Refined assessment of space-time changes, influencing factors and socio-economic impacts of the terrestrial ecosystem quality: A case study of the GBA.

The terrestrial ecosystem is the cradle of energy and material basis for human survival and development. However, there are large research deficits in accurately and finely depicting the quality of the terrestrial ecosystem (QTE) and assessing its changing triggers' contribution. Here, we summarized three major principles for selecting image sources in remote sensing data fusion. A continuous 30-m net vegetation productivity (NPP) dataset during 2000-2019 for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was derived by using the Carnegie-Ames-Stanford approach model and pre-fused normalized difference vegetation index. The factors' contributions to the QTE changes were quantitatively assessed. The role of the QTE in affecting the socio-economic and its behind mechanisms was quantitatively investigated. The results showed that: (1) High-quality images sources are the preference for spatio-temporal fusion of remote sensing data. Images with close month, the same season and year, and sensors should be then selected. Images of different sensors with similar spectral bandwidth, the ones from adjacent years and seasons, can be alternately considered. (2) Fine-resolution NPP has higher accuracy than coarse-resolution NPP and has marked advantages in finely characterizing the QTE. In the past 20 years, the QTE in the GBA has shown a fluctuating increasing trend (0.20 Tg C/yr). (3) Human activities contributed 54.19% of the QTE changes in the GBA, and dominates the QTE changes in the central rapidly urbanizing areas. Residual factors accounted for an overall contribution ratio of 35.71%. Climate change dominants the peripheral forest variations in the GBA. (4) In the GBA, the improvement of QTE has a significant positive socio-economic impact, it contributes to the GDP increment firstly then the GDP aggregate indirectly. Our results highlight that it is of great urgent to estimate long-term continuous NPP with high spatio-temporal resolution globally. Controlling strategies should be implemented to reduce factitious impacts on QTE. High level of ecological and environmental protection promotes the sustainable development.

An improved algorithm for estimating the Secchi disk depth of inland waters across China based on Sentinel-2 MSI data.

Accurate remote sensing of the Secchi disk depth (ZSD) in waters is beneficial for large-scale monitoring of the aquatic ecology of inland lakes. Herein, an improved algorithm (termed as ZSD20 in this work) for retrieving ZSD was developed from field measured remote sensing data and is available for various waters including clear waters, slightly turbid waters, and highly turbid waters. The results show that ZSD20 is robust in estimating ZSD in various inland waters. After further validation with an independent in situ dataset from 12 inland waters (0.1 m < ZSD < 18 m), the developed algorithm outperformed the native algorithm, with the mean absolute square percentage error (MAPE) reduced from 32.8 to 19.4%, and root mean square error (RMSE) from 0.87 to 0.67 m. At the same time, the new algorithm demonstrates its generality in various mainstreaming image data, including Ocean and Land Color Instrument (OLCI), Geostationary Ocean Color Imager (GOCI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Finally, the algorithm's application was implemented in 410 waters of China based on Sentinel-2 MSI imagery to elucidate the spatiotemporal variation of water clarity during 2015 and 2021. The new algorithm reveals great potential for estimating water clarity in various inland waters, offering important support for protection and restoration of aquatic environments.

Global urban expansion offsets climate-driven increases in terrestrial net primary productivity.

The global urbanization rate is accelerating; however, data limitations have far prevented robust estimations of either global urban expansion or its effects on terrestrial net primary productivity (NPP). Here, using a high resolution dataset of global land use/cover (GlobeLand30), we show that global urban areas expanded by an average of 5694 km2 per year between 2000 and 2010. The rapid urban expansion in the past decade has in turn reduced global terrestrial NPP, with a net loss of 22.4 Tg Carbon per year (Tg C year-1). Although small compared to total terrestrial NPP and fossil fuel carbon emissions worldwide, the urbanization-induced decrease in NPP offset 30% of the climate-driven increase (73.6 Tg C year-1) over the same period. Our findings highlight the urgent need for global strategies to address urban expansion, enhance natural carbon sinks, and increase agricultural productivity.

Determining the impacts of climate change and urban expansion on terrestrial net primary production in China.

Climate change and urbanization strongly affect the variations of terrestrial net primary production (NPP), but the relative contributions of these two factors to NPP changes have not been determined yet (especially on a macroscale). In this study, spatial-temporal variations of NPP in China from 2000 to 2010 were estimated using the Carnegie-Ames-Stanford Approach model, and the effects induced by urbanization and climate change were quantified. The obtained results showed that during the study period, the NPP in China exhibited an annual increase of 0.03 Pg C accompanied by large spatial heterogeneities. During the whole study period, the urban area in China increased by 16.44 × 103 km2, and the corresponding NPP losses amounted to 11.60 × 10-3 Pg C. Urban expansion significantly offset the climate change-induced NPP increases and worsened NPP decreases (the offsetting ratio calculated for China was 5.42%, and its exact magnitudes varied by province). The largest NPP variations were observed over the regions with rapid urban expansion, whose contribution ratio was 32.20% for China and exceeded 30% for most provinces. Climate change contributed considerably to the NPP variations in both the newly urbanized (30.45%) and purely vegetated (46.92%) areas, but its contribution ratios were slightly lower than those of residual factors. Moreover, climate change strongly affected the NPP levels over the arid and semi-arid regions as well as over the Tibet Plateau; however, residual factors dominated the NPP variations over the central and southeast China. Our study highlights a significant role of urbanization in driving terrestrial NPP variations on a macroscale and provides a new perspective on disentangling the impacts of external factors on NPP values.

Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data.

Urbanization has profoundly altered the terrestrial ecosystem carbon cycle, especially the net primary productivity (NPP). Many attempts have been made to assess the influence of urbanization on NPP at coarse resolutions (e.g., 250m or larger), which may ignore many smaller and highly fragmented urban lands, and to a large extent, underestimate the NPP variations induced by urban sprawl. Hence, we attempted to analyze the NPP variations influenced by urban sprawl at a fine resolution (e.g., 30m), toward which the accuracy of NPP was improved using remotely sensed data fusion algorithm. In this paper, this assumption was tested in the Pearl River Delta of China. The land cover datasets from the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) were acquired to quantify the urban sprawl. The synthetic Normal Differential Vegetation Index (NDVI) data was obtained by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI via spatiotemporal fusion algorithm. The Carnegie-Ames-Stanford Approach (CASA) model was driven by land cover map, synthetic NDVI and meteorological data to estimate the 30-m resolution NPP. Then, we analyzed the influence of urban sprawl on 30-m resolution NPP during the period of 2001-2009. Additionally, we also simulated the spatiotemporal change of future urban sprawl under different scenarios using the Future Land Use Simulation (FLUS) model, and further analyzed its influence on 30-m resolution NPP. Our results showed that the accuracy of 30-m resolution NPP from synthetic NDVI is better than 500-m resolution NPP from MODIS NDVI. The loss in 30-m resolution NPP due to urban sprawl was much higher than 500-m resolution NPP. Moreover, the harmonious development scenario, characterized by a reasonable size of urban sprawl and a corresponding lower NPP loss from 2009 to 2050, would be considered as a more human-oriented and sustainable development strategy.