The impact of land use landscape pattern on river hydrochemistry at multi-scale in an inland river basin, China

Zhou, Junju; Luo, Chuyu; Ma, Dongfeng; Shi, Wei; Wang, Lanying; Guo, Zhaonan; Tang, Haitao; Wang, Xue; Wang, Jiarui; Wei, Wei; Wang, Chunli

doi:10.1016/j.ecolind.2022.109334

Cited by 16 publications

(5 citation statements)

References 31 publications

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“…While grasslands are typically expected to improve water quality by acting as a detention medium, several studies have demonstrated that grasslands may not consistently offer protection from diffuse pollution (e.g., Ahearn et al, 2005;Amiri & Nakane, 2006;Xiao & Ji, 2007;Nash et al, 2009;Ding et al, 2013;Shen et al, 2014;Chen et al, 2016;Nava-López et al, 2016;Vrebos et al, 2017;Asare et al, 2018;Morse et al, 2018;Lacher et al, 2019;Chen et al, 2021;Dymek et al, 2021;Zhou et al, 2022). Moreover, according to Kleynhans et al (2005), grasslands are not typical of indigenous flora in the study area under undisturbed conditions.…”

Section: Classifying Natural Vegetationmentioning

confidence: 88%

Estimating Thresholds of Natural Vegetation for the Protection of Water Quality in South African Catchments

Locke

2024

Preprint

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Many of South Africa’s water quality problems are directly attributable to the negative impacts of anthropogenic land use transformations, including urban and agricultural expansion. To mitigate these impacts, the preservation of an adequate amount of natural vegetation within catchment areas is an important management strategy. However, it is unclear how much natural vegetation cover is required to provide adequate levels of protection, nor at which scale(s) this strategy would be most effective. Accordingly, regression analysis was used to model relationships between water quality (measured using Nemerow’s Pollution Index) and metrics of natural vegetation at multiple scales across a sample of sub-catchments located within South Africa’s Eastern and Western Cape provinces. With conspicuous outliers removed, the models were able to explain between 69 and 82% of variation in the sample. Moreover, a statistically significant, nonlinear, and inverse relationship was found between proportions of natural vegetation cover and pollution levels. This relationship was strongest when measured (1) across the whole catchment and (2) within a 200 m riparian buffer zone. The models further indicated that approximately 80 to 90% natural vegetation cover was necessary at these scales to maintain water quality at ecologically acceptable levels. Additional nonlinear thresholds estimated using breakpoint analysis suggested that if proportions of natural vegetation fall below 45% (across the whole catchment) and 60% (within a 200 m riparian buffer zone) a dramatic increase in pollution levels can be expected.

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Section: Classifying Natural Vegetationmentioning

confidence: 88%

Estimating Thresholds of Natural Vegetation for the Protection of Water Quality in South African Catchments

Locke

2024

Preprint

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“…According to previous studies, changes in landscape patterns affect various ecological processes and functions, ultimately leading to changes in ecosystem services [58]. Based on existing studies and combined with the actual situation of the study area [8,9,58,59], seven landscape pattern metrics were selected for analysis, i.e., the proportion of cropland (cropland%), proportion of forest (forest%), proportion of construction land (construction land%), patch density (PD), cohesion (COHE), landscape shape index (LSI), and Shannon's diversity index (SHDI), respectively. Among them, cropland%, forest%, and construction land% were landscape composition indicators, which represented the dominant type of regional land use.…”

Section: Selection and Calculation Of Landscape Pattern Metricsmentioning

confidence: 99%

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Ding

Yan

Zhu

et al. 2023

Land

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Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.

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“…Largest patch index (LPI) LPI = Max(a 1 a 2 ......an ) A × 100 LPI [78] is used to reflect the impact of the patch with the largest area on the landscape pattern in the study area.…”

Section: Landscape Patternmentioning

confidence: 99%

“…LSI [78] reflects the complexity of patch shape and the possible evolution trend of shape characteristics of landscape spatial structure. The larger the value, the more complex and irregular the patch shape.…”

Section: Landscape Shape Index (Lsi)mentioning

confidence: 99%

Multi-Factor Collaborative Analysis of Conservation Effectiveness of Nature Reserves Based on Remote Sensing Data and Google Earth Engine

Zhang,

Ju,

Cai

et al. 2023

Remote Sensing

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Protected areas (PAs) play a crucial role in safeguarding biological resources and preserving ecosystems. However, the lack of standardized and highly operational criteria for evaluating their conservation effectiveness, particularly across different ecological types, remains a significant gap in the literature. This study aims to address this gap by constructing a conservation effectiveness evaluation model for two distinct types of PAs in Heilongjiang Province, China: the Zhalong National Nature Reserve (ZlNNR), a wetland ecological reserve; and the Mudanfeng National Nature Reserve (MdfNNR), a forest ecological reserve. We employed various methods, including land use dynamic index, visual analysis of landscape patterns, remote sensing inversion, and a multi-factor comprehensive assessment model, to assess changes in conservation effectiveness from 2000 to 2020. Our findings reveal a contrast between the two PAs. In the ZlNNR, croplands and water bodies increased significantly by 4069.4 ha (K = 1.5820%) and 2541.58 ha (K = 3.2692%). In the MdfNNR, impervious lands increased greatly by 65.35 ha (K = 7.4021%), whereas forest lands decreased by 125 ha (K = −0.067%). The core area of the two PAs displayed increased landscape regularity, whereas the experimental area showed heightened landscape diversity. In ZlNNR, the MPSL value increased by 134.91%, whereas the PDL value decreased by 57.43%, indicating a more regular landscape pattern. In MdfNNR, the SHDIL value decreased by 110.7%, whereas the PDL value increased by 52.55%, indicating a more fragmented landscape pattern. The area with improved vegetation trends in ZlNNR was 8.59% larger than in MdfNNR, whereas the area with degraded vegetation trends was 4.86% smaller than in MdfNNR. In all years, the high effectiveness area was larger in ZlNNR than in MdfNNR, whereas the medium and low effectiveness areas were smaller in ZlNNR compared to MdfNNR. This study provides a scientifically rigorous assessment method for evaluating the conservation effectiveness of different types of PAs, laying a solid theoretical foundation and practical guidance for future conservation strategies.

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The impact of land use landscape pattern on river hydrochemistry at multi-scale in an inland river basin, China

Cited by 16 publications

References 31 publications

Estimating Thresholds of Natural Vegetation for the Protection of Water Quality in South African Catchments

Estimating Thresholds of Natural Vegetation for the Protection of Water Quality in South African Catchments

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Multi-Factor Collaborative Analysis of Conservation Effectiveness of Nature Reserves Based on Remote Sensing Data and Google Earth Engine

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