A 33-Year NPP Monitoring Study in Southwest China by the Fusion of Multi-Source Remote Sensing and Station Data

Vegetation responses to extreme climatic indices in coastal China from 1986 to 2015

Climate extremes have resulted in substantial vegetation changes in the marine-terrestrial transitional zone. As a climatically-sensitive region, coastal China is currently experiencing prominent environmental climate change. To identify how climatic extremes affect ecosystem function, we calculated eleven indices of climatic extremes and four mean indices for six sub-regions of coastal China. Deseasonalized thirty-year (1986-2015) net primary productivity (NPP) was used as an indicator of ecosystem productivity, and its relationships with the climate indices were investigated at multiple scales (annual and seasonal) explicitly. The results demonstrated that: (1) annual NPP indicated an overall greening trend (in 73.71% of the study area) and partial degradation (in 26.29% of the study area) over the last thirty decades years; (2) coastal areas had experienced warming overall, with higher increases in nighttime temperatures relative to daytime temperatures; (3) in southern areas, maximum/ minimum daily maximum temperature had driven increases in NPP, whereas in northern areas, this effect varied between vegetation types; (4) Diurnal temperature range (DTR) and NPP were negatively correlated in the north and positively correlated in the south; and (5) Maximum 1-day precipitation promoted vegetation production across the whole study area. Maximum 5-day precipitation promoted vegetation growth in the north but had the opposite effect in the south. Our study advances understanding of vegetation dynamics and its driving mechanisms, and provides support for scientifically informed ecological management practices in coastal China.

Variations in vegetation dynamics and its cause in national key ecological function zones in China

Continued long-term monitoring of vegetation activity in national key ecological function zones (NKEFZs) has implications for national ecological security and sustainability in China. We used Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) dataset to map and analyze the spatiotemporal patterns of change in vegetation growth and their linkage with climate change and human activities in NKEFZs during 1982-2013. Statistically significant increases of growing season, spring, and autumn NDVI were observed during all or most periods while 25 NKEFZs are taken as a whole. Non-significant decreases of NDVI were found in 7 NKEFZs during a few periods, and obvious increases were observed during fifteen periods in all other NKEFZs. Vegetation growth in NKEFZs was mainly regulated by a thermal factor, and the dominant climatic drivers varied across different regions and seasons. The influence of temperature was stronger on vegetation activity in spring and autumn for those NKEFZs located in high latitudes and high elevations, while precipitation was the main climatic control factor for NKEFZs in the arid and semi-arid regions. The effects of human activity on the NDVI of NKEFZs were not ignored; a significant decrease of NDVI in the Sanjiang Plain may be related to the rapid change in land use from wetland into farmland.

A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity

Urbanization not only directly alters the regional ecosystem net primary productivity (NPP) through land-cover replacement, but it is also accompanied by huge indirect impacts due to the associated climate change and anthropogenic activities. However, to date, limited efforts have been made to quantitatively separate the two types of urbanization impacts, and the continuous variations over a long-time span are not well understood. In this study, both the long-term direct and indirect impacts of urbanization on NPP were established and analyzed based on multi-source remote sensing data, taking the city of Kunming in China as a case study area. The results indicated that the intense urbanization process has led to a continuous decrease in NPP from 1990 to 2014, due to the direct impact of land-cover replacement. Nevertheless, the urbanization has also resulted in an apparently positive indirect impact on NPP, which has offset about 30% of the direct impact in recent years. The increasing trend of the indirect impact was found to be higher than the NPP trend in the surrounding forest areas, which proves that vegetation growth has been promoted by the urban environment. The indirect impact has also shown great spatial and temporal heterogeneity, with generally higher values in the old city area and winter season. This can mostly be attributed to the distribution of temperature, i.e., the urban heat island effect, which has shown a significantly positive correlation with the indirect impact. However, the correlations between NPP and climatic factors were found to be completely different, which confirmed the need to separate the direct and indirect impacts. Overall, this study has demonstrated that urbanization has reduced the total NPP over the region, but has promoted some vegetation growth, and the knowledge of the indirect impact will help to support urban greening planning.

Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.