Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data

Guo, Meng; Li, Jing; Yu, Fangbing; Yin, Shuai; Huang, Shubo; Wen, Lixiang

doi:10.1071/wf20162

Cited by 7 publications

(10 citation statements)

References 58 publications

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“…Each second, OCO-2 acquires 24 data points with a footprint of 1.3 km × 2.25 km. The precision of the OCO-2 SIF is better than 0.05 W m −2 sr −1 µm −1 with just a single detector footprint in the ideal case of an entirely opaque Fraunhofer line [8,34,35].…”

Section: Oco-2 Sif Datamentioning

confidence: 96%

“…The Greater Khingan Mountains are part of the coniferous forests of northern Eurasia and constitute the largest virgin forest in China. The zonal vegetation is typical taiga type composed principally of Dahurian larch (Larix gmelinii), deciduous broadleaf birch (Betula platyphylla), and aspen (Populus davidiana and Populus suaveolens) [6,8]. Elevations in the area range from 260 m to 1700 m. The Greater Khingan Mountains experience a cold temperate and continental monsoon climate with an annual average temperature of −2.8 • C and a mean annual precipitation of 350-500 mm [31,32].…”

Section: Study Regionmentioning

confidence: 99%

“…Furthermore, some of these VIs can experience a saturation effect in cases where the target vegetation is particularly dense. As an alternative, solar-induced chlorophyll fluorescence (SIF) has been widely used in studies concerned with monitoring vegetation physiology, given its functional connection with plant photosynthesis [5][6][7][8]. The terrestrial ecosystem carbon cycle has been studied for decades by using time serious remote sensing data, as the cycle is directly related to the actual photochemistry of plants [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China

Guo

et al. 2022

Remote Sensing

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Solar-induced chlorophyll fluorescence (SIF), when used as a proxy for plant photosynthesis, can provide an indication of the photosynthesis rate and has the potential to improve our understanding of carbon exchange mechanisms within an ecosystem. However, the relationships between SIF and vegetation indices (VIs) operating within different ecological contexts and the effect of other environmental factors on SIF remain unclear. This study focused on three ecosystems (cropland, forest, and grassland), with different ecological characteristics, located in Northeast China. These areas provide case studies where numerous relationships can be explored, including the correlations between the Orbiting Carbon Observatory-2 (OCO-2) SIF and MODIS products, meteorological factors, and the differences in the relationships between the three different ecosystems. Some interesting results and conclusions were obtained. First, in different ecosystems, the relationships between SIF and MODIS products show different correlations, whereby the enhanced vegetation index (EVI) has a close relationship with SIF in all the three ecosystems of forest, cropland, and grassland. Second, forest-type ecosystems appear to be sensitive to changes in daily temperature, whereas cropland and grassland areas respond more closely to changes in previous 16-day daily minimum temperature. Compared with forest and cropland areas, grasslands were more sensitive to precipitation (although the R2 value was small). Third, different ecosystems have different mechanisms of photosynthesis. Hence, we suggest that it is better to use SIF in areas exhibiting different ecological characteristics, and different models should be employed while simulating SIF.

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Section: Oco-2 Sif Datamentioning

confidence: 96%

Section: Study Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China

Guo

et al. 2022

Remote Sensing

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“…Vegetation growth and recovery in burned forests are affected by solar radiation, hydrological conditions, soil nutrients, and other factors (Liu et al 2017;Whitman et al 2018a;Zhang et al 2021a). Accurate evaluation of postfire vegetation recovery using species diversity indicators (Gonzalez-Tagle et al 2008), especially under different topographic conditions, provides a scientific basis for forest decision-making and management (Francos et al 2020;Liu et al 2020;Guo et al 2021;Oettel and Lapin 2021).…”

Section: Introductionmentioning

confidence: 99%

Forest succession trajectories after fires in valleys and on slopes in the Greater Khingan Mountains, China

et al. 2023

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Accurate assessment of postfire vegetation recovery is important for forest management and the conservation of species diversity. Topography is an important factor affecting vegetation recovery but whether species composition varies with different recovery stages and between valleys and slopes is unclear. Using field data and a space-for-time substitution method, we quantified species richness and diversity to obtain the successional trajectories of valleys and slopes. We surveyed the species of 10 burned areas from 1986 to 2010 in the Greater Khingan Mountains in northeastern China, and found that with increasing postfire recovery time, species richness in both valleys and slopes gradually decreased. However, species richness in valleys was relatively higher. Shrubs recovered rapidly in the valleys, and species diversity maximized approximately 11 years after fire. However, it maximized 17–18 years after fire on the slopes. Numerous shade-tolerant species were present in the valleys 11 years after fire but not until after 18 years on slopes. Larch appeared earlier than 11 years after fire and its recovery was slow in the valleys but appeared quickly on slopes and established dominance early. Our study provides some new insights into vegetation succession after fire at local scales. After fire, the vegetation recovery processes differ with topography and it affects the initial rate of recovery and species composition at different successional stages.

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“…A considerable amount of literature on post-fire vegetation recovery has been published. Though these works studied a variety of forest ecosystems (Bartels et al 2016;Hao et al 2022), fewer works defined the different succession stages (Meng et al 2015;João et al 2018;Viana-Soto et al 2020), and few have been applied to the Great Xing'An Range (Shi et al 2019;Guo et al 2021). Our study focuses on classifying and identifying vegetation recovery in burned areas at different stages and their driving factors in the Great Xing'An Range.…”

Section: Introductionmentioning

confidence: 99%

Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia

Zhang

Homayouni

Zhao

et al. 2023

Int. J. Wildland Fire

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Forest fire is one of the most important factors that alter a forest ecosystem's biogeochemical cycle. Large-scale distributed burned areas lose their original vegetation structure and are more impacted by climate change in the vegetation recovery process, thus making it harder to restore their original vegetation structure. In this study, we used historical Landsat imagery and the LandTrendr algorithm in the Google Earth Engine platform to study and identify post-fire stages in the Great Xing'An Range of Inner Mongolia. Moreover, we categorized different post-fire vegetation recovery trajectories. The usefulness of spectral indices was also evaluated in the study region. We applied the Geodetector model to analyze the driving factors of the burned area vegetation regeneration process. The results show that burn severity and earth-atmosphere hydrological cycle are two main impacting factors in the short term after the fire (e.g. 5-6 years). Other climatical conditions affect vegetation recovery, including prolonged vegetation recovery process, hydrothermal circulation process and topographical conditions, seasonally frozen soil, freeze-thaw processes, and climate events. This study improves understanding of the dynamic successional processes in the burned area and the driving factors. Also, the outcomes can facilitate and support sustainable forest management of the Great Xing'An Range.

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Estimation of post-fire vegetation recovery in boreal forests using solar-induced chlorophyll fluorescence (SIF) data

Cited by 7 publications

References 58 publications

Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China

Solar-Induced Chlorophyll Fluorescence Trends and Mechanisms in Different Ecosystems in Northeastern China

Forest succession trajectories after fires in valleys and on slopes in the Greater Khingan Mountains, China

Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia

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