Land degradation monitoring using terrestrial ecosystem carbon sinks/sources and their response to climate change in China

Abstract

Global warming, which is mainly caused by greenhouse gases, can greatly aggravate land degradation; therefore, the examination of the NEP (net ecosystem productivity) and the analysis of its response to climate change are very critical for understanding carbon cycling. Based on Moderate Resolution Imaging Spectroradiometer data, meteorological data, and soil organic carbon data, this study examined the NEP from 2000 to 2013 and investigated how ongoing climate change affects the NEP. The study results indicate that the terrestrial ecosystems in China generally act as net carbon sinks with increasing NEP values. The western inland region and part of northeast China mainly act as carbon sources, with the NEP exhibiting an increasing trend, whereas the other regions mainly act as carbon sinks, with the NEP showing a decreasing trend across large areas of southern China, where the most obvious land degradation occurs. Homogeneity and heterogeneity co‐occur. The general pattern is that ecosystems with high biomass usually have a high NEP value, acting as high carbon sinks in relatively wet and warm environments, but have a low value and even act as carbon sources in dry and cold environments. Both moderate precipitation and temperature are essential in increasing the NEP, whereas lower precipitation and temperatures might have negative effects. Heterogeneity also widely breaks up the general pattern. Temporally, more NEP grids were positively correlated with changes in temperature and showed stronger correlation coefficients with temperature than with precipitation, but the grids showing a significant correlation with these factors accounted for only a small proportion of the total for both precipitation and temperature.

Spatial Heterogeneity of the Carbon Emission Effect Resulting from Urban Expansion among Three Coastal Agglomerations in China

“…where NEP is the annual net ecosystem productivity (gC•m −2 .year −1 ), NPP is the annual net primary productivity (gC•m −2 •year −1 ) directly provided by the MODIS products, and Rh is the annual soil heterotrophic respiration (gC•m −2 •year −1 ) [35].…”

“…In this study, Rh was calculated from the relationship between Rs and Rh [35]. We collected Rs and Rh data from up to 101 groups, with the observed years from 1994 to 2013.…”

“…It is remote-sensing capabilities that a large number of researchers have previously focused on in studying carbon sources and sinks. Chuai et al examined the net ecosystem production (NEP) trend, an indicator of whether an ecosystem can fix or release carbon from or into the atmosphere using Moderate Resolution Imaging Spectroradiometer data, meteorological data, and soil organic carbon data [35]. Abdalla and Fadul assessed the relationship between green cover and carbon emissions from cars using remote-sensing data from Landsat and Quick Bird satellites [36].…”

Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017

“…Land degradation describes permanent changes in the capability of lands to support human activities (Abel & Blaikie, ). Land degradation could be possibly associated with three kinds of landscape changes engaging vegetation loss: urbanization, devegetation, and inundation (Chuai et al, ; Mao et al, ; Pekel, Cottam, Gorelick, & Belward, ). Mapping and quantifying large scale land degradation caused by landscape changes are very important for regional sustainable environment management (Burrell, Evans, & Liu, ).…”

“…It is recognized that urbanization and desertification have resulted in substantial land degradation in the past few decades, even in the wet region in China (Chuai et al, 2018;Mao et al, 2018). However, there is limited spatiotemporal explicit information on devegetation (a process from vegetated land to permanent bare lands, desertification in arid/semiarid regions) and their driving forces at the national scale (Feng, Ma, Jiang, Wang, & Cao, 2015).…”