Pavel A. Dmitriev, Boris L. Kozlovsky, Anastasiya A. Dmitrieva
{"title":"Assessing the phenological state of evergreen conifers using hyperspectral imaging time series","authors":"Pavel A. Dmitriev, Boris L. Kozlovsky, Anastasiya A. Dmitrieva","doi":"10.1016/j.rsase.2024.101342","DOIUrl":null,"url":null,"abstract":"<div><p>Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: <em>Thuja occidentalis</em>, <em>Platycladus orientalis</em>, <em>Pinus sylvestris</em> and <em>P. nigra</em> subsp. <em>pallasiana</em>. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R<sup>2</sup> exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101342"},"PeriodicalIF":3.8000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938524002064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: Thuja occidentalis, Platycladus orientalis, Pinus sylvestris and P. nigra subsp. pallasiana. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R2 exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems