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Artykuły w czasopismach na temat „Vegetation change”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

He, Dong, Xianglin Huang, Qingjiu Tian, and Zhichao Zhang. "Changes in Vegetation Growth Dynamics and Relations with Climate in Inner Mongolia under More Strict Multiple Pre-Processing (2000–2018)." Sustainability 12, no. 6 (2020): 2534. http://dx.doi.org/10.3390/su12062534.

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Inner Mongolia Autonomous Region (IMAR) is related to China’s ecological security and the improvement of ecological environment; thus, the vegetation’s response to climate changes in IMAR has become an important part of current global change research. As existing achievements have certain deficiencies in data preprocessing, technical methods and research scales, we correct the incomplete data pre-processing and low verification accuracy; use grey relational analysis (GRA) to study the response of Enhanced Vegetation Index (EVI) in the growing season to climate factors on the pixel scale; explo

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Zhang, Xianliang, and Xuanrui Huang. "Human disturbance caused stronger influences on global vegetation change than climate change." PeerJ 7 (September 25, 2019): e7763. http://dx.doi.org/10.7717/peerj.7763.

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Global vegetation distribution has been influenced by human disturbance and climate change. The past vegetation changes were studied in numerous studies while few studies had addressed the relative contributions of human disturbance and climate change on vegetation change. To separate the influences of human disturbance and climate change on the vegetation changes, we compared the existing vegetation which indicates the vegetation distribution under human influences with the potential vegetation which reflects the vegetation distribution without human influences. The results showed that climat

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3

Khan, Asim, Warda Asim, Anwaar Ulhaq, and Randall W. Robinson. "A deep semantic vegetation health monitoring platform for citizen science imaging data." PLOS ONE 17, no. 7 (2022): e0270625. http://dx.doi.org/10.1371/journal.pone.0270625.

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Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen scien

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4

Wan, Lei, Huiyu Liu, Haibo Gong, and Yujia Ren. "Effects of Climate and Land Use changes on Vegetation Dynamics in the Yangtze River Delta, China Based on Abrupt Change Analysis." Sustainability 12, no. 5 (2020): 1955. http://dx.doi.org/10.3390/su12051955.

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Vegetation dynamics is thought to be affected by climate and land use changes. However, how the effects vary after abrupt vegetation changes remains unclear. Based on the Mann-Kendall trend and abrupt change analysis, we monitored vegetation dynamics and its abrupt change in the Yangtze River delta during 1982–2016. With the correlation analysis, we revealed the relationship of vegetation dynamics with climate changes (temperature and precipitation) pixel-by-pixel and then with land use changes analysis we studied the effects of land use changes (unchanged or changed land use) on their relatio

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5

Han, Hongzhu, Jianjun Bai, Gao Ma, and Jianwu Yan. "Vegetation Phenological Changes in Multiple Landforms and Responses to Climate Change." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 111. http://dx.doi.org/10.3390/ijgi9020111.

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Vegetation phenology is highly sensitive to climate change, and the phenological responses of vegetation to climate factors vary over time and space. Research on the vegetation phenology in different climatic regimes will help clarify the key factors affecting vegetation changes. In this paper, based on a time-series reconstruction of Moderate-Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data using the Savitzky–Golay filtering method, the phenology parameters of vegetation were extracted, and the Spatio-temporal changes from 2001 to 2016 were analy

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Liu, Q., Z. Yang, L. Liang, and W. Nan. "Do changes in climate or vegetation regulate evapotranspiration and streamflow trends in water-limited basins?" Hydrology and Earth System Sciences Discussions 11, no. 10 (2014): 11183–202. http://dx.doi.org/10.5194/hessd-11-11183-2014.

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Abstract. Interactions between climate change, vegetation, and soil regulate hydrological processes. In this study, it was assumed that vegetation type and extent remained fixed and unchanged throughout the study period, while the effective rooting depth (Ze) changed under climate change scenarios. Budyko's hydrological model was used to explore the impact of climate change and vegetation on evapotranspiration (E) and streamflow (Q) on the static vegetation rooting depth and the dynamic vegetation rooting depth. Results showed that both precipitation (P) and potential evapotranspiration (Ep) e

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7

Xu, Xiaojuan, Huiyu Liu, Zhenshan Lin, Fusheng Jiao, and Haibo Gong. "Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China." Remote Sensing 11, no. 13 (2019): 1564. http://dx.doi.org/10.3390/rs11131564.

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Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the ensemble empirical mode decomposition (EEMD) method, the abrupt changes in vegetation and its possible relationships with the driving factors in the karst region of southwest China during 1982–2015 are revealed at multiple timescales. The results showed that: (1) the Normalized Difference Vegetation Index (NDVI

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8

Timalsina, Bhuban, Suzanne Mavoa, and Amy K. Hahs. "Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016." Land 10, no. 8 (2021): 814. http://dx.doi.org/10.3390/land10080814.

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Understanding changes in urban vegetation is essential for ensuring sustainable and healthy cities, mitigating disturbances due to climate change, sustaining urban biodiversity, and supporting human health and wellbeing. This study investigates and describes the distribution and dynamic changes in urban vegetation over a 15-year period in Greater Melbourne, Australia. The study investigates how vegetation cover across Melbourne has changed at five-yearly intervals from 2001 to 2016 using the newly proposed dynamic change approach that extends the net change approach to quantify the amount of v

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9

Schoenbrun, David Lee. "The Contours of Vegetation Change and Human Agency in Eastern Africa's Great Lakes Region: ca. 2000 BC to ca. AD 1000." History in Africa 21 (1994): 269–302. http://dx.doi.org/10.2307/3171889.

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Elsewhere I have set forth a basic outline for charting histories of vegetation change through the use of paleoenvironmental data (Schoenbrun 1991). This essay builds on the previous one by laying out the contours of vegetation change and human agency in the Great Lakes region (Map 1) over the roughly three millennia after ca. 2000 BC.The history of the vegetation in eastern Africa's Great Lakes region brings into focus several important features of long-term environmental change—human action, climatic shift, and internal successional patterns. The primary sources for this history come from a

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10

Dong, Xi, and Chunming Hu. "Remote Sensing Monitoring and Evaluation of Vegetation Changes in Hulun Buir Grassland, Inner Mongolia Autonomous Region, China." Forests 13, no. 12 (2022): 2186. http://dx.doi.org/10.3390/f13122186.

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Constantly increasing vegetation changes pose serious challenges to the sustainable use of global ecosystems. Thus, facing the increasingly serious climate and ecological environment problems and improving vegetation coverage is crucial to the sustainable development of the region. Along these lines, in this work, a monitoring model of vegetation cover change was proposed and developed by using Landsat TM (1989, 1999, and 2011) and Landsat OLI-TIRS (2021) data. More specifically, it was used to assess vegetation change. Based on this model, the vegetation change in the core area of Hulun Buir

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11

Nugraha, Wahyu Ananta, Pramaditya Wicaksono, and Sanjiwana Arjasakusuma. "Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery." Jurnal Geografika (Geografi Lingkungan Lahan Basah) 5, no. 1 (2024): 9. http://dx.doi.org/10.20527/jgp.v5i1.11709.

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Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images

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12

Song, Zhiyuan, Ziyi Gao, Xianming Yang, and Yuejing Ge. "Distinguishing the Impacts of Human Activities and Climate Change on the Livelihood Environment of Pastoralists in the Qinghai Lake Basin." Sustainability 14, no. 14 (2022): 8402. http://dx.doi.org/10.3390/su14148402.

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Grassland vegetation is the largest terrestrial ecosystem in the Qinghai Lake Basin (QLB), and it is also the most important means of production for herders’ livelihoods. Quantifying the impact of climate change and human activities on grassland vegetation changes is an essential task for ensuring the sustainable livelihood of pastoralists. To this end, we investigated vegetation cover changes in the QLB from 2000 to 2020 using the normalized difference vegetation index (NDVI), meteorological raster data, and digital elevation and used residual analysis of multiple linear regression to evaluat

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13

Liu, Yu, Jiyang Tian, Ronghua Liu, and Liuqian Ding. "Influences of Climate Change and Human Activities on NDVI Changes in China." Remote Sensing 13, no. 21 (2021): 4326. http://dx.doi.org/10.3390/rs13214326.

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The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of

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14

Wang, Yiming, Zengxin Zhang, and Xi Chen. "Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China." Remote Sensing 13, no. 24 (2021): 5081. http://dx.doi.org/10.3390/rs13245081.

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Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to a

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15

BAKR, DHER I., JASIM Al-KHALIDI, and BASHAR TALIB HAMID. "Climate changes impact on the distribution of vegetation in Wasit and Nineveh regions of Iraq." Journal of Agrometeorology 26, no. 1 (2024): 87–91. http://dx.doi.org/10.54386/jam.v26i1.2417.

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Climate changes have a direct or indirect impact on many vital systems, including human and animal, as well as vegetation. The monthly precipitation and temperature for the period (1981-2021) and vegetation images (NDVI) for the period (2000-2022) from the satellite (NASA) for the regions of Ninevah and Wasit of Iraq were used to find out their variations over the space and time. It was found that the temperature was increasing with time, but the precipitation was in a state of turbulent increase in the two study areas. The distribution of vegetation was also in a state of change with time as

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16

Pfeiffer, Mirjam, Dushyant Kumar, Carola Martens, and Simon Scheiter. "Climate change will cause non-analog vegetation states in Africa and commit vegetation to long-term change." Biogeosciences 17, no. 22 (2020): 5829–47. http://dx.doi.org/10.5194/bg-17-5829-2020.

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Abstract. Vegetation responses to changes in environmental drivers can be subject to temporal lags. This implies that vegetation is committed to future changes once environmental drivers stabilize; e.g., changes in physiological processes, structural changes, and changes in vegetation composition and disturbance regimes may happen with substantial delay after a change in forcing has occurred. Understanding the trajectories of such committed changes is important as they affect future carbon storage, vegetation structure, and community composition and therefore need consideration in conservation

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17

Li, Z., and T. Zhou. "Responses of vegetation growth to climate change in china." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 225–29. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-225-2015.

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Global warming-related climate changes have significantly impacted the growth of terrestrial vegetation. Quantifying the spatiotemporal characteristic of the vegetation’s response to climate is crucial for assessing the potential impacts of climate change on vegetation. In this study, we employed the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI) that was calculated for various time scales (1 to 12 months) from monthly records of mean temperature and precipitation totals using 511 meteorological stations in China to study the re

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18

Zhang, Yuxin, Yafeng Lu, and Xueqian Song. "Identifying the Main Factors Influencing Significant Global Vegetation Changes." Forests 14, no. 8 (2023): 1607. http://dx.doi.org/10.3390/f14081607.

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Understanding the dynamics of vegetation change is crucial for comprehending ecosystem functioning and its response to anthropogenic activities and climate change. This study investigates significant vegetation changes worldwide and aims to identify the dominant factors responsible for these changes. By analyzing long-term data on vegetation dynamics and climatic factors, this research identifies regions with significant global vegetation changes and determines the main factors leading to such changes at the grid scale. The results reveal important insights into the drivers of vegetation chang

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19

ZHANG, Yajie, Weihang XU, and Yuxin JIANG. "Remote Sensing Monitoring of Vegetation Change in Yungang Area for Ecological Restoration." Acta Interdisciplinary Science 2, no. 1 (2025): 1–9. https://doi.org/10.48014/ais.20241004001.

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Coal mining will change the land nutrient conditions and affect the growth of surface vegetation. In view of the lack of analysis and research on the spatio-temporal changes of vegetation coverage in Yungang District, Shanxi Province, in the hinterland of Datong coalfield, deeply explored the vegetation index information from remote sensing data and conducted statistical analysis of vegetation time series. Based on landsat8 oli images from 2019 to 2022, the normalized difference vegetation index (NDVI) , vegetation coverage and greenness change rate were extracted, and the long-term vegetation

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20

Sun, Na, Naijing Liu, Xiang Zhao, Jiacheng Zhao, Haoyu Wang, and Donghai Wu. "Evaluation of Spatiotemporal Resilience and Resistance of Global Vegetation Responses to Climate Change." Remote Sensing 14, no. 17 (2022): 4332. http://dx.doi.org/10.3390/rs14174332.

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The quantitative assessment of vegetation resilience and resistance is worthwhile to deeply understand the responses of vegetation growth to climate anomalies. However, few studies comprehensively evaluate the spatiotemporal resilience and resistance of global vegetation responses to climate change (i.e., temperature, precipitation, and radiation). Furthermore, although ecosystem models are widely used to simulate global vegetation dynamics, it is still not clear whether ecosystem models can capture observation-based vegetation resilience and resistance. In this study, based on remotely sensed

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Chen, Zhichao, Honghao Feng, Xueqing Liu, Hongtao Wang, and Chengyuan Hao. "Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area." Forests 15, no. 9 (2024): 1573. http://dx.doi.org/10.3390/f15091573.

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The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanc

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Ma, Bo, Shanshan Wang, Christophe Mupenzi, Haoran Li, Jianye Ma, and Zhanbin Li. "Quantitative Contributions of Climate Change and Human Activities to Vegetation Changes in the Upper White Nile River." Remote Sensing 13, no. 18 (2021): 3648. http://dx.doi.org/10.3390/rs13183648.

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Vegetation changes in the Upper White Nile River (UWNR) are of great significance to the maintenance of local livelihoods, the survival of wildlife, and the protection of species habitats. Based on the GIMMS NDVI3g and MODIS normalized difference vegetation index (NDVI) data, the temporal and spatial characteristics of vegetation changes in the UWNR from 1982 to 2020 were analyzed by a Theil-Sen median trend analysis and Mann-Kendall test. The future trend of vegetation was analyzed by the Hurst exponential method. A partial correlation analysis was used to analyze the relationship of the vege

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23

Sun, Huaizhang, Jiyan Wang, Junnan Xiong, et al. "Vegetation Change and Its Response to Climate Change in Yunnan Province, China." Advances in Meteorology 2021 (January 31, 2021): 1–20. http://dx.doi.org/10.1155/2021/8857589.

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The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by t

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Rokni, Komeil, and Tajul Ariffin Musa. "Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery." CATENA 178 (July 2019): 59–63. http://dx.doi.org/10.1016/j.catena.2019.03.007.

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Rasouli, Kabir, John W. Pomeroy, and Paul H. Whitfield. "Are the effects of vegetation and soil changes as important as climate change impacts on hydrological processes?" Hydrology and Earth System Sciences 23, no. 12 (2019): 4933–54. http://dx.doi.org/10.5194/hess-23-4933-2019.

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Abstract. Hydrological processes are widely understood to be sensitive to changes in climate, but the effects of concomitant changes in vegetation and soils have seldom been considered in snow-dominated mountain basins. The response of mountain hydrology to vegetation/soil changes in the present and a future climate was modeled in three snowmelt-dominated mountain basins in the North American Cordillera. The models developed for each basin using the Cold Regions Hydrological Modeling platform employed current and expected changes to vegetation and soil parameters and were driven with recent an

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Lim, Young-Kwon, Ming Cai, Eugenia Kalnay, and Liming Zhou. "Impact of Vegetation Types on Surface Temperature Change." Journal of Applied Meteorology and Climatology 47, no. 2 (2008): 411–24. http://dx.doi.org/10.1175/2007jamc1494.1.

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Abstract The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEP–NCAR I (NNR), and two observation datasets, namely, Climatic Research Unit (CRU) and Global Historical Climate Network (GHCN). The basis of the observation minus reanalysis (OMR) approach is that the NNR reanalysis surface fields, and to a lesser extent the ERA-40, are insensitive to surfac

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Strandberg, G., and E. Kjellström. "Climate Impacts from Afforestation and Deforestation in Europe." Earth Interactions 23, no. 1 (2019): 1–27. http://dx.doi.org/10.1175/ei-d-17-0033.1.

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Abstract Changes in vegetation are known to have an impact on climate via biogeophysical effects such as changes in albedo and heat fluxes. Here, the effects of maximum afforestation and deforestation are studied over Europe. This is done by comparing three regional climate model simulations—one with present-day vegetation, one with maximum afforestation, and one with maximum deforestation. In general, afforestation leads to more evapotranspiration (ET), which leads to decreased near-surface temperature, whereas deforestation leads to less ET, which leads to increased temperature. There are ex

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Li, Yang, Yaochen Qin, Liqun Ma, and Ziwu Pan. "Climate change: vegetation and phenological phase dynamics." International Journal of Climate Change Strategies and Management 12, no. 4 (2020): 495–509. http://dx.doi.org/10.1108/ijccsm-06-2019-0037.

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Purpose The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau. Design/methodology/approach The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend an

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Yan, Dan, Zhizhu Lai, and Guangxing Ji. "Using Budyko-Type Equations for Separating the Impacts of Climate and Vegetation Change on Runoff in the Source Area of the Yellow River." Water 12, no. 12 (2020): 3418. http://dx.doi.org/10.3390/w12123418.

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Assessing the contribution rates of climate change and human activities to the runoff change in the source area of the Yellow River can provide support for water management in the Yellow River Basin. This paper firstly uses a multiple linear regression method to evaluate the contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River. Next, the paper uses the Budyko hypothesis method to calculate the contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetat

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Su, Yanli, Jielin Zhang, Shouzhang Peng, and Yongxia Ding. "Simulating Ecological Functions of Vegetation Using a Dynamic Vegetation Model." Forests 13, no. 9 (2022): 1464. http://dx.doi.org/10.3390/f13091464.

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The ecological functions of vegetation play a significant role in improving human well-being. However, previous studies on ecological functions have only used semi-empirical models, which do not include physiological mechanisms and therefore do not accurately estimate the ecological functions of vegetation under scenarios of future climate change. To address this problem, a process-based dynamic vegetation model (LPJ-GUESS) was used to simulate the ecological functions of vegetation under different climate change scenarios in the Loess Plateau (LP), a typical ecologically fragile area in China

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Pastor, John. "Vegetation Dynamics and Climate Change." Ecology 75, no. 7 (1994): 2145–46. http://dx.doi.org/10.2307/1941620.

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NOGAMI, Michio. "Seasonal Change of Vegetation Index." Journal of Geography (Chigaku Zasshi) 101, no. 6 (1992): Plate4. http://dx.doi.org/10.5026/jgeography.101.6_plate4.

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Chambers, F. M., A. M. Solomon, and H. H. Shugart. "Vegetation Dynamics and Global Change." Journal of Ecology 81, no. 4 (1993): 834. http://dx.doi.org/10.2307/2261689.

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Giesecke, Thomas, Petr Kuneš, and Triin Reitalu. "Millennial to centennial vegetation change." Journal of Vegetation Science 29, no. 3 (2018): 357–59. http://dx.doi.org/10.1111/jvs.12650.

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Milne, J. A. "Grazing intensity and vegetation change." BSAP Occasional Publication 18 (January 1994): 23–29. http://dx.doi.org/10.1017/s0263967x00001476.

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AbstractChange in the semi-natural vegetation of the hills and uplands of the UK is a relatively slow process. Whilst exogenous influences, such as climate and air quality, can influence the rate of change, the principal means whereby more rapid change can occur is through the actions of man in managing such resources to meet a range of objectives. Burning and grazing by large herbivores are the two most important management practices adopted and their interaction is central to the maintenance of vegetation in its current state and to its direction of change. This paper reviews how vegetation

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Woodwell, George M. "Vegetation dynamics and global change." Trends in Ecology & Evolution 8, no. 10 (1993): 381. http://dx.doi.org/10.1016/0169-5347(93)90229-i.

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Cannell, Melvin G. R. "Vegetation dynamics and global change." Forest Ecology and Management 72, no. 1 (1995): 86–87. http://dx.doi.org/10.1016/0378-1127(95)90028-4.

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Ritchie, J. C. "Climate change and vegetation response." Vegetatio 67, no. 2 (1986): 65–74. http://dx.doi.org/10.1007/bf00037358.

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Wiegand, T., and S. J. Milton. "Vegetation change in semiarid communities." Vegetatio 125, no. 2 (1996): 169–83. http://dx.doi.org/10.1007/bf00044649.

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Scheel, D., T.L.S. Vincent, and Guy N. Cameron. "Global Warming and the Species Richness of Bats in Texas." Conservation Biology 10, no. 2 (1996): 452–64. https://doi.org/10.5281/zenodo.14815389.

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(Uploaded by Plazi for the Bat Literature Project) General circulation models provide predictions for global climate under scenarios of increased atmospheric CO2. Climate change is expected to lead directly to changes in distributions of vegetation associations. Distribution of animals will also change to the extent that animals rely on vegetation for food or shelter. Bat species in Texas appear to be restricted, in part, by the availability of roosts. We used geographic information systems and the Holdridge vegetation-climate association scheme to model the effect of climate change on bat dis

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Caddy-Retalic, S., G. M. Wardle, E. J. Leitch, F. A. McInerney, and A. J. Lowe. "Vegetation change along a Mediterranean to arid zone bioclimatic gradient reveals scale-dependent ecotone patterning." Australian Journal of Botany 68, no. 8 (2020): 574. http://dx.doi.org/10.1071/bt20036.

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The drivers and rate of vegetation change across spatial gradients can give critical insights into the compositional and structural change we can expect under climate change. Spatial ecotones are of particular interest as they represent heterogeneity in the patterning of vegetation that may reflect how temporal environmental change will manifest in more abrupt step changes in plant composition and/or structure. Another dimension of interest is the degree to which survey methodology impacts the detectability of thresholds in vegetation. We surveyed a Mediterranean to arid zone gradient in South

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Lai, Wenli, Mingming Wang, Jun Wei, et al. "Separating the Impact of Climate Changes and Human Activities on Vegetation Growth Based on the NDVI in China." Advances in Meteorology 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/6294029.

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Vegetation growth is affected by both climate changes and human activities. In this study, we investigated the vegetation growth response to climate change (precipitation and temperature) and human activities in nine subregions and for nine vegetation types in China from 1982 to 2015. The normalized difference vegetation index (NDVI) and the RESTREND method based on a multiple linear regression model were employed to this end. An overall increasing trend in the NDVI was observed in recent decades, and the fastest increases were identified in southern China (TrendNDVI = +0.0190) and evergreen b

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Subin, Z. M., W. J. Riley, J. Jin, D. S. Christianson, M. S. Torn, and L. M. Kueppers. "Ecosystem Feedbacks to Climate Change in California: Development, Testing, and Analysis Using a Coupled Regional Atmosphere and Land Surface Model (WRF3–CLM3.5)." Earth Interactions 15, no. 15 (2011): 1–38. http://dx.doi.org/10.1175/2010ei331.1.

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Abstract A regional atmosphere model [Weather Research and Forecasting model version 3 (WRF3)] and a land surface model [Community Land Model, version 3.5 (CLM3.5)] were coupled to study the interactions between the atmosphere and possible future California land-cover changes. The impact was evaluated on California’s climate of changes in natural vegetation under climate change and of intentional afforestation. The ability of WRF3 to simulate California’s climate was assessed by comparing simulations by WRF3–CLM3.5 and WRF3–Noah to observations from 1982 to 1991. Using WRF3–CLM3.5, the authors

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Hirayama, Hidetake, Mizuki Tomita, and Keitarou Hara. "PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 883–87. http://dx.doi.org/10.5194/isprs-archives-xli-b8-883-2016.

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The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperatur

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Hirayama, Hidetake, Mizuki Tomita, and Keitarou Hara. "PREDICTION OF CHANGES IN VEGETATION DISTRIBUTION UNDER CLIMATE CHANGE SCENARIOS USING MODIS DATASET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 883–87. http://dx.doi.org/10.5194/isprsarchives-xli-b8-883-2016.

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The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan’s cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperatur

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Zhang, Qingbin, Cuicui Qi, and Hui Wang. "Quantifying the relative impacts of climate and human activities on vegetation change in Hunan Province between 2000 and 2015." E3S Web of Conferences 560 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202456002005.

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Since1999, Hunan Province has embarked on a series of ecological projects to return farmland to forests across the province to explore the impacts of anthropogenic policies and actions on changes in vegetation cover between 2000 and 2015.It is important to understand the impacts of anthropogenic and climatic changes on regional vegetation cover change is an important guide for formulating reasonable environmental protection and restoration strategies in the region .this paper analysed the influence of climate and anthropogenic activities on the change of vegetation cover of Hunan Province duri

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Adepoju, Kayode, Samuel Adelabu, and Olutoyin Fashae. "Vegetation Response to Recent Trends in Climate and Landuse Dynamics in a Typical Humid and Dry Tropical Region under Global Change." Advances in Meteorology 2019 (December 13, 2019): 1–15. http://dx.doi.org/10.1155/2019/4946127.

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The influence of global change on vegetation cover and processes has drawn increasing attention in the past few decades. In this study, we used remotely sensed rainfall and land surface temperature to investigate the spatiotemporal pattern and trend in vegetation condition using NDVI as proxy from 2001 to 2017 in a humid and dry tropical region. We also determined the partial correlation coefficient of temperature and rainfall with NDVI and the response of NDVI to changes in landcover categories due to human activities. We found that the mean annual maximum NDVI was 0.42, decreasing at a rate

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Zhang, Shanghong, Zehao Li, Xiaonan Lin, and Cheng Zhang. "Assessment of Climate Change and Associated Vegetation Cover Change on Watershed-Scale Runoff and Sediment Yield." Water 11, no. 7 (2019): 1373. http://dx.doi.org/10.3390/w11071373.

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Climate change has an important impact on water balance and material circulation in watersheds. Quantifying the influence of climate and climate-driven vegetation cover changes on watershed-scale runoff and sediment yield will help to deepen our understanding of the environmental effects of climate change. Taking the Zhenjiangguan Watershed in Sichuan Province, China as a case study, three downscaled general circulation models with two emission scenarios were used to generate possible climatic conditions for three future periods of P1 (2020–2039), P2 (2050–2069) and P3 (2080–2099). Differences

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Nordbakken, Jørn‐Frode. "Fine‐scale five‐year vegetation change in boreal bog vegetation." Journal of Vegetation Science 12, no. 6 (2001): 771–78. http://dx.doi.org/10.2307/3236864.

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Chytrý, Milan, Lubomír Tichý, Stephan M. Hennekens, and Joop H. J. Schaminée. "Assessing vegetation change using vegetation-plot databases: a risky business." Applied Vegetation Science 17, no. 1 (2013): 32–41. http://dx.doi.org/10.1111/avsc.12050.

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