{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:48:32Z","timestamp":1765295312900,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forest Competence Centre (ERDF)","award":["1.2.1.1\/18\/A\/004"],"award-info":[{"award-number":["1.2.1.1\/18\/A\/004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing extreme weather and climate events have a significant impact on the resistance and resilience of Norway spruce trees. The responses and adaptation of individual trees to certain factors can be assessed through the tree breeding programmes. Tree breeding programmes combined with multispectral unmanned aircraft vehicle (UAV) platforms may assist in acquiring regular information of individual traits from large areas of progeny trials. Therefore, the aim of this study was to investigate the vegetation indices (VI) to detect the early stages of tree stress in Norway spruce stands under prolonged drought and summer heatwave. Eight plots within four stands throughout the vegetation season of 2021 were monitored by assessing spectral differences of tree health classes (Healthy, Crown damage, New crown damage, Dead trees, Stem damage, Root rot). From all tested VI, our models showed a moderate marginal R2 and total explanatory power\u2014for Normalized Difference Red-edge Index (NDRE), marginal R2 was 0.26, and conditional R2 was 0.49 (p &lt; 0.001); for Normalized Difference Vegetation Index (NDVI), marginal R2 was 0.34, and conditional R2 was 0.60 (p &lt; 0.001); for Red Green Index (RGI), marginal R2 was 0.36, and conditional R2 was 0.55 (p &lt; 0.001); while for Chlorophyll Index (CI), marginal R2 was 0.27, and conditional R2 was 0.49 (p &lt; 0.001). The reliability of the identification of tree health classes for selected VI was weak to fair (overall classification accuracy ranged from 34.4% to 56.8%, kappa coefficients ranged from 0.09 to 0.34) if six classes were assessed, and moderate to substantial (overall classification accuracy ranged from 71.1% to 89.6% and kappa coefficient from 0.39 to 0.71) if two classes (Crown damage and Healthy trees) were tested.<\/jats:p>","DOI":"10.3390\/rs14092122","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"2122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Integration of Linear Model and \u2018Random Forest\u2019 Techniques for Prediction of Norway Spruce Vitality: A Case Study of the Hemiboreal Forest, Latvia"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0447-0838","authenticated-orcid":false,"given":"Endijs","family":"B\u0101ders","sequence":"first","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"given":"Ed\u017eus","family":"Rom\u0101ns","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"given":"Iveta","family":"Desaine","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"given":"Oskars","family":"Kri\u0161\u0101ns","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"given":"Andris","family":"Seipulis","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3365-0566","authenticated-orcid":false,"given":"J\u0101nis","family":"Donis","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]},{"given":"\u0100ris","family":"Jansons","sequence":"additional","affiliation":[{"name":"Latvian State Forest Research Institute \u201cSilava\u201d, R\u012bgas str. 111, LV-2169 Salaspils, Latvia"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1007\/s10113-015-0788-z","article-title":"Alternative forest management strategies to account for climate change-induced productivity and species suitability changes in Europe","volume":"15","author":"Schelhaas","year":"2015","journal-title":"Reg. 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