Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth

Laurin, Gaia Vaglio; Vittucci, Cristina; Tramontana, Gianluca; Ferrazzoli, P.; Guerriero, L.; Papale, Dario

doi:10.1111/gcb.15072

Cited by 19 publications

(5 citation statements)

References 61 publications

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“…However, most studies have focused on small regional scales of eddy covariance flux observations and fixed-point experiments. Although there have been studies using machine learning to upscale based on point observation data in order to achieve large-scale inversion [15][16][17], the upscaling process is a function of the flux data available and can induce uncertainty, particularly in regions that have few flux sites [18]. Therefore, the NEP inversion model based on remote sensing data is the guarantee for realizing large-area research, and it is more conducive to evaluating macroscopic patterns.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region

Guo

Song

et al. 2021

Remote Sensing

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The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m−2∙yr−1, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.

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Section: Introductionmentioning

confidence: 99%

Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region

Guo

Song

et al. 2021

Remote Sensing

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“…To understand how they may respond to future environmental change, it is of utmost importance to quantify the spatial and temporal distribution of their functional structure. To date, functional biogeography (Reichstein et al 2014, Violle et al 2014 has focused on a wide range of issues, including the analysis of temporal changes in a number of functional properties (Musavi et al 2017, Soranno et al 2019, Vaglio Laurin et al 2020. However, the study of spatial heterogeneity in functional properties as a means of understanding their functional composition has received much less attention (Jetz et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Patterns and trends in the spatial heterogeneity of land surface phenology of global forests

Girardello,

Checcherini,

Duveiller

et al. 2024

Environ. Res. Commun.

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Land-surface phenology is a widely used indicator of how terrestrial ecosystems respond to environmental change. The spatial variability of this plant functional trait has also been advocated as an indicator of the functional composition of ecosystems. However, a global-scale assessment of spatial patterns in the spatial heterogeneity of forest phenology is currently lacking. To address this knowledge gap, we developed an index based on satellite retrievals and used it to quantify phenological diversity across global forest biomes. We show that there is considerable variation in phenological diversity among biomes, with the highest overall levels occurring in arid and temperate regions. An analysis of the drivers of the spatial patterns revealed that temperature-related factors primarily determine the variation in phenological diversity. Notably, temperature seasonality and mean annual temperature emerged as the most significant variables in explaining this global-scale variability. Furthermore, an assessment of temporal changes over an 18-year period revealed strong climate-driven shifts of phenological diversity in boreal and arid regions, suggesting that there may be an ongoing widespread homogenisation of land surface phenology within forest ecosystems. Our findings ultimately contribute to the development of a novel Essential Biodiversity Variable, which may enable scientists and practitioners to quantify the functional composition of ecosystems at unprecedented spatial and temporal scales.

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“…Forest is the dominant component of the earth's biogeochemical system, which provides critical refuge for terrestrial biodiversity [1,2]. Accurate and up-to-date forest cover information is considered an important parameter to help humans to protect, conserve, monitor, and sustainably manage forests and ensure their ecological functions [3]. As one of the most forested tropical countries in mainland Southeast Asia [4], forest cover is particularly important in Myanmar.…”

Section: Introductionmentioning

confidence: 99%

“…(2) we compare the area and spatial agreement among different datasets; (3) we explore the driving factor within the two recognized impacting factors (i.e., terrain and climate) over the discrepant areas.…”

Section: Introductionmentioning

confidence: 99%

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

et al. 2021

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Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries.

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Monitoring tropical forests under a functional perspective with satellite‐based vegetation optical depth

Cited by 19 publications

References 61 publications

Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region

Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region

Patterns and trends in the spatial heterogeneity of land surface phenology of global forests

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

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