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Journal articles on the topic 'Vegetation change'
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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 (March 24, 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; explore the factors that affect the response speed and response degree from multiple perspectives, including vegetation type, longitude, latitude, elevation and local climate type; and solve the problems of excessive ignorance of details and severe distortion of response results due to using average values of the wide area or statistical data. The results show the following. 1. The vegetation status of IMAR in 2000-2018 was mainly improved. The change rates were 0.23/10° N and 0.25/10° E, respectively. 2. The response speed and response degree of forests to climatic factors are higher than that of grasslands. 3. The lag time of response for vegetation growth to precipitation, air temperature and relative humidity in IMAR is mainly within 2 months. The speed of vegetation‘s response to climate change in IMAR is mainly affected by four major factors: vegetation type, altitude gradient, local climate type and latitude. 4. Vegetation types and altitude gradients are the two most important factors affecting the degree of vegetation’s response to climate factors. It is worth noting that when the altitude rises to 2500 m, the dominant factor for the vegetation growth changes from precipitation to air temperature in terms of hydrothermal combination in the environment. Vegetation growth in areas with relatively high altitudes is more dependent on air temperature.
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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 climate-induced vegetation changes only occurred in a few grid cells from the period 1982–1996 to the period 1997–2013. Human-induced vegetation changes occurred worldwide, except in the polar and desert regions. About 3% of total vegetation distribution was transformed by human activities from the period 1982–1996 to the period 1997–2013. Human disturbances caused stronger damage to global vegetation change than climate change. Our results indicated that the regions where vegetation experienced both human disturbance and climate change are eco-fragile regions.
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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 (July 27, 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 science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices.
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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 (March 4, 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 relationship. Results showed that: (1) the Normalized Vegetation Index (NDVI) during growing season that is represented as GSN (growing season NDVI) showed an overall increasing trend and had an abrupt change in 2000. After then, the area percentages with decreasing GSN trend increased in cropland and built-up land, mainly located in the eastern, while those with increasing GSN trend increased in woodland and grassland, mainly located in the southern. Changed land use, except the land conversions from/to built-up land, is more favor for vegetation greening than unchanged land use (2) after abrupt change, the significant positive correlation between precipitation and GSN increased in all unchanged land use types, especially for woodland and grassland (natural land use) and changed land use except built-up land conversion. Meanwhile, the insignificant positive correlation between temperature and GSN increased in woodland, while decreased in the cropland and built-up land in the northwest (3) after abrupt change, precipitation became more important and favor, especially for natural land use. However, temperature became less important and favor for all land use types, especially for built-up land. This research indicates that abrupt change analysis will help to effectively monitor vegetation trend and to accurately assess the relationship of vegetation dynamics with climate and land use changes.
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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 (February 19, 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 analyzed. Moreover, the response characteristics of the vegetation phenology to climate changes, such as changes in temperature, precipitation, and sunshine hours, were discussed. The results showed that the responses of vegetation phenology to climatic factors varied within different climatic regimes and that the Spatio-temporal responses were primarily controlled by the local climatic and topographic conditions. The following were the three key findings. (1) The start of the growing season (SOS) has a regular variation with the latitude, and that in the north is later than that in the south. (2) In arid areas in the north, the SOS is mainly affected by the temperature, and the end of the growing season (EOS) is affected by precipitation, while in humid areas in the south, the SOS is mainly affected by precipitation, and the EOS is affected by the temperature. (3) Human activities play an important role in vegetation phenology changes. These findings would help predict and evaluate the stability of different ecosystems.
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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 (July 2, 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) showed an overall increasing trend and had an abrupt change in 2001. After the abrupt change, the greening trend of the NDVI in the east and the browning trend in the west, both changed from insignificant to significant. (2) After the abrupt change, at the 2.5-year time scale, the correlation between the NDVI and temperature changed from insignificantly negative to significantly negative in the west. Over the long-term trend, it changed from significantly negative to significantly positive in the east, but changed from significantly positive to significantly negative in the west. The abrupt change primarily occurred on the long-term trend. (3) After the abrupt change, 1143.32 km2 farmland was converted to forests in the east, and the forest area had significantly increased. (4) At the 2.5-year time scale, the abrupt change in the relationships between the NDVI and climate factors was primarily driven by climate change in the west, especially rising temperatures. Over the long-term trend, it was caused by ecological protection projects in the east, but by rising temperatures in the west. The integration of the abrupt change analysis and multiple timescale analysis help assess the relationship of vegetation changes with climate changes and human activities accurately and comprehensively, and deepen our understanding of the driving mechanism of vegetation changes, which will further provide scientific references for the protection of fragile ecosystems in the karst region.
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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 (October 9, 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) exhibited negative trends, which resulted in decreasing trends for dynamic Ze scenarios. Combined with climatic change, decreasing trends in Ze altered the partitioning of P into E and Q. For dynamic scenarios, total E and Q were predicted to be −1.73 and 28.22%, respectively, greater than static scenarios. Although climate change regulated changes in E and Q, the response of Ze to climate change had a greater overall contribution to changes in hydrological processes. Results from this study suggest that with the exception of vegetation type and extent, Ze scenarios were able to alter water balances, which in itself should help to regulate climate change impacts on water resources.
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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 (October 27, 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 climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.
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Timalsina, Bhuban, Suzanne Mavoa, and Amy K. Hahs. "Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016." Land 10, no. 8 (August 2, 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 vegetation gain as well as loss. We examine this question at two spatial resolutions: (1) at the municipal landscape scale to capture broadscale change regardless of land tenure; and (2) at the scale of designated public open spaces within the municipalities to investigate the extent to which the loss of vegetation has occurred on lands that are intended to provide public access to vegetated areas in the city. Vegetation was quantified at four different times (2001, 2006, 2011, 2016), using the normalized difference vegetation index (NDVI). Dynamic changes of gain and loss in urban vegetation between the three periods were quantified for six local government areas (LGAs) and their associated public open spaces using a change matrix. The results showed an overall net loss of 64.5 square kilometres of urban vegetation from 2001 to 2016 in six LGAs. When extrapolated to the Greater Melbourne Area, this is approximately equivalent to 109 times the size of Central Park in New York City.
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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 variety of published palynological and limnological studies from Burundi, Rwanda, Uganda, and Zaire. Perhaps the most rewarding data for reconstructing climatic and vegetational change come from palynological studies. Pollen studies often reflect detailed changes in the constitution of plant communities, and their value for reconstructing the vegetational and climatic contexts for Holocene human history has provoked the development of a rigorous method for their analysis. Contemporary studies of plant community succession and human-vegetation relationships are a secondary source for the history of land clearance in the Great Lakes region. These works provide a means to determine the different imprints of human and climatic action on the paleoenvironmental record.In this study I combine the full range of paleoenvironmental evidence to reconstruct the form and pace of vegetation change. I focus on a part of eastern Africa famous for its great ecological diversity. One of the rewards of this endeavor is to demonstrate to paleoecologists, archaeologists, and historians alike the value of a truly interdisciplinary approach to environmental change.
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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 (November 27, 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 management. In this study, we investigate whether transient vegetation states can be represented by a time-shifted trajectory of equilibrium vegetation states or whether they are vegetation states without analog in conceivable equilibrium states. We use a dynamic vegetation model, the aDGVM (adaptive Dynamic Global Vegetation Model), to assess deviations between simulated transient and equilibrium vegetation states in Africa between 1970 and 2099 for the RCP4.5 and 8.5 scenarios using regionally downscaled climatology based on the MPI-ESM output for CMIP5. We determined lag times and dissimilarity between simulated equilibrium and transient vegetation states based on the combined difference of nine selected state variables using Euclidean distance as a measure for that difference. We found that transient vegetation states over time increasingly deviated from equilibrium states in both RCP scenarios but that the deviation was more pronounced in RCP8.5 during the second half of the 21st century. Trajectories of transient vegetation change did not follow a “virtual trajectory” of equilibrium states but represented non-analog composite states resulting from multiple lags with respect to vegetation processes and composition. Lag times between transient and most similar equilibrium vegetation states increased over time and were most pronounced in savanna and woodland areas, where disequilibrium in savanna tree cover frequently acted as the main driver of dissimilarities. Fire additionally enhanced lag times and dissimilarity between transient and equilibrium vegetation states due to its restraining effect on vegetation succession. Long lag times can be indicative of high rates of change in environmental drivers, of meta-stability and non-analog vegetation states, and of augmented risk for future tipping points. For long-term planning, conservation managers should therefore strongly focus on areas where such long lag times and high residual dissimilarity between most similar transient and equilibrium vegetation states have been simulated. Particularly in such areas, conservation efforts need to consider that observed vegetation may continue to change substantially after stabilization of external environmental drivers.
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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 (December 19, 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 Grassland was systematically analyzed., From the acquired results, the existence of spatial differences in the vegetation coverage changes in the study area were demonstrated. The total area of vegetation coverage changes was 758.95 km2, and the area from low vegetation coverage to high vegetation coverage was 456.41 km2, accounting for 60.14% of the total change area. The area from high vegetation coverage to low vegetation coverage was 302.57 km2, accounting for 39.86% of the total change area, whereas the area of the area without vegetation coverage was 1963.92 km2, accounting for 72.13% of the study area, and the overall vegetation coverage is improving. Vegetation cover change monitoring models can also be used to reveal and describe large-scale vegetation landscape changes and obtain clear vegetation change results through easy-to-obtain data; our work suggests that in the process of pursuing regional economic development and accelerating urbanization, industrialization, and agricultural modernization, human beings should assume more responsibilities and pursue the sustainable development of the natural environment. The results of this work are of great importance to further study the potential driving mechanism of the vegetation coverage changes and provide theoretical guidance for relevant managers to formulate vegetation restoration measures.
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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 (July 8, 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 evaluate the residuals of human activities. The residual analysis of partial derivatives was used to quantify the contribution of climate change and human activities to changes in vegetation cover. The results showed that: (1) The vegetation coverage of the QLB increased significantly (0.002/a, p < 0.01), with 91.38% of the area showing a greening trend, and 8.62% of the area suffering a degrading trend. The NDVI decreased substantially along the altitude gradient (−0.02/a, p < 0.01), with the highest vegetation coverage at 3600–3700 m (0.37/a). The vegetation degraded from 3200–3300 m, vegetation greening accelerated from 3300–3500 m, and vegetation greening slowed above 3500 m. (2) The contribution of climate change, temperature (T), and precipitation (P) to vegetation cover change were 1.62/a, 0.005/a, and 1.615/a, respectively. Below 3500 m, the vegetation greening was more limited by P. Above 3500 m, the vegetation greening was mainly limited by T. (3) Residual analysis showed that the contribution of human activities to vegetation cover was −1.618/a. Regarding the altitude gradient, at 3300–3500 m, human activities had the highest negative contribution to vegetation coverage (−2.389/a), and at 3200–3300 m, they had the highest positive contribution (0.389/a). In the past 21 years, the impact of human activities on vegetation coverage changed from negative to positive. Before 2009, the annual average NDVIres value was negative; after 2010, the average yearly NDVIres value turned positive. In general, the vegetation greening of the QLB depends on climate warming and humidification. The positive impact of human activities over the past decade was also essential for vegetation greening. These findings deepen our understanding of the QLB vegetation changes under climate change and human activities.
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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 response of vegetation types to droughts. We separated the NDVI into 12 time series (one per month) and also used the SPEI of 12 droughts time scales to make the correlation. The results showed that the differences exist in various vegetation types. For needle-leaved forest, broadleaf forest and shrubland, they responded to droughts at long time scales (9 to 12 months). For grassland, meadow and cultivated vegetation, they responded to droughts at short time scales (1 to 5months). The positive correlations were mostly found in arid and sub-arid environments where soil water was a primary constraining factor for plant growth, and the negative correlations always existed in humid environments where temperature and radiation played significant roles in vegetation growth. Further spatial analysis indicated that the positive correlations were primarily found in northern China, especially in northwestern China, which is a region that always has water deficit, and the negative correlations were found in southern China, especially in southeastern China, that is a region has water surplus most of the year. The disclosed patterns of spatiotemporal responses to droughts are important for studying the impact of climate change to vegetation growth.
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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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Pastor, John. "Vegetation Dynamics and Climate Change." Ecology 75, no. 7 (October 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 (December 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 (May 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 change associated with grazing occurs, how it can be measured, what the critical levels of grazing are for the most abundant species and what the implications of grazing are for nutrient supply for animal production systems.
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Wiegand, T., and S. J. Milton. "Vegetation change in semiarid communities." Vegetatio 125, no. 2 (August 1996): 169–83. http://dx.doi.org/10.1007/bf00044649.
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Woodwell, George M. "Vegetation dynamics and global change." Trends in Ecology & Evolution 8, no. 10 (October 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 (March 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 (October 1986): 65–74. http://dx.doi.org/10.1007/bf00037358.
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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 (December 14, 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 analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions.
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Sun, Huaizhang, Jiyan Wang, Junnan Xiong, Jinhu Bian, Huaan Jin, Weiming Cheng, and Ainong Li. "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 the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.
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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 (September 1, 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 and model-simulated leaf area index (LAI) time series and climate datasets, we quantified spatial patterns and temporal changes in vegetation resilience and resistance from 1982–2015. The results reveal clear spatial patterns of observation-based vegetation resilience and resistance for the last three decades, which were closely related to the local environment. In general, most of the ecosystem models capture spatial patterns of vegetation resistance to climate to different extents at the grid scale (R = 0.43 ± 0.10 for temperature, R = 0.28 ± 0.12 for precipitation, and R = 0.22 ± 0.08 for radiation); however, they are unable to capture patterns of vegetation resilience (R = 0.05 ± 0.17). Furthermore, vegetation resilience and resistance to climate change have regionally changed over the last three decades. In particular, the results suggest that vegetation resilience has increased in tropical forests and that vegetation resistance to temperature has increased in northern Eurasia. In contrast, ecosystem models cannot capture changes in vegetation resilience and resistance over the past thirty years. Overall, this study establishes a benchmark of vegetation resilience and resistance to climate change at the global scale, which is useful for further understanding ecological mechanisms of vegetation dynamics and improving ecosystem models, especially for dynamic resilience and resistance.
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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 (February 1, 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 surface processes associated with different vegetation types and their changes because the NNR does not use surface observations over land, whereas ERA-40 only uses surface temperature observations indirectly, in order to initialize soil temperature and moisture. As a result, the OMR trends can provide an estimate of surface effects on the observed temperature trends missing in the reanalyses. The OMR trends obtained from observation minus NNR show a strong and coherent sensitivity to the independently estimated surface vegetation from normalized difference vegetation index (NDVI). The correlation between the OMR trend and the NDVI indicates that the OMR trend decreases with surface vegetation, with a correlation < −0.5, indicating that there is a stronger surface response to global warming in arid regions, whereas the OMR response is reduced in highly vegetated areas. The OMR trend averaged over the desert areas (0 < NDVI < 0.1) shows a much larger increase of temperature (∼0.4°C decade−1) than over tropical forest areas (NDVI > 0.4) where the OMR trend is nearly zero. Areas of intermediate vegetation (0.1 < NDVI < 0.4), which are mostly found over midlatitudes, reveal moderate OMR trends (approximately 0.1°–0.3°C decade−1). The OMR trends are also very sensitive to the seasonal vegetation change. While the OMR trends have little seasonal dependence over deserts and tropical forests, whose vegetation state remains rather constant throughout the year, the OMR trends over the midlatitudes, in particular Europe and North America, exhibit strong seasonal variation in response to the NDVI fluctuations associated with deciduous vegetation. The OMR trend rises up approximately to 0.2°–0.3°C decade−1 in winter and early spring when the vegetation cover is low, and is only 0.1°C decade−1 in summer and early autumn with high vegetation. However, the Asian inlands (Russia, northern China with Tibet, and Mongolia) do not show this strong OMR variation despite their midlatitude location, because of the relatively permanent aridity of these regions.
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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 (July 6, 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 analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described. Findings The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November. Originality/value This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.
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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 (September 11, 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. The simulated ecological functions were the carbon stock function (CS), soil conservation function (SC), and the water conservation function (WC). The results showed that: (1) Compared with 2001–2020, the proportion of area by shrub and grass vegetation that was predicted to transform into forest accounted for more than 1% of the area in the LP under the SSP119 scenario and 3% of the area in the LP under the SSP585 scenario during 2081–2100, respectively. (2) Compared with 2001–2020, the CS would decrease in the central and south-eastern regions, the SC would decrease in the western regions, and the WC would decrease in the Qilian, Wushaoling, Xinglong and Liupan Mountains during 2081–2100. (3) The relationships and the corresponding regions between the ecological functions of the vegetation and the corresponding regions in the LP would change significantly under climate change from 2001–2020 to 2081–2100.These results indicate that a process-based dynamic vegetation model can capture the changes in the carbon and water fluxes under changes in the climate and CO2 concentration. It can also capture the vegetation succession, changes in ecological functions, and the transformation of functional relationships, which provide information that is conducive to the management and restoration of vegetation on the LP. This study supplies a novel perspective for vegetation management and high-quality development in other ecologically fragile regions worldwide.
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Nordbakken, Jørn‐Frode. "Fine‐scale five‐year vegetation change in boreal bog vegetation." Journal of Vegetation Science 12, no. 6 (February 24, 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 (May 29, 2013): 32–41. http://dx.doi.org/10.1111/avsc.12050.
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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 (September 13, 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 vegetation and climate factors, and a residual trend analysis was used to quantify the influence of climate change and human activities on vegetation change. The results indicated that the average NDVI value (0.75) of the UWNR from 1982 to 2020 was relatively high. The average coefficient of variation for the NDVI was 0.059, and the vegetation change was relatively stable. The vegetation in the UWNR increased 0.013/10 year on average, but the vegetation degradation in some areas was serious and mainly classified as agricultural land. The results of a future trend analysis showed that the vegetation in the UWNR is mainly negatively sustainable, and 62.54% of the vegetation will degrade in the future. The NDVI of the UWNR was more affected by temperature than by precipitation, especially on agricultural land and forestland, which were more negatively affected by warming. Climate change and human activities have an impact on vegetation changes, but the spatial distributions of the effects differ. The relative impact of human activities on vegetation change accounted for 64.5%, which was higher than that of climate change (35.5%). Human activities, such as the large proportion of agriculture, rapid population growth and the rapid development of urbanization were the main driving forces. Establishing a cross-border drought joint early warning mechanism, strengthening basic agricultural research, and changing traditional agricultural farming patterns may be effective measures to address food security and climate change and improve vegetation in the UWNR.
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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 (December 3, 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 and perturbed high-altitude meteorological observations. Monthly perturbations were calculated using the differences in outputs between the present- and a future-climate scenario from 11 regional climate models. In the three basins, future climate change alone decreased the modeled peak snow water equivalent (SWE) by 11 %–47 % and increased the modeled evapotranspiration by 14 %–20 %. However, including future changes in vegetation and soil for each basin changed or reversed these climate change outcomes. In Wolf Creek in the Yukon Territory, Canada, a statistically insignificant increase in SWE due to vegetation increase in the alpine zone was found to offset the statistically significant decrease in SWE due to climate change. In Marmot Creek in the Canadian Rockies, the increase in annual runoff due to the combined effect of soil and climate change was statistically significant, whereas their individual effects were not. In the relatively warmer Reynolds Mountain in Idaho, USA, vegetation change alone decreased the annual runoff volume by 8 %, but changes in soil, climate, or both did not affect runoff. At high elevations in Wolf and Marmot creeks, the model results indicated that vegetation/soil changes moderated the impact of climate change on peak SWE, the timing of peak SWE, evapotranspiration, and the annual runoff volume. However, at medium elevations, these changes intensified the impact of climate change, further decreasing peak SWE and sublimation. The hydrological impacts of changes in climate, vegetation, and soil in mountain environments were similar in magnitude but not consistent in direction for all biomes; in some combinations, this resulted in enhanced impacts at lower elevations and latitudes and moderated impacts at higher elevations and latitudes.
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Strandberg, G., and E. Kjellström. "Climate Impacts from Afforestation and Deforestation in Europe." Earth Interactions 23, no. 1 (February 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 exceptions, mainly in regions with little water available for ET. In such regions, changes in albedo are relatively more important for temperature. The simulated biogeophysical effect on seasonal mean temperature varies between 0.5° and 3°C across Europe. The effect on minimum and maximum temperature is larger than that on mean temperature. Increased (decreased) mean temperature is associated with an even larger increase (decrease) in maximum summer (minimum winter) temperature. The effect on precipitation is found to be small. Two additional simulations in which vegetation is changed in only one-half of the domain were also performed. These simulations show that the climatic effects from changed vegetation in Europe are local. The results imply that vegetation changes have had, and will have, a significant impact on local climate in Europe; the climatic response is comparable to climate change under RCP2.6. Therefore, effects from vegetation change should be taken into account when simulating past, present, and future climate for this region. The results also imply that vegetation changes could be used to mitigate local climate change.
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Carranza, Maria Laura, Carlo Ricotta, Paola Fortini, and Carlo Blasi. "Quantifying landscape change with actual vs. potential natural vegetation maps." Phytocoenologia 33, no. 4 (November 19, 2003): 591–601. http://dx.doi.org/10.1127/0340-269x/2003/0033-0591.
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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 (December 4, 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 vegetation changes caused by human activities to the runoff change of the Tangnaihai Hydrometric Station. The results showed that: (1) the annual runoff and precipitation in the source area of the Yellow River have a downward trend, while the annual potential evaporation and NDVI (Normalized Difference Vegetation Index) show an increasing trend; (2) The contribution rates of climate change and human activities to the vegetation change in the source area of the Yellow River is 62.79% and 37.21%, respectively; (3) The runoff change became more and more sensitive to changes in climate and underlying surface characteristic parameters; (4) The contribution rates of climatic factors (including precipitation, potential evaporation, and subsequent vegetation changes) and vegetation changes caused by human activities to the runoff change at Tangnaihai Hydrological Station are 75.33% and 24.67%, respectively; (5) The impact of precipitation on runoff reduction is more substantial than that of potential evaporation.
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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 (July 4, 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 in scenarios were compared with the base period 1980–1999. Then, a Normalized Difference Vegetation Index climate factor regression model was established to analyze changes to vegetation cover under the climate change scenarios. Finally, a Soil and Water Assessment Tool model was built to simulate the response of runoff and sediment yield in the three future periods under two different scenarios: only changes in climate and synergistic changes in climate and vegetation cover. The temperature and precipitation projections showed a significant increasing trend compared to the baseline condition for both emission scenarios. Climate change is expected to increase the average annual runoff by 15%–38% compared with the base period, and the average annual sediment yield will increase by 4%–32%. The response of runoff and sediment yield varies in different periods, scenarios, and sub-watersheds. Climate-driven vegetation cover changes have an impact on runoff and sediment yield in the watershed, resulting in a difference of 5.8%–12.9% to the total changes. To some extent, the changes in vegetation cover will inhibit the hydrological impact of climate changes. The study helps to clarify the effects of climate and vegetation cover factors on hydrological variations in watersheds and provides further support for understanding future hydrological scenarios and implementing effective protection and use of water and soil resources.
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Lai, Wenli, Mingming Wang, Jun Wei, Jie Zhang, Jiayu Song, Haiyan Zhou, Shuren Chou, and Yongping Wang. "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 broad-leaved forests (TrendNDVI = +0.0152). For >66% of China, vegetation is more sensitive to temperature and less sensitive to precipitation based on the regression coefficients. The water demand for vegetative growth increased significantly from 1999 to 2015 with global warming, especially in parts of the temperate zone. We defined a relative change in the residual trend to quantify the impact of human activities on vegetation. RESTREND NDVI / NDVI in two periods (P1, 1982–1998 and P2, 1999–2015) markedly increased, indicating that human activities play a key role in the reversal of land degradation.
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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 of 0.06 per decade. About 27.4% of the area was found to have experienced a significant negative trend in vegetation cover, while only 0.34% exhibited significant increasing vegetation vigour. Land surface temperature increased at a mean rate of 0.75°C/decade, with higher rates in agriculture, savanna, settlements, woodlands, and riparian vegetation than in forest and mangrove vegetations. Precipitation also reduced at a mean rate of 58.69 mm/decade, with higher rates in agriculture savanna and riparian vegetation than in sahelian grasslands, mangrove, forest, and woodlands. NDVI was negatively correlated with temperature in savanna, settlements, degraded forest, and sahelian grasslands providing confirmation of ongoing land degradation. It was concluded that vegetation vigour will continue to decline under rainfall and increasing temperature conditions especially in dryer regions. The use of land surface temperature in this study is particularly valuable in highlighting areas where changes in NDVI occurred as a result of synergistic action between climate and human-induced landcover changes. Our findings underscore the importance of landuse policies that account for spatial variation in synergistic relationships between the nexus of climate and land conversion processes that influence vegetation cover change in different landcover types in tropical regions.
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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 Australia with nested and non-nested transect designs and related the observed vegetation change to soil, landscape and climate to determine the strongest environmental associations. Ordination, principal components analysis (PCA) and threshold indicator taxa analysis (TITAN) were used to detect potential ecotones associated with environmental thresholds. Results from the two transects were compared with test the effects of survey method and spatial sampling on pattern detection. Ordinations and regressions for both transects indicated vegetation changed linearly along the environmental gradient. Species richness and total cover increased with rainfall. Species turnover was very high, with low nestedness, indicating high susceptibility to environmental change. Climate is the major driver of broad-scale vegetation change on our gradient and at this scale vegetation trends are detectable with a range of survey methodologies. TITAN identification of a threshold within the shorter, nested transect (but not the longer transect which extended into the arid zone) indicated that survey methodology influences ecotone detectability, and that although smaller-scale vegetation disjunctions may be present, change spanning the entire mesic to arid zone is largely monotonic.
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Yu, Yang, Li, and Yang. "An Improved Conceptual Model Quantifying the Effect of Climate Change and Anthropogenic Activities on Vegetation Change in Arid Regions." Remote Sensing 11, no. 18 (September 10, 2019): 2110. http://dx.doi.org/10.3390/rs11182110.
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Vegetation shows a greening trend on the global scale in the past decades, which has an important effect on the hydrological cycle, and thus quantitative interpretation of the causes for vegetation change is of great benefit to understanding changes in ecology, climate, and hydrology. Although the Donohue13 model, a simple conceptual model based on gas exchange theory, provides an effective tool to interpret the greening trend, it cannot be used to evaluate the impact from land use and land cover change (LULCC) on the regional scale, whose importance to vegetation change has been demonstrated in a large number of studies. Hence, we have improved the Donohue13 model by taking into account the change in vegetation cover ratio due to LULCC, and applied this model to the Yarkand Oasis in the arid region of northwest China. The estimated change trend in leaf area index (LAI) is 1.20%/year from 2001 to 2017, which accounts for approximately half of the observed (2.31%/year) by the moderate resolution imaging spectroradiometer (MODIS). Regarding the causes for vegetation greening, the contributions of: (1) LULCC; (2) atmospheric CO2 concentration; and (3) vapor pressure deficit were: (1) 88.3%; (2) 40.0%; and (3) −28.3%, respectively, which reveals that the largest contribution was from LULCC, which is probably driven by increased total water availability in whole oasis with a constant transpiration in vegetation area. The improved Donohue13 model, a simple but physics-based model, can partially explain the impact of factors related to climate change and anthropogenic activity on vegetation change in arid regions. It can be further combined with the Budyko hypothesis to establish a framework for quantifying the changes in coupled response of vegetation and hydrological processes to environment changes.
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LIU, Wei-guo, Wen-shou WEI, and Zhi-hui LIU. "NPP Change in Vegetation in Xinjiang under Climate Change." Arid Zone Research 26, no. 2 (April 14, 2010): 206–11. http://dx.doi.org/10.3724/sp.j.1148.2009.00206.
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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 temperature of the coldest month (TMC)，warmth index (WI)， winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.
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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 temperature of the coldest month (TMC)，warmth index (WI)， winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.
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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 (May 1, 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 performed six 13-yr experiments using historical and future large-scale climate boundary conditions from the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1). The land-cover scenarios included historical and future natural vegetation from the Mapped Atmosphere-Plant-Soil System-Century 1 (MC1) dynamic vegetation model, in addition to a future 8-million-ha California afforestation scenario. Natural vegetation changes alone caused summer daily-mean 2-m air temperature changes of −0.7° to +1°C in regions without persistent snow cover, depending on the location and the type of vegetation change. Vegetation temperature changes were much larger than the 2-m air temperature changes because of the finescale spatial heterogeneity of the imposed vegetation change. Up to 30% of the magnitude of the summer daily-mean 2-m air temperature increase and 70% of the magnitude of the 1600 local time (LT) vegetation temperature increase projected under future climate change were attributable to the climate-driven shift in land cover. The authors projected that afforestation could cause local 0.2°–1.2°C reductions in summer daily-mean 2-m air temperature and 2.0°–3.7°C reductions in 1600 LT vegetation temperature for snow-free regions, primarily because of increased evapotranspiration. Because some of these temperature changes are of comparable magnitude to those projected under climate change this century, projections of climate and vegetation change in this region need to consider these climate–vegetation interactions.
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Vranová, V., P. Formánek, K. Rejšek, and M. Pavelka. "Impact of land-use change on proteolytic activity of mountain meadows." Soil and Water Research 4, No. 3 (September 22, 2009): 122–25. http://dx.doi.org/10.17221/16/2009-swr.
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Casein-protease activity assessed at 50°C and with adjustment of optimum pH conditions (PA), and casein-protease activity near soil pH and at field soil temperature (LPA) were studied one vegetation period in mountain meadow soils covered with moderately mown vegetation, and over which vegetation had been abandoned for thirteen years. PA peaked in the first part of the vegetation season whereas LPA increased throughout the season; in addition, LPA was not linearly related to temperature (r = 0.127 resp. 0.312; P > 0.05). The combined effect of field soil temperature and pH decreased a casein-protease activity by > 98.4%. A management of meadows had no significant (P > 0.05) effect on PA and LPA.
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Overpeck, Jonathan T., and David D. Breshears. "The growing challenge of vegetation change." Science 372, no. 6544 (May 20, 2021): 786–87. http://dx.doi.org/10.1126/science.abi9902.
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Sugden, Andrew M. "The pace of Holocene vegetation change." Science 372, no. 6544 (May 20, 2021): 803.17–805. http://dx.doi.org/10.1126/science.372.6544.803-q.
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Grimm, Eric C. "Vegetation Change on a Grand Scale." Ecology 69, no. 6 (December 1988): 2038–39. http://dx.doi.org/10.2307/1941187.
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