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| 2. |
- Wang, Lanhui, et al.
(författare)
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Asymmetric patterns and temporal changes in phenology-based seasonal gross carbon uptake of global terrestrial ecosystems
- 2020
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Ingår i: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 29:6, s. 1020-1033
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To study global patterns and temporal changes in the seasonal dynamics (quantity and seasonal distribution) of terrestrial gross carbon uptake in response to global environmental change. Location: Global. Time period: 2000–2016. Major taxa studied: Terrestrial ecosystems. Methods: Following a phenology-based definition of photosynthetic seasonality, we decompose gross primary production (GPP) into three periods, green-up, maturity and senescence, and derive their corresponding GPP (GPPgp, GPPmp and GPPsp, respectively) from a newly developed time series of satellite-based global GPP to study spatio-temporal dynamics of seasonal GPP. Results: We find that the global fraction of GPPsp (19.8%) is larger than GPPgp (14.3%), indicating a globally asymmetric seasonal distribution of gross carbon uptake by terrestrial ecosystems. Globally, GPPmp plays a dominant role in shaping spatial patterns and increasing/decreasing trends in GPP, while GPPgp/GPPsp contributes to increasing GPP at the regional scale. Higher fractions of GPPgp/GPPmp (lower of GPPsp), as well as the co-occurrence of increasing GPP and non-tree vegetation cover in major croplands, are likely to be caused by agricultural intensification. Global changes in GPPgp and GPPsp are closely related to changes in their seasonal distributions (R =.86/.8, respectively), whereas this relationship is weaker for GPPmp (R =.53). Finally, high correlations are observed between changes in GPPgp and GPPsp and changes in their durations (R =.78/.78, respectively), while GPPmp shows a relatively lower correlation with its duration (R =.67). Main conclusions: The asymmetric spatio-temporal patterns in the seasonal dynamics of global terrestrial gross carbon uptake found here have been substantially reshaped by anthropogenic land-use/cover changes and changes in photosynthetic phenology. Compared to calendar-based meteorological seasons more suitable for temperate/subpolar ecosystems, our phenology-based approach is expected to provide an alternative starting point for a better understanding of global spatio-temporal changes in the seasonal dynamics of terrestrial ecosystem processes and functioning under accelerating global change.
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| 3. |
- Wang, Lanhui, et al.
(författare)
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Tree cover and its heterogeneity in natural ecosystems is linked to large herbivore biomass globally
- 2023
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Ingår i: One Earth. - 2590-3330. ; 6, s. 1759-1770
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Tidskriftsartikel (refereegranskat)abstract
- Addressing intertwined crises of climate change and biodiversity loss is a pressing global challenge, with trees playing pivotal roles in promoting carbon sequestration and habitat diversity. However, there is a distinct knowledge gap concerning the global drivers shaping tree cover and its heterogeneity, particularly the roles and relative importance of large herbivores and fire compared to climatic and topo-edaphic conditions. Here, we deploy satellite observations of strictly protected areas worldwide to reveal that in regions where vegetation may be in disequilibrium with climate, high biomass of large herbivores, especially browsers, is inversely related to tree cover but positively associated with its spatial heterogeneity. Conversely, fire reduces both tree cover and heterogeneity. These results suggest that top-down megafauna effects on landscape-scale vegetation openness and heterogeneity manifest worldwide. Our finding supports the need to consider megafauna, particularly large herbivores, in ecosystem effects on climate change mitigation and conservation and restoration efforts through trophic rewilding.
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| 4. |
- Huang, Ke, et al.
(författare)
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The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 756
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Tidskriftsartikel (refereegranskat)abstract
- The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.
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| 5. |
- Wang, Lanhui, et al.
(författare)
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Accelerated cropland expansion into high integrity forests and protected areas globally in the 21st century
- 2023
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Ingår i: iScience. - : Elsevier BV. - 2589-0042. ; 26:4
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Tidskriftsartikel (refereegranskat)abstract
- Intact forests and protected areas (PAs) are central to global biodiversity conservation and nature-based climate change mitigation. However, cropland encroachment threatens the ecological integrity and resilience of their functioning. Using satellite observations, we find that a large proportion of croplands in the remaining forests globally have been gained during 2003–2019, especially for high-integrity forests (62%) and non-forest biomes (60%) and tropical forests (47%). Cropland expansion during 2011–2019 in forests globally has even doubled (130% relative increase) than 2003–2011, with high medium-integrity (190%) and high-integrity (165%) categories and non-forest (182%) and tropical forest biomes (136%) showing higher acceleration. Unexpectedly, a quarter of croplands in PAs globally were gained during 2003–2019, again with a recent accelerated expansion (48%). These results suggest insufficient protection of these irreplaceable landscapes and a major challenge to global conservation. More effective local, national, and international coordination among sustainable development goals 15, 13, and 2 is urgently needed.
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| 6. |
- Zheng, Meiduan, et al.
(författare)
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Ground-Based Hyperspectral Retrieval of Soil Arsenic Concentration in Pingtan Island, China
- 2023
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Ingår i: Remote Sensing. - 2072-4292. ; 15:17
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Tidskriftsartikel (refereegranskat)abstract
- The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide.
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