A Method for Quantifying the Impacts of Human Activities on Net Primary Production of Grasslands in Northwest China

Li, Chuanhua; Dou, Tianbao; Wang, Yutao; Zhu, Tongbin; Yin, Huanhuan; Zhou, Min; Liu, Lihui; Wu, Xiaodong

doi:10.3390/rs13132479

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…These factors are directly or indirectly effective on land degradation or restoration. Because LD is a multidimensional phenomenon that can be divided into three main groups including soil degradation, vegetation degradation, and water resources degradation (Liniger et al, 2019), NPP changes trend can be a suitable indicator and a new addition to assess the relative status of LD (Li et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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Although land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.

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Section: Discussionmentioning

confidence: 99%

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

et al. 2022

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“…Theoretically, if the grids without human activities can be detected, then it would be possible to upscale the ANPP values in these grids to the entire region to obtain the PNPP (Wang, 2020). We hypothesized that the undisturbed grids are those only affected by climate under natural conditions, and thus the interannual variation should be consistent with climate conditions (C. Li et al, 2021). Because only temperature and precipitation are used in the PNPP estimation by climate models, it is reasonable to use the change in NPP to represent climate change.…”

Section: Methodsmentioning

confidence: 99%

“…interannual variation should be consistent with climate conditions (C. Li et al, 2021). Because only temperature and precipitation are used in the PNPP estimation by climate models, it is reasonable to use the change in NPP to represent climate change.…”

Section: Selection Of Grids Undisturbed By Humansmentioning

confidence: 99%

Human activities rather than climate dominated the grassland net primary production in the three basins of the Hexi Corridor, North‐Western China

Wang¹,

Dou

2023

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Actual net primary production (ANPP) is affected by both natural conditions and human activities in many areas, while the contributions of these factors to NPP remain unknown because it is difficult to isolate their effects. This knowledge gap hinders our implementation of environmental protection and restoration. In this study, we selected three basins of the Hexi Corridor, North‐western China, as the study area. Based on the monthly growth of grassland and field observations, we identified the undisturbed grids and then calculated the potential net primary productivity (PNPP) of these grids. Using the Carnegie‐Ames‐Stanford‐Approach (CASA) model, we calculated the ANPP in these areas. The influence of human activities on NPP (HNPP) was then calculated as the difference between ANPP and PNPP. The results showed that from 2001 to 2017, the mean grassland HNPP was −236.25 gC m−2 a−1. The area with a relative contribution index (RCI) of human activity greater than 0.5 accounted for more than 50% of the total, indicating that human activities were a dominant factor in the NPP change. After the implementation of ecological governance projects in the Hexi Corridor in 2006, HNPP exhibited a negative weakening rate of 2.79 gC m−2 a−1 (p < 0.05), and the RCI decreased from 0.708 at the beginning of the study period to 0.522 at the end (p < 0.01), indicating that reducing human interference with nature was an effective measure for ecological restoration. Based on these findings, we concluded that the changes in NPP in the three basins of the Hexi Corridor was dominated by human activities. Our results suggest that future studies pertaining to the response of NPP to climate change should consider the impact of human activities in semi‐arid regions.

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“…However, these retrievals might have partly mixed the impacts of human activity and climate change when they contributed to ecosystem dynamics in a similar fashion, which led to the uncertainty and unreliability of the assessment benchmark [22,32,33]. The potential status of the climax community was also used to build a benchmark under climate change [38] in the short term. Thus, the measurements based on such benchmarks are limited in practical application significance [28,33,39] and theoretically include both the human impacts during the assessment period and the historically accumulative ones.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change

et al. 2022

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Intense human activities and rapid climate changes both have obvious impacts on alpine ecosystems. However, the magnitudes and directions of the impacts by these two drivers remain uncertain due to a lack of a reasonable assessment method to distinguish between them. The impact of natural resilience is also generally included in the dynamics of a disturbed ecosystem and is liable to be mixed into the impact of human activity. It is urgent that we quantitatively discriminate human activity impacts on the ecosystem under climate change, especially for fast-developing alpine regions. Here, we propose an assessment method to determine human activity impacts under a dynamic climate, taking the potential net primary production (NPP) of an ecosystem as a benchmark. The potential NPP (NPPP) series under the changing climate was retrieved by an improved integrated biosphere simulator based on the initial disturbed ecosystem status of the assessment period. The actual NPP (NPPA) series monitored by remote sensing was considered as the results derived from the joint impacts of climate change, natural resilience and human activity. Then, the impact of human activity was quantified as the difference between the NPPP and NPPA. The contributions of human activity and natural forces to ecosystem NPP dynamics were then calculated separately and employed to explore the dominant driver(s). This assessment method was demonstrated in a typical alpine ecosystem in Northwest China. The results indicate that this method capably revealed the positive impacts of local afforestation and land-use optimization and the negative impacts caused by grazing during the assessment period of 2001–2017. This assessment method provides a quantitative reference for assessing the performances of ecological protections or human damage to alpine ecosystems at the regional scale.

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A Method for Quantifying the Impacts of Human Activities on Net Primary Production of Grasslands in Northwest China

Cited by 13 publications

References 33 publications

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

Human activities rather than climate dominated the grassland net primary production in the three basins of the Hexi Corridor, North‐Western China

A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change

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