Jingyi Ding

Ecosystem service trade-offs and their influencing factors: A case study in the Loess Plateau of China

Soil erosion control (SEC), carbon sequestration (CAS), and soil moisture (SMO) strongly interact in the semi-arid Loess Plateau. Since SMO has supportive effects on SEC and CAS, it can be considered as ecosystem service (ES), and there is an immediate need to coordinate the relationships among these ecosystem services (ESs) to promote the sustainability of vegetation recovery. In this study, we quantified the ESs, ES trade-offs, and the environmental factors in 151 sample plots in the Ansai watershed, and we used a redundancy analysis (RDA) to clarify the effects of environmental factors on these ESs and their trade-offs. The results were as follows: (1) the general trend in the SEC of vegetation types was Robinia pseudoacacia (CH)>native grass (NG)>small arbor (ST)>Hippophae rhamnoides (SJ)>artificial grass (AG)>Caragana korshinskii (NT)>apple orchard (GY)>crop (CP); the CAS trend was CH>SJ>NT>AG>CP>ST>GY>NG; and the SMO trend was CP>NG>GY>AG>SJ>ST>CH>NT. (2) For SEC-SMO trade-offs, the influence of vegetation type, altitude, silt and sand composition was dominant. The arrangement of NG, AG, and SJ could decrease the extent of the trade-offs. (3) For CAS-SMO trade-offs, vegetation coverage and types were the dominant factors, but the effects were not complex. The extent of these trade-offs was lowest for NT, and that for SJ was the second lowest. (4) Considering the relationships among the three ESs, SJ was the most appropriate afforestation plant. Combing the vegetation types, slope position, slope gradient, and soil properties could regulate these ES relationships. The dominant factors influencing ES trade-offs varied among the different soil layers, so we must consider the corresponding influencing factors to regulate ESs. Moreover, manual management measures were also important for coordinating the ES relationships. Our research provides a better understanding of the mechanisms influencing the relationships among ESs.

Landscape service capability, landscape service flow and landscape service demand: A new framework for landscape services and its use for landscape sustainability assessment

Creating methods to achieve sustainable development is a global challenge faced by civilization in the 21st century. As an operational element of sustainability science, landscape sustainability science (LSS) plays an important role in the development of methods for sustainable development. Landscape services (LS) is a newly emerging concept associated with ecosystem services (ES) that exhibits great potential for promoting landscape sustainability research despite its nascent stage of development. In this article, the historical development of the LS concept is reviewed, and the special implications and advantages of LS relative to ES for landscape practices are further expanded. Furthermore, a sustainability-oriented LS conceptual framework specifically developed for the integration of LS and landscape sustainability research is proposed. We refer to this framework as the landscape service capability-flow-demand (LSCFD) framework. Finally, the prospects for the application of the new framework in landscape sustainability assessments are explored. By using LSCFD, we suggest that a distinction be made between landscape service capacity (LSC), landscape service flow (LSF), and landscape service demand (LSD). LSC refers to the long-term potential of a landscape for producing various types of materials, energy, information, conditions, and effectiveness that are valued by people. LSF refers to the transmission process for material, energy, information, conditions and effectiveness from a landscape to benefit people that occur either within or across the landscape. LSD is the societal dimension of LS and refers to the types and volume of material, energy, information, conditions, and effectiveness that a landscape’s inhabitants need to satisfy their existence, livelihood, and development. Based on the LSCFD framework, landscape sustainability assessments can be performed by considering the following four areas: LSC sustainability, LSF sustainability, LSD sustainability, and the dynamic equilibrium relationships among the other three areas. Thus, various types of LS capabilities, integrated services capabilities, and the diversity and balance of LS demands should be evaluated. Additionally, analyzing the supplying regions of LS flow, spatial orientation of the population that benefits, transmission media, transmission mechanism, and transmission limiting factors is essential to explore the dynamic equilibrium relationships between LS capability, flow, and demand. The LSCFD concept framework of LS provides a method for implementing LSS into actual practice. In the context of global environmental changes and sustainable development, the LSCFD framework of LS will definitely contribute to future research.

Estimation of the cover and management factor based on stratified coverage and remote sensing indices: a case study in the Loess Plateau of China

PurposeWe attempt to describe the cover and management (C) factor more comprehensively through the use of a simple and efficient method.Materials and methodsWe measure the coverage of each vegetation layer and C factor for 152 sampled plots in the Ansai watershed. We propose four stratified coverage indices (green coverage (VG), total coverage (VT), probability coverage (VP), weight coverage (VW)), derive green and yellow vegetation indices from Landsat 8 OLI images to reflect green and residue cover, and construct and validate C factor estimation models from stratified coverage and remote sensing indices, respectively.Results and discussion(1) VT and VP present C factor estimation advantages for grassland and shrub land. VW can better illustrate the C factor due to the relatively complete spatial structuring of woodland and orchard land. For cropland, four stratified coverage indices present the same estimation capacities for the C factor. Except for cropland and grassland, the estimation capabilities of VG are relatively low because the residue layer is ignored. (2) The C factor is more sensitive to yellow vegetation indices, which indicates that senescent fractional cover and litter are important and cannot be ignored. The linear and non-linear models can explain 56.6 and 61.8% of C factor variation, respectively, and the linear model is more accurate than the non-linear model. (3) Compared to traditional indices (projective coverage and single remote sensing indices), stratified coverage indices and a combination of several remote sensing indices can estimate the C factor more effectively.ConclusionsAt the field scale, the C value estimation model can be selected according to the land-use type. At the watershed and regional scales, a linear model is recommended for C factor estimation.