Assessing the Impacts of Urbanization and Climate Change on NPP Under Different Habitat Quality Conditions over the Last Two Decades in the Tibetan Plateau, China DOI Creative Commons

Tian Xia,

Liusheng Han, Yunmin Chen

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2139 - 2139

Published: Dec. 9, 2024

The processes of urbanization and climate change have exerted a marked influence on net primary productivity (NPP). However, the underlying mechanisms that drive these effects remain intricate insufficiently understood. both an adverse effect habitat quality (HQ) biodiversity loss. HQ has direct health stability ecosystems, which regulate level NPP. A higher is associated with stronger Now, quantification assessment impacts NPP are still challenging because various driving factors influencing production terrestrial vegetation. Therefore, new perspective was adopted to study in Qinghai–Tibet Plateau China during 2000–2020. spatiotemporal analysis method employed investigate impact night light index different regions (the divided into five levels, each area type corresponding specific level). Then, coupled coordination model (CCD) used analyze coupling relationship between HQ. Finally, relative contribution studied using scenario simulation. results showed (1) whole Tibetan increased very little, average growth rate 0.42 g C m⁻2 per year. (2) It surprising find urban areas did not decline significantly as result urbanization. there notable areas. (3) mean found be 17%, while other 69% 14%, respectively. These findings provide valuable insights interactions human development environmental factors, enhancing our comprehension their role Plateau’s carbon cycle.

Language: Английский

Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain DOI Creative Commons

Jinhao Shi,

Peng Zhang, Yang Liu

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112812 - 112812

Published: Nov. 9, 2024

Language: Английский

Citations

10
The Impact of Climate Change and Human Activities on the Spatial and Temporal Variations of Vegetation NPP in the Hilly-Plain Region of Shandong Province, China DOI Open Access
Yangyang Wu, Jinli Yang, Si‐Liang Li

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(6), P. 898 - 898

Published: May 22, 2024

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green low-carbon development, as well carbon peaking neutrality goals. This article employs various analytical approaches, including Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient variation, Hurst index, land-use land-cover change (LUCC) transition matrix, to conduct a thorough study NPP variations in Shandong Hilly Plain (SDHP) region. Furthermore, geographic detector method was used investigate synergistic effects meteorological human activities on this Between 2000 2020, vegetation SDHP exhibited an average increase rate 0.537 g C·m−2·a−1. However, fluctuation mean annual NPP, ranging from 203 230 C·m−2·a−1, underscores uneven growth pattern. Significant regional disparities are evident gradually ascending southeast northwest coastal areas inland regions. The index entire area stands at 0.556, indicating overall sustained trend time series NPP. can be explained by climate variables (mean temperature, precipitation) (LUCC, night light index); these, LUCC (q = 0.684) has highest explanatory power impact major influencing factor. deepens understanding factors patterns dynamic response At same time, it provides valuable scientific insights improving ecosystem quality promoting

Language: Английский

Citations

3
Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin DOI Creative Commons
Bo Cao, Yan Wang, Xiaolong Zhang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 587 - 587

Published: Feb. 4, 2024

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of Haihe River basin (MHRB) is a crucial location for application engineering. Vegetation an important component in conservation eco-hydrological progress MHRB. A better understanding regional vegetation growth can be achieved by thorough investigation indicators. this research, leaf index (LAI) gross primary productivity (GPP) were chosen The characteristics driving forces spatiotemporal variations LAI GPP MHRB explored through Sen’s slope, Mann–Kendall test, optimal parameter-based geographical detector model, correlation analysis. From 2001 2018, annual increased significantly on scale. areas with accounted more than 81% Land use was most influential element spatial heterogeneity GPP, humidity one among climate Non-linear enhancement or bivariate discovered between any two factors, strongest interaction from land index. lowest cover found dry regions precipitation below 407 mm under 0.41; while both forests large undulating mountains, higher observed. About 87% unaltered use. increase 2018 promoted reduced vapor pressure deficit. sensitivity change stronger that LAI. These findings serve theoretical guide engineering preservation

Language: Английский

Citations

2
Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging DOI Creative Commons

Jihan Wang,

Nan Zhang,

Laifu Zhang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(14), P. 2542 - 2542

Published: July 10, 2024

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing methods have difficulties simultaneously constructing expressing non-stationarity, autocorrelation, complex non-linearity during the process, which limits performance of models. Moreover, there lack research on nighttime land (NLST) reconstruction based downscaling, does not meet data needs for urban-scale urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) propose (GNNWRK) model NLST downscaling. To verify model’s generality robustness, selected four areas different landform climate type experiments. The GNNWRK was compared benchmark methods, including TsHARP, Random Forest, Regression, GNNWR. results show that these higher accuracy maximum Pearson’s Correlation Coefficient (Pcc) 0.930 minimum Root Mean Square Error (RMSE) 0.886 K. validation MODIS ground-measured also indicates can obtain more accurate, richer detailed texture. This enhances potential studying effects islands at finer scale.

Language: Английский

Citations

2
Disentangling the Spatiotemporal Dynamics, Drivers, and Recovery of NPP in Co-Seismic Landslides: A Case Study of the 2017 Jiuzhaigou Earthquake, China DOI Open Access

Yuying Duan,

Xiangjun Pei,

Jing Luo

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(8), P. 1381 - 1381

Published: Aug. 7, 2024

The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered series devastating geohazards, including landslides, collapses, and mudslides within the World Natural Heritage Site. These destructive events obliterated extensive tracts vegetation, severely compromising carbon storage in terrestrial ecosystems. Net Primary Productivity (NPP) reflects capacity vegetation to absorb dioxide. Accurately assessing changes NPP is crucial for unveiling recovery ecosystem after earthquake. To this end, we designed study using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. findings are as follows. co-seismic landslide areas remained stable between 525 575 g C/m2 before earthquake decreased 533 This decline continued, reaching 483 due extreme rainfall 2018, 2019, 2020. Recovery commenced 2021, by 2022, had rebounded 544 C/m2. rate revealed that, five years only 18.88% exhibited an exceeding pre-earthquake state. However, 17.14% these less than 10%, indicating that has barely begun most areas. factor detector temperature, precipitation, elevation significantly influenced recovery. Meanwhile, interaction highlighted lithology, slope, aspect also played roles when interacting with other factors. Therefore, not determined single factor, but rather interactions among various resilience demonstrated current primarily stems from restoration grassland Overall, while potential optimistic, it will require considerable amount time return

Language: Английский

Citations

2
Impact of climate and human activity on NDVI of various vegetation types in the Three-River Source Region, China DOI

Qing Lu,

Haili Kang,

Fuqing Zhang

et al.

Journal of Arid Land, Journal Year: 2024, Volume and Issue: 16(8), P. 1080 - 1097

Published: Aug. 1, 2024

Language: Английский

Citations

2
Spatiotemporal pattern of post-earthquake vegetation recovery in a mountainous catchment in southwestern China DOI
Jiaorong Lv,

Xiubin He,

Yuhai Bao

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

Citations

2
Responses of vegetation dynamics to complex environmental changes in the Runoff Producing area of the World’s Sixth Longest River: Evolution, Identification, and prediction DOI
Qingsong Wu,

Xing Yuan

Journal for Nature Conservation, Journal Year: 2024, Volume and Issue: unknown, P. 126776 - 126776

Published: Dec. 1, 2024

Language: Английский

Citations

0
Assessing the Impacts of Urbanization and Climate Change on NPP Under Different Habitat Quality Conditions over the Last Two Decades in the Tibetan Plateau, China DOI Creative Commons

Tian Xia,

Liusheng Han, Yunmin Chen

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2139 - 2139

Published: Dec. 9, 2024

The processes of urbanization and climate change have exerted a marked influence on net primary productivity (NPP). However, the underlying mechanisms that drive these effects remain intricate insufficiently understood. both an adverse effect habitat quality (HQ) biodiversity loss. HQ has direct health stability ecosystems, which regulate level NPP. A higher is associated with stronger Now, quantification assessment impacts NPP are still challenging because various driving factors influencing production terrestrial vegetation. Therefore, new perspective was adopted to study in Qinghai–Tibet Plateau China during 2000–2020. spatiotemporal analysis method employed investigate impact night light index different regions (the divided into five levels, each area type corresponding specific level). Then, coupled coordination model (CCD) used analyze coupling relationship between HQ. Finally, relative contribution studied using scenario simulation. results showed (1) whole Tibetan increased very little, average growth rate 0.42 g C m⁻2 per year. (2) It surprising find urban areas did not decline significantly as result urbanization. there notable areas. (3) mean found be 17%, while other 69% 14%, respectively. These findings provide valuable insights interactions human development environmental factors, enhancing our comprehension their role Plateau’s carbon cycle.

Language: Английский

Citations

0