Mikko Kukkonen, Mari Myllymäki, Janne Räty, Petri Varvia, Matti Maltamo, Lauri Korhonen, Petteri Packalen
{"title":"波段配置和季节性对利用无人机系统图像数据预测常见北方树种的影响","authors":"Mikko Kukkonen, Mari Myllymäki, Janne Räty, Petri Varvia, Matti Maltamo, Lauri Korhonen, Petteri Packalen","doi":"10.1186/s13595-024-01251-w","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Key message</h3><p>Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees.</p><h3 data-test=\"abstract-sub-heading\">Context</h3><p>The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.</p><h3 data-test=\"abstract-sub-heading\">Aims</h3><p>The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Tree species proportions (0–1) and volumes (m<sup>3</sup> ha<sup>−1</sup>) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.</p>","PeriodicalId":7994,"journal":{"name":"Annals of Forest Science","volume":"8 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Band configurations and seasonality influence the predictions of common boreal tree species using UAS image data\",\"authors\":\"Mikko Kukkonen, Mari Myllymäki, Janne Räty, Petri Varvia, Matti Maltamo, Lauri Korhonen, Petteri Packalen\",\"doi\":\"10.1186/s13595-024-01251-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Key message</h3><p>Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees.</p><h3 data-test=\\\"abstract-sub-heading\\\">Context</h3><p>The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.</p><h3 data-test=\\\"abstract-sub-heading\\\">Aims</h3><p>The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>Tree species proportions (0–1) and volumes (m<sup>3</sup> ha<sup>−1</sup>) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression.</p><h3 data-test=\\\"abstract-sub-heading\\\">Results</h3><p>Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. 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Band configurations and seasonality influence the predictions of common boreal tree species using UAS image data
Key message
Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees.
Context
The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.
Aims
The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots.
Methods
Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression.
Results
Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data.
Conclusion
The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.
期刊介绍:
Annals of Forest Science is an official publication of the French National Institute for Agriculture, Food and Environment (INRAE)
-Up-to-date coverage of current developments and trends in forest research and forestry
Topics include ecology and ecophysiology, genetics and improvement, tree physiology, wood quality, and silviculture
-Formerly known as Annales des Sciences Forestières
-Biology of trees and associated organisms (symbionts, pathogens, pests)
-Forest dynamics and ecosystem processes under environmental or management drivers (ecology, genetics)
-Risks and disturbances affecting forest ecosystems (biology, ecology, economics)
-Forestry wood chain (tree breeding, forest management and productivity, ecosystem services, silviculture and plantation management)
-Wood sciences (relationships between wood structure and tree functions, and between forest management or environment and wood properties)