing supervised regression methods for AGB prediction has been our main objective.\u00a0Fortunately, in the case of conventional croplands, the below-ground biomass (BGB) is largely determined\u00a0by management practices. However, for forests, the systematic observation and prediction of\u00a0BGB is beyond our scope due to its inherent complexity.<\/p>\n

At the national scale (Paper IV), the availability of large datasets allowed us to test Deep Learning\u00a0(DL) regression based on 3D convolutions, using airborne laser scanning(ALS) point cloud data (PCD)\u00a0scenes, to predict forest biomass across the Danish territory without the need for intermediate data\u00a0representations or feature engineering procedures. Results showed that prediction accuracy in test sets\u00a0outperformed state-of-the-art methods without overfitting the models, thereby indicating a significant\u00a0methodological shift in AGB mapping initiatives at large scales.<\/p>\n

In conclusion, the results and the methods we developed illustrate how above-ground carbon (AGC)\u00a0can be accurately estimated from mobile platforms and LiDAR sensors by leveraging machine and deep\u00a0learning regression methods at both the ecosystem and regional scales, thereby facilitating applications\u00a0to more accurate C cycling assessments, precision agriculture and quantitative ecology.<\/p>\n

A digital version of the PhD thesis can be obtained from the PhD secretary: phd@ign.ku.dk<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Supervisor: Professor Thomas Friborg, IGN, UCPH, DK Assessment committee: Professor Signe Normand, Dept. of Biology, Aarhus University, DK Professor Ioannis N. Athanasiadis, Wageningen University, NL Associate Professor Guy Schurgers (chair), IGN, UCPH, DK Abstract Croplands and forests are key agents in the global carbon (C) cycle and their response to a warming\u00a0climate is largely uncertain […]<\/p>\n","protected":false},"author":0,"featured_media":153738,"template":"","class_list":["post-153737","event","type-event","status-publish","has-post-thumbnail","hentry","event_category-phd-thesis-defense"],"acf":[],"aioseo_notices":[],"yoast_head":"\nPhD defence: Jaime Caballer Revenga (IGN)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/en\/event\/phd-defence-jaime-caballer-revenga-ign\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PhD defence: Jaime Caballer Revenga (IGN)\" \/>\n<meta property=\"og:description\" content=\"Supervisor: Professor Thomas Friborg, IGN, UCPH, DK Assessment committee: Professor Signe Normand, Dept. of Biology, Aarhus University, DK Professor Ioannis N. Athanasiadis, Wageningen University, NL Associate Professor Guy Schurgers (chair), IGN, UCPH, DK Abstract Croplands and forests are key agents in the global carbon (C) cycle and their response to a warming\u00a0climate is largely uncertain […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/uniavisen.dk\/en\/event\/phd-defence-jaime-caballer-revenga-ign\/\" \/>\n<meta property=\"og:site_name\" content=\"University Post\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uniavis\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@Uniavisen\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/\",\"name\":\"PhD defence: Jaime Caballer Revenga (IGN)\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/ph.d.jaime_.jpg\",\"datePublished\":\"2023-08-15T14:27:41+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/#primaryimage\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/ph.d.jaime_.jpg\",\"contentUrl\":\"https:\\\/\\\/uniavisen.dk\\\/wp-content\\\/uploads\\\/2023\\\/08\\\/ph.d.jaime_.jpg\",\"width\":452,\"height\":297},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/event\\\/phd-defence-jaime-caballer-revenga-ign\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/uniavisen.dk\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\\\/\\\/uniavisen.dk\\\/event\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"PhD defence: Jaime Caballer Revenga (IGN)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/uniavisen.dk\\\/#website\",\"url\":\"https:\\\/\\\/uniavisen.dk\\\/\",\"name\":\"University Post\",\"description\":\"Independent of management\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/uniavisen.dk\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"PhD defence: Jaime Caballer Revenga (IGN)","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/uniavisen.dk\/en\/event\/phd-defence-jaime-caballer-revenga-ign\/","og_locale":"en_US","og_type":"article","og_title":"PhD defence: Jaime Caballer Revenga (IGN)","og_description":"Supervisor: Professor Thomas Friborg, IGN, UCPH, DK Assessment committee: Professor Signe Normand, Dept. of Biology, Aarhus University, DK Professor Ioannis N. 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