Grassland Conservation Effectiveness of National Nature Reserves in Northern China

Zhao, Siqing; Zhao, Xiang; Zhao, Jiacheng; Liu, Naijing; Sun, Mengmeng; Mu, Baohui; Sun, Na; Guo, Yinkun

doi:10.3390/rs14071760

Cited by 6 publications

(7 citation statements)

References 84 publications

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“…The coefficient of variation (CV) characterizes the degree of dispersion of the time series and measures the degree of fluctuation of the NDVI over time [47]. The CV is calculated at the pixel scale as follows:…”

Section: Trend Analysismentioning

confidence: 99%

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

Li,

Wang,

Sun

et al. 2024

Remote Sensing

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The normalized difference vegetation index (NDVI) can depict the status of vegetation growth and coverage in grasslands, whereas coarse spatial resolution, cloud cover, and vegetation phenology limit its applicability in fine-scale research, especially in areas covering various vegetation or in fragmented landscapes. In this study, a methodology was developed for obtaining the 30 m annual maximum NDVI to overcome these shortcomings. First, the Landsat NDVI was simulated by fusing Landsat and MODIS NDVI by using the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and then a single-peaked symmetric logistic model was employed to fit the Landsat NDVI data and derive the maximum NDVI in a year. The annual maximum NDVI was then used as a season-independent substitute to monitor grassland variation from 2001 to 2022 in a typical area covering the major vegetation types in the Qinghai Lake Basin. The major conclusions are as follows: (1) Our method for reconstructing the NDVI time series yielded higher accuracy than the existing dataset. The root mean square error (RMSE) for 91.8% of the pixels was less than 0.1. (2) The annual maximum NDVI from 2001 to 2022 exhibited spatial distribution characteristics, with higher values in the northern and southern regions and lower values in the central area. In addition, the earlier vegetation growth maximum dates were related to the vegetation type and accompanied by higher NDVI maxima in the study area. (3) The overall interannual variation showed a slight increasing trend from 2001 to 2022, and the degraded area was characterized as patches and was dominated by Alpine kobresia spp., Forb Meadow, whose change resulted from a combination of permafrost degradation, overgrazing, and rodent infestation and should be given more attention in the Qinghai Lake Basin.

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Section: Trend Analysismentioning

confidence: 99%

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

Li,

Wang,

Sun

et al. 2024

Remote Sensing

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“…NDVI data of the Moderate Resolution Imaging Spectrometer (MODIS) MOD13Q1 product, collected from Google Earth Engine (GEE) cloud platform, were analysed in this investigation. MOD13Q1 dataset are considered, since it has already been widely and successfully used in previous vegetation trends investigation [31] [32]. Time series of NDVI 16-day composites with a spatial resolution of 250 m were gathered for the 2000-2022 period.…”

Section: Remotely Sensed Vegetation Datamentioning

confidence: 99%

NDVI-Derived Vegetation Trends and Driving Factors in West African Sudanian Savanna

Zoungrana,

Dimobé

2023

AJPS

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The Sudanian savanna is a key vegetation biome in West Africa providing food and vital ecosystem services. Recently, it has been reported alarming vegetation loss in this biome, calling for more investigation, relevant to tackle land degradation and ensure food security. However, vegetation dynamics in this area remains a matter of debate, and one of the main challenges is to document consistently the underlying driving factors. This study aimed at assessing vegetation trends and driving factors from 2000 to 2022. NDVI trend, detected using the Mann-Kendall's monotonic trend test, was used as proxy to express vegetation dynamics. In addition to the non-parametric Spearman correlation analysis, variables importance scores, derived from Random Forest (RF) classifications, were used to determine key driving factors among climatic, topographic, edaphic, accessibility and demographic factors. During 2000-2022, no significant trends largely characterised the vegetation cover of the study area. However, patterns of strong (weak) browning and strong (weak) greening affected 7.1% (10.6%) and 12.8% (19.1%) of the study area respectively. According to the driving factors analysis, the observed vegetation trends were mainly driven by rainfall dynamics (trend and mean annual), population growth and anthropogenic activities. The results of this study can support the development of efficient strategies for safeguarding vegetation cover in the Sudanian savanna of Burkina Faso.

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“…These represent the innovations in our research ideology and analytical methodology. Thus far, many appraisals of conservation effectiveness have focused solely on either a single category of PA [21,29] or involved a plethora of disparate PA categories [19,[30][31][32][33][34]. Therefore, our study aims to analyze differences in the effects of various PA types through a comparative analysis of two major types (forest and wetland PAs).…”

Section: Introductionmentioning

confidence: 99%

“…Landscape indexes have been extensively employed to assess the effectiveness of PAs [40][41][42]. The spatial and temporal variation of the Normalized Difference Vegetation Index (NDVI) is one of the most cost-effective methods for assessing ecological effectiveness [34,[43][44][45]. The Google Earth Engine (GEE) Cloud Platform can swiftly and accurately process remote sensing images using its robust data calculation function.…”

Section: Introductionmentioning

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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Grassland Conservation Effectiveness of National Nature Reserves in Northern China

Cited by 6 publications

References 84 publications

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

Monitoring Grassland Variation in a Typical Area of the Qinghai Lake Basin Using 30 m Annual Maximum NDVI Data

NDVI-Derived Vegetation Trends and Driving Factors in West African Sudanian Savanna

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

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