Assessing the Driving Forces in Vegetation Dynamics Using Net Primary Productivity as the Indicator: A Case Study in Jinghe River Basin in the Loess Plateau

Abstract

An objective and effective method to distinguish the influence of climate change and human activities on vegetation dynamics has great significance in the design and implementation of ecosystem restoration projects. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote data and the Miami and Carnegie-Ames-Stanford Approach (CASA) model, this study simulated and used net primary productivity (NPP) as an indicator to identify vegetation dynamics and their driving forces in the Jinghe River basin from 2000 to 2014. The results showed that: (1) The vegetation in the Jinghe River basin, which accounted for 84.4% of the study area, showed an increasing trend in NPP; (2) Human activities contributed most to vegetation restoration, which accounted for 54.5% of the areas; 24.0% of the areas showed an increasing trend in the NPP that was dominated by climate factors. Degradation dominated by human activities accounted for 4.3% of the study area, and degradation dominated by climate factors resulted in 17.2%; (3) The rate of vegetation degradation in areas dominated by climate factors rose with increased slope, where the arid climate caused shortages of water resources, and the human-dominated vegetation restoration activities exacerbated the vegetation's water demand further, which surpassed the carrying capacity of regional water resources and led ultimately to vegetation degradation. We recommend that future ecological restoration programs pay more attention to maintaining the balance between ecosystem restoration and water resource demand to maximize the benefits of human activities and ensure the vegetation restoration is ecologically sustainable.

Comparative Assessment of Vegetation Dynamics under the Influence of Climate Change and Human Activities in Five Ecologically Vulnerable Regions of China from 2000 to 2015

“…Due to the development of space science and technology, more and more studies have begun to use remote sensing data to estimate the NPP (Net Primary Productivity) of terrestrial ecosystems, and to study the spatial distribution of vegetation dynamics caused by different driving factors [12][13][14][15]. NPP refers to the net amount of solar radiation that vegetation converts to plant organic matter through photosynthesis [16].…”

“…We interpolated and resampled all of the data into a 250-m resolution using ArcGIS v10.2 software (Environmental Systems Research Institute, Inc., Redlands, CA, USA) [32,33]. According to the land use map and the NDVI data, we also excluded all NDVI pixels with an average value of less than 0.05 and excluded pixels with land-use types that were always bare land or urban areas from 2000 to 2015, so as to avoid the influence of clouds, bare land, and non-plant areas on the accuracy of the research results [8,14].…”

“…The result of NPP p indicates the annual potential NPP (g C•m −2 •year −1 ). For more information on the model calculation, refer to Zhou et al [34] and Wang et al [14].…”

“…T ε1 (x, t) and T ε2 (x, t) represent the temperature stress coefficients at low and high temperatures, respectively; W ε (x, t) is the water stress coefficient; and ε max indicates the maximum light energy conversion rate under ideal conditions. For more details on the parameters' calculation, refer to Feng et al [7] and Wang et al [14].…”

Climate Change Affects Forest Productivity in a Typical Climate Transition Region of China

“…Net primary productivity (NPP) of forests is very sensitive to the changes in CO 2 concentration and climate [7] and has obtained much attention in studies on global climate change [8]. Hence, forest NPP can be a crucial indicator investigating the responses of forest ecosystems to future climate [9]. Several experience-and process-based models are established to simulate the forest NPP.…”