Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 + 4491 word(s) 4491 2021-07-01 11:33:28 |
2 format correct Meta information modification 4491 2021-07-14 08:46:11 | |
3 format correct Meta information modification 4491 2021-09-30 04:03:15 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Afuye, G. Vegetation Dynamics and Climate Change. Encyclopedia. Available online: (accessed on 04 December 2023).
Afuye G. Vegetation Dynamics and Climate Change. Encyclopedia. Available at: Accessed December 04, 2023.
Afuye, Gbenga. "Vegetation Dynamics and Climate Change" Encyclopedia, (accessed December 04, 2023).
Afuye, G.(2021, July 13). Vegetation Dynamics and Climate Change. In Encyclopedia.
Afuye, Gbenga. "Vegetation Dynamics and Climate Change." Encyclopedia. Web. 13 July, 2021.
Vegetation Dynamics and Climate Change

Climate extremes and their impacts on vegetation dynamics have been of great concern to the ecosystem and environmental conservation and the policy-decision makers. Of great concern now is that climate change impacts on vegetation dynamics have influenced the global terrestrial ecosystem adversely, thus making ecosystems vulnerability one of the current issues in ecological studies. For instance, the negative consequences attributed to natural hazards associated with climate extremes have been estimated to be billions of dollars across the globe. Accordingly, vegetation dynamics are influenced by several factors including climate change, environmental and climatic components among others. These can expend considerable impact on the water balance by evapotranspiration, interception and development strategy which has the potential to lead to vegetation degradation in a wide variety of ecosystems and biodiversity.

Vegetation Dynamics Climate Change Information-Based Model Risk assessment Vegetation distribution

1. Global Vegetation Response to Climate Change Impacts

Global vegetation response in the terrestrial ecosystem is considerably impacted by incoming climate variability and change [1][2]. Owing to the spatial variance of ecosystems, the responses of vegetation dynamics to climate change vary significantly with the different spatial patterns and sensitivity effects to global climate change [3][4]. This presents a feedback mechanism in vegetation-climate interactive effects. Of great concern now, is that climate change can significantly impede vegetation activities [5]. Studies showed that El Niño–Southern Oscillation (ENSO) influences the dynamics of vegetation in Africa [6][7], while certain regions tend to experience significant vegetation enhancement or suppression which depends on the ENSO phase [8][9][10]. An individual ENSO event can cause a variety of changes in vegetation intensity [11]. The magnitude and timing of the response of vegetation intensity to climate forcing may vary between different vegetation types and classification which depend on high spatial coverage and long-time series [7][12] to counter the reliance on ENSO episodes. Conversely, the interaction between the changes in air temperature and precipitation may influence the distribution of plants and vegetation vigour, thus temperature changes can impede the length of the growing season. Studies have reported that a warming climate may significantly enhance the process of respiration in vegetation, evapotranspiration, and increase the deficit of soil moisture which can influence vegetation growth [13][14]. Climate change impacts on natural and human activities may determine the immediate cause of the observed pattern in vegetation growth as a result of the interaction between climate change and the responses of vegetation dynamics [15][16][17]. Studies show that increasing global temperatures have a significant impact on the responses of vegetation, rising sea levels, and the environment [17][18]. Accordingly, air temperature exerts the highest influence on changes in the inter-annual variability of vegetation vigour after solar radiation and sunlight and precipitation change [19]. Conversely, in the Southern Hemisphere, the decline in precipitation is likely to have contributed to the drying trends and the resultant observed vegetation activity in the semi-arid regions such as southern-coastal Chile, southern Africa and south-eastern Australia [20][21]. Consequently, affecting certain structural characteristics of the world vegetation types and their associated functions in the earth-atmospheric system functioning, which are determined by the vegetation sensitivity response effect, productivity and distribution of plant species [1][13][22]. In general, global vegetation coverage has been noted to have undergone significant transformation, affecting species dynamics and grassland conditions [23][24]. The world’s vegetation types including the native vegetation such as forests, grasslands and shrublands are adversely affected by land, topography and soil (land cover change, drainage and erosion potentials and decreased cohesion of residual plant) in response to environmental factors [25][26]. The understanding of these factors on vegetation dynamics and related effects affords the adoption of targeted effective mitigation measures to ensure biodiversity sustainability including vegetation conservation.

1.1. Climate-Related Vegetation Interactions

According to the literature, scientific models are more significant in analysing climate-vegetation interactions and permit simulation of biogeochemical mechanisms. For instance, vegetation and terrestrial ecosystem services thus permit increases for the potential carbon dioxide (CO2) which may serve as the justification for predicting vegetation response to variability and change [27]. A study identified climate-related vegetation models such as the dynamic global vegetation model (DGVM) to be evident on crop yields, and to predict weather impacts and other important events on agriculture [28]. The processed-based model validates how climate change may alter crop yields and has shown to be a good indicator for agriculture, climate and the economy [29][30]. The experimental outcomes on the effect of climate change on terrestrial ecosystems and their functions on vegetation dynamics revealed a significant increase in rising air temperatures owing to a global warming climate [22][31]. Vegetation dynamics have been shown to have a fairly slow response to temperature and rainfall conditions; as the higher level of latent heat is found with a more vegetated area [32], while the sensible heat exchange was more prominent with a more sparsely vegetated region [33]. Nevertheless, oftentimes more than the immediate resultant effects of climate variability and change, the indirect aspects may upset the diversity of life and terrestrial ecosystem. Studies revealed the response lag as the period before the reaction to a perturbation is evident and occurs due to changes in vegetation and geomorphic temporal response [16][34]. Climate change-induced vegetation shifts may be related to climatic perturbation (i.e., the imposed perturbation on energy earth balance). Climate-vegetation interaction can be perturbed by human activities through deforestation, and natural extremes or surface disturbance [35][36]. Human-made forcings are the result of aerosols and gases from fossil fuel or anthropogenic activities, changes in land use, such as the transformation of the forest into agricultural land, loss of habitat, and other intense disturbances among others. Accordingly, the global land-use changes have transformed farmlands, grazing fields, human settlements, and urban area at the expense of natural vegetation with resultant land degradation, deforestation and loss of biodiversity [25][36]. The evaluation of vegetation dynamics and its increasing trends due to climatic and environmental conditions including rainfall, temperature, land, topography and soils play a key role in better understanding the vegetation stress and its related effects [37][38][39]. The natural response of land is the response of vegetation, soils and human-induced environmental changes, leading to the increasing atmospheric concentration (CO2), nitrogen deposition, and climate change [40]. Changes in vegetation coverage and biomass may lead to an alteration in the earth-atmosphere processes and climate dynamics [22]. Studies show that temperatures were found to be the major limiting factor for vegetation growth at high latitudes in the Northern Hemisphere [41] and Western Europe [42], while in Central Asia, South America and Southern Africa [43][29], declining precipitation and rising temperatures were correlated with a decline in vegetation vigour. Ref. [44] reveal that vegetation vigour will continue to decline under temperature and rainfall conditions especially in the arid or semi-arid region. The interconnections between climatic conditions and terrestrial ecosystems offer some insight into the status of vegetation [45]. Vegetation dynamics have undergone extreme climate change events both in Asia and Europe with agricultural drought hazards, landslides, heat-wave, increased risk and intensity of wildfires, and flooding, among others, including in Africa [13][46]. Vegetation growth is highly unstable and susceptible to drastic changes such as climate change [47]. Therefore, understanding the responses of vegetation to climate change and the precision of different types is important to guide decision-making on climate change impacts especially in the area of vegetation and forest resource management. This is fundamental in adopting targeted adaptation and mitigation strategies to improve resilience to climate change effects, for example, drought occurrences especially on rangeland vegetation which experiences additional pressure from overgrazing. The grazing pressure on vegetation is often altered in rangelands particularly in dense grassland where much of the primary production is being removed compared to open arid vegetation [48][49].

1.2. Socio-Economic Scenarios of Climate Change on Vegetation Dynamics

Vegetation dynamics are strongly influenced by global climate change, including complete seasonal cycles in the estimation of climate change associated with shifts in vegetation [50]. Consequently, socio-economic challenges that many low-income communities in the world such as Liberia, Somalia, Zimbabwe and other African countries experience are from the extreme weather and climate events related to the changing climate, with the resultant adverse effects on the ecosystem and human well-being [51]. Extreme climate events such as torrential rain, drought, wildfires and heat-waves are reported to threaten forest ecosystems and sustainable livelihoods, resulting in limited food and water supply, and forces families from their homes and pushes people into poverty in the low-income countries [52][53]. Studies have shown that the Arctic, small islands, South East Asia, and Western Europe including Africa are considered regions most susceptible to climate change and variability [54]. This present resultant multiple environmental changes, geographical location and low adaptive capacity [55]. Recurrent extreme climatic events are worsened to bring about socioeconomic losses and limit the capacity of local communities and individual resilience to cope and adapt to these potential challenges that might be induced by future climate change [56]. Climate change will adversely affect the socio-economic sectors such as forest management, water resources, agriculture, and human settlements as well as ecological systems [57]. Consequently, this poses several climate threats and risks in areas across the globe in which both rural and urban livelihood is built, particularly in Africa [58]. Developing countries such as China, Brazil, India and Somalia, whose population is vulnerable to extreme climate events, are at risk of natural human disturbances causing both socio-economic and climatic impact on the environment [59][60][61]. Even in developed countries, the significance of vegetation response to the pattern of land use and intense human activities have gained attention with livelihood activities susceptible to incoming climate change and variability [62]). Studies have reported that most countries are affected by climate change; in the magnitude of extreme heat or cold events with some confidence level of increased socio-economic impacts on vegetation and terrestrial ecosystem as well as human settlement [63][61].

1.3. Terrestrial Vegetation Responses to Future Climate Change

The pattern of weather and climate has been altered by global climate change around the world, causing degradation or drought in some regions and floods in others [64]. The frequency and intensity of these events are projected to increase as a result of global climate change. Future climate change impacts on vegetation and ecosystem conservation, sustainable livelihood and rural economies of poor societies in developing nations, especially among the local rural smallholder farmers. Appropriate mitigation and adaptation strategies to improve climate resilience and recovery should be put in place to empower communities and institutions to adapt, innovate and thrive [52][65]. Uncertainties in terrestrial vegetation responses to future climate change and biotic features provide key insights into the precise mechanisms associated with different spatial and socio-economic impacts. The risk from these uncertainties of future climate change requires strategies to respond to climate issues based on local knowledge of coping with uncertainty and systems spanning a wide range of spatiotemporal scales of model projections [66]. Climate change is projected to adversely impact biodiversity, environment and human settlement with associated impacts on agriculture and natural resources to survive the effects of extreme climate events [67]. The development of programmes in developing nations will alleviate hunger and poverty where limited water resources and increasing competition and conflict over natural resources determine their existence [68].

2. Vegetation Biodiversity Vulnerability to Future Climate Change

Vegetation biodiversity is vulnerable to changing climate with complexity in the hierarchy and high influence in diversity [66][69]. Consequently, global warming has brought several detrimental effects on environmental components including vegetation and ecosystem, making their vulnerability one of the current research hotspots in ecological studies [22][69]. Vegetation biodiversity vulnerability may be considered as exposure to contingencies, stress, and challenges in coping with the resultant climatic conditions which are determined by its location, extent and its biodiversity, and the number of linkages within the food cycle [70][71]. The magnitude and nature of stressors are determined relative to vulnerability such that, the assessment is restrained by uncertainties in the drivers of change such as climatic, physical and environmental, and other forms of threats. A recent meta-analysis established that the negative impacts of vegetation loss and fragmentation have been unduly severe in regions with high temperatures in the warmest month and decreasing rainfall, and the impacts varied across vegetation types [72]. A better understanding of the multidimensional vegetation biodiversity vulnerability to rapid climate change and other threats is needed concerning the socio-economic consequences of biodiversity loss and ecosystem services [73]. The inadequate observations of multifaceted systems under rapidly changing climate; the socio-economic and environmental change are the cause of the deficiencies spurred by key changes in species adaptive capacity, the role of species range movement, vegetation dynamics, and its response to climate change and variability [74][75]. Vegetation community to ecosystem vulnerability and landscape dynamics and their interaction with the changing climate and other threats cannot be overemphasised; therefore, these vulnerabilities are multifaceted and across a wide range of spatial and temporal scales. VRCC impacts on vegetation biodiversity vulnerability are amplified by the limited capacity to shift into suitable climates due to the near-relationship to certain ecological formations and the fragmentation of the landscape by agriculture and other land uses [26][76]. This is projected to significantly impact societal well-being if degradation of biodiversity results in a decline in the quantity and resilience of ecosystem service provision. Understanding how biodiversity is linked to vegetation is crucial for designing more sustainable environmental policy formulation and landscape planning. The significance of regressions in biodiversity and the consequences for vegetation and ecosystem services are increasingly projected for future climate scenarios. For instance, the over-exploitation of land use for agriculture and other purposes has led to drastic declines in vegetation biodiversity through rapid urbanisation, wildfire, high population growth and infrastructure development associated with changing patterns of land use [57]. The effects of declining vegetation with biodiversity and ecosystem degradation will be exacerbated by climate change, with consequences especially for human well-being and societies in the absence of effective management and planning outcomes. The complexity within these levels includes composition among elements, structure, and their functions of genetic through eco-regional diversity which contributes to the preservation of species diversity [77][78]. The key aspects of the vulnerability of biodiversity to climate change are considered from the ecology of species and their genetics through community and ecosystem dynamics and the states of species and their landscapes [79][80]. The corresponding challenges in integrating vegetation biodiversity vulnerability to changing climatic conditions in natural resource management and planning are inherently both important and challenging.

Spatial Assessment of Local Climate

Globally, studies on vegetation-climate responses and environmental impact postulate that extreme climatic events pose a severe risk in ecosystem services. This is a serious emerging concern across the globe, for example, in the USA [29], Australia [81], Europe [42], Asia [43] and Southern Africa [82], among others. The alteration of the natural environment in urban regions has made the surface temperatures and local air rise a few degrees higher than that of surrounding urban areas [83]. Local microclimate and meteorological variables such as rainfall, wind speed and surface temperature, among others, are often influenced by biophysical and chemical properties of soil ecology, anthropogenic activities and climatic condition in a relatively small area within vegetation canopies present in the environment [84][85]. The difference between the absorptive and reflective abilities of a surface to interact with incoming solar irradiance and associated heterogeneity of their physical characteristics often leads to the modification of climatic variables which may influence the drivers of vegetation coverage in urban settlement [86][87]. The relationship between edaphic factors and local micro-climatic patterns has led to the development of various climatological, geophysical, hydrological indices which have been studied in climate-vegetation interactive effects [88][89].

3. Linkage between Long-Term Vegetation Dynamics and Climate Change

Studies about the linkage between climate change and vegetation dynamics provide a lot of powerful scientific information [90][91]. Studies have shown a significant relationship between the terrestrial ecosystem and climatic variation [6][92][93]. Accordingly, studies characterise three key indicators in vegetation response to climatic variation. The first indicator is the sensitivity effect which refers to the condition of susceptibility for measuring inter-annual climatic disturbances or the degree to which vegetation is responsive to incoming climate variability and change, for example, inter-annual variability in weather and climate [19][94]. The second indicator is the sensitivity effect on vegetation productivity, which is the magnitude, long-term and seasonal variability along gradients of aridity varying from semi-arid to sub-humid conditions [6][94]. This is done to detect and spatially delineate anomalies in vegetation condition, growth and development, in both length and intensity, for example, climate interaction with vegetation structure, biogeochemical cycles and energy fluxes [95][48][96]. The third indicator is the distribution as well as their response to climate change based on the spatial distribution and cover change, associated with terrain characteristics of vegetation types, human activity and changing climate [70][97]. Consequently, spatiotemporal vegetation monitoring and assessment of its dynamics at large scales are vital to design appropriate measures needed to address the multiple threats at different time scales [6][98].

The novel climate approach used for climate change projections could simulate the observed climate at spatiotemporal scales to provide novel space-based solutions in earth observations and to detect and monitor vegetation trends, sensitivity effect, productivity as well as distribution. The ecosystem’s biodiversity is complex in the hierarchy with high influence in diversity including agricultural drought hazard, flood, torrential rainfall and environmental factors [66]. The physical, socio-economic, and infrastructural project are the testaments of the impact of climate change on livelihood and other environment-related effects on vegetation vigour [99]. Climate change variations have been considered to pose major threats to the terrestrial ecosystem and sustainable human settlement [100]. The spatial observation of regional climate on vegetation and plant phenology such as the increasing temperature trend on vegetation dynamics and the emergence of environmental threats to ecosystem functioning has revealed a positive correlation [101]. Global climate change has been reported to reveal the drying and warming trend and thus, will continue to experience unprecedented increased warming climate as a consequence of natural and other human disturbances [102]. The understanding of the long- and short-term natural fluctuations in climate is crucial in tracking the effect of human-induced climate change occurring from year to year and decade to decade on ecosystem dynamics. The natural climate fluctuations in different climates have a direct impact on drivers of ecosystem change such as drought, floods, wildfires and alien invasion, as well as the timing of vegetation greening [103][104]. Studies have revealed that the large-scale inter-annual fluctuations in weather and climate are caused by the changes in the pattern of oceanic circulation and atmospheric pressure in response to global warming [8][9][105]. The responses of vegetation to short-term variation have far prominent impacts in the short interval because of its short-term climate change (e.g., El Niño occurs in cycles and lasts from days to a year), and its causes are of greater significance to human activities compared to the long-term changes in rainfall and temperature trend [63]. A recent study used precipitation and temperature to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015 [9]. Their results point out that anomalies of NDVI correlate differently with precipitation and temperature during the long and short rainy seasons, which indicates that, the moisture source in each of the seasons influences vegetation dynamics over East Africa. The effect of ENSO on NDVI series is predominant when vegetation is considered in seasons before actual months, suggesting a time lag between them. In general, there is a need to characterise the linkage between long-term temporal vegetation variability and climate change impacts on terrestrial ecosystems. This is because a deeper understanding is needed on key issues of vegetation dynamics to improve our comprehension of vegetation responses to climate change.

The information in Table 1 reveals the techniques used in global vegetation–climate response analysis to highlight the types of indices, algorithms, remote sensing imagery used as well as their findings or gaps filled. The gaps filled in the various studies highlighted their findings with different vegetation indices such as Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Enhanced Vegetation Condition Index (EVI), Vegetation Health Index (VHI) and Leaf Area Index (LAI) among others in contributing to the understanding of vegetation dynamics and their response to climate change. Therefore, these indices are broad spatiotemporal vegetation monitoring and drought indicators and a step toward monitoring global climate change. A recent study has shown that the monthly NDVI, VHI and VCI trends were most considered suitable indices and showed a good signal in the assessment of spatiotemporal changes in vegetation dynamics and drought over South Asia [106]. Nevertheless, their results varied based on topography and climatic condition for different vegetation types and distribution. More so, most studies showed that there exists a positive correlation between the response of vegetation and different climatic parameters such as precipitation and temperature [13][82]; however, some showed a negative correlation [106][107]. The newest GIMMS NDVI3g dataset from AVHRR showed a good surrogate measure in the length of the growing season of the physiologically functioning surface greenness level of a region [108][93][109][110].

Table 1. Techniques used in global vegetation-climate response analysis.

S/N Vegetation Indices Algorithms Remote Sensing (RS) Imagery Findings/Gaps References
1. Normalized Difference Vegetation Index NDVI3g derived from (GIMMS) NDVI = (λNIR  λRED)(λNIR + λRED) Advanced Very High-Resolution Radiometer NOAA (AVHRR) Findings show that NDVI significantly increased in most seasons at the regional scale. AVHRR NDVI3g show good quality and the correlation between growing season NVDI and low precipitation was significantly positive. [111]
2. Enhanced Vegetation Index (EVI) EVI = G ∗ ρNIR  ρRedρNIR + C1  ρRed  C2  ρBlue + L  Moderate Resolution Imaging Spectrometer (MODIS) The model performance improved using lags of up to one year and found that a one-month lag provided the best explanatory power for vegetation responses to variability on different timescales. [1]
3. Leaf Water Content Index (LWCI) LWCI = G x log[1  (NIR  SWR)]log[1  NIR  SWIR] Landsat TM Findings reveal that the model could apply not only to the forest area but also to the agricultural area indicating that the time lag comparison between LWCI and NDVI was significantly observed about a month in the tropical forest while it was barely observed in the temperate deciduous forest. [112]
4. Leaf Area Index (LAI) LAĪ(τ) = 1τ τtLAI(τ) Moderate Resolution Imaging Spectrometer (MODIS) The model shows that the vegetation status is positively sustainable and there limited accuracy of LAI for sparsely vegetated arid areas which indicates that the findings require support from detailed fieldwork at a local scale. [113]
5. Fraction of Photosynthetically Active Radiation (fAPAR) FPAR = [PARci  PARcr  (PARgi  PARgr)]PARci Moderate Resolution Imaging Spectrometer (MODIS) The model showed higher assessment accuracy up to 16% when compared with FPAR assessment models based on a single vegetation index. Findings show that vegetation productivity is significantly affected by environmental factors; hence, the effect of FPAR cannot be neglected in the satellite-derived FPAR algorithms. [114]
6. Vegetation Condition Index (VCI) VCIijk = VIijk  VIi,min     VIi,max  VIi,min ∗ 100 Moderate Resolution Imaging Spectrometer (MODIS) Findings show that the VCI widely distributed vegetation stress for a long period and enhanced the trend of vegetation activity. Hence, the VCI should be cautiously used in the context of climate warming but may vary with different topography and climatic condition for different vegetation distributions. [115]
7. Temperature Condition Index (TCI) TCI = 100 ∗ (NDVI  NDVImin)(NDVImax  NDVImin) Advanced Very High-Resolution Radiometer (AVHRR) sensor of the NOAA satellite Findings show that the model has the advantage of being independent of the surface type and is available for all regions where a sparse weather-observing network exists. TCI should be jointly used with VCI to reflect the meteorological conditions and drought monitoring. [116]
8. Vegetation Health Index (VHI) VHI = αVCI + TCI (1 − α) TCI Advanced Very High-Resolution Radiometer (AVHRR) sensor of the NOAA satellite Findings show that the northern ecosystems are characterised by positive correlations, indicating that increasing temperature favourably influence vegetation activity. Hence, the VHI should be undertaken with caution, especially in high-latitude regions where vegetation growth is primarily limited by lower temperatures which are opposite to the low-latitudes, mainly in arid, semi-arid and sub-humid climatic regions. [117]
9. Soil-adjusted Vegetation Index (SAVI) SAVI = (NIR  RED)](NIR + RED + L) ∗ (1 + L) Satellite Pour l’Observation de la Terre
(SPOT-6 and SPOT-7) satellite
The model was found to be an important step toward the development of global models that can describe dynamic soil-vegetation systems from remotely sensed data using the most sensitive L-factor value for SAVI. Findings indicate that the SAVI is suitable for distinguishing between the vegetation and non-vegetation areas of mangrove forest. [118]

The information in Table 2 shows the types of climatic parameters used as well as their temporal reference. The findings established in the various literature highlighted the relationship between vegetation dynamics and forms of extreme climate events; including agricultural drought hazard, floods and heat-waves among others, which have thus, greatly impacted the global terrestrial ecosystem. Although the results varied based on location and climatic zone, in general, most studies have reported that there exists a positive correlation between the response of vegetation and different environmental factors such as temperature, drought impact, land-use change, soil moisture, precipitation, variability and environmental changes, among others. Some reported a negative relationship, i.e., increasing temperature response and vegetation parameters with decreased precipitation over species transformation affecting ecosystem dynamics. Consequently, vegetation dynamics and its response to climate change are critical challenges for ecological conservation and restoration policy.

Table 2. Related studies on climate extreme events and their associated impact on vegetation.

S/N Forms of Extreme Climate Events Continent Country Duration Author Data Source (Models and Climate Variables) Data
1. Agricultural drought hazard and drastic decline of vegetation Asia China 1982–2012
(30 years)
[43] SPEI from AVHRR, seasonal NDVI and Mann-Kendall (MK) Test, Climate data Meteorological air temperature, precipitation, and evaporation
2. Severe flash flooding and intense storm Asia China August 2015 [92] Climate data (nearest rain gauge), hydrodynamic model, Digital Elevation Model downstream from LiDAR Field survey numerical hydrodynamic simulation and rainfall
3. Flood damage to croplands and grassland Europe Germany and France 2002–2007
(5 years)
[42] Evaluation of direct and indirect flood losses and the State of Saxony in Germany Interviews, review of flood loss estimation, water depth, inundation duration for cropland, magnitude and flow velocities
4. Flooding and Agricultural drought North America USA (1985–2005) (20 years) [29] Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and NDVI from NOAA AVHRR dataset, Global Vegetation Index (GVI) from global area coverage (GAC) data, and Climate data Soil moisture, snow cover, precipitation, solar radiation, and air temperature
5. Drought and floods Asia China 1880–1998 [59] The long-term observational study, National Natural Foundation of China, dust storm from Beijing Weather Station, and Climate data Drought index, inter-decadal changes, surface temperature anomalies, and precipitation based on a documented record
6. Floods, agricultural damage, uprooted vegetation, and landslide/earthquake Western Asia Yemen 1973–2008
(35 years)
[46] Global Facility for Disaster Risk Reduction (GFDRR), Wadi Flood protection system and Emergency Events Database Desk reviews of the data including triangulation and field visits and surveys in the affected areas
7. Floods, drought, and landslides South Asia Colombo,
Sri Lanka
(13 years)
[119] Sri Lanka and Civic Force, Disaster Management Centre, and Ministry of Foreign Affairs of Japan Questionnaire survey involving quantitative and qualitative questions
8. Agricultural drought hazard Africa South Africa 2015–2017
(2 years)
[82] Department of Water and Sanitation, Department of Environmental Affairs, MOD13Q1 data from MODIS, Climate data and census data Vegetation Condition Index (VCI), Standard Precipitation Evapotranspiration Index (SPEI), precipitation and temperature
9. Torrential rainfall, heat waves, and agricultural drought Arica Gambia 2017–2018
[120] Ministry of Finance and Economic Affairs and Gambian Disaster Management Agency A multi-modal cross-sectional survey comprising online/electronic survey software and a face-to-face interview
10. The drastic decline of vegetation and narrow grazing, and shortage of water resources Africa South Africa 2019 [121] Multistage sampling procedure, snowball sampling approach
statistical program
A cross-sectional household survey, Simple descriptive statistical tools


  1. Seddon, A.W.R.; Macias-Fauria, M.; Long, P.R.; Benz, D.; Willis, K. Sensitivity of global terrestrial ecosystems to climate variability. Nat. Cell Biol. 2016, 531, 229–232.
  2. Liu, Z.; Notaro, M.; Kutzbach, J.; Liu, N. Assessing Global Vegetation–Climate Feedbacks from Observations. J. Clim. 2006, 19, 787–814.
  3. Turner, M.G.; Calder, W.J.; Cumming, G.; Hughes, T.P.; Jentsch, A.; LaDeau, S.; Lenton, T.M.; Shuman, B.N.; Turetsky, M.R.; Ratajczak, Z.; et al. Climate change, ecosystems and abrupt change: Science priorities. Philos. Trans. R. Soc. B Biol. Sci. 2020, 375, 20190105.
  4. Zhang, Y.; Ye, A. Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China. Int. J. Remote Sens. 2020, 41, 4238–4268.
  5. Mennis, J. Exploring the Influence of ENSO on African Vegetation Variability Using Multidimensional Map Algebra. Giscience Remote Sens. 2006, 43, 352–376.
  6. Fensholt, R.; Proud, S.R. Evaluation of Earth Observation Based Global Long Term Vegetation Trends — Comparing GIMMS and MODIS Global NDVI Time Series. Remote. Sens. Environ. 2012, 119, 131–147.
  7. Anyamba, A.; Tucker, C.J.; Mahoney, R. From El Niño to La Niña: Vegetation response patterns over east and southern Africa during the 1997–2000 period. J. Clim. 2002, 15, 3096–3103.
  8. Fer, I.; Tietjen, B.; Jeltsch, F.; Wolff, C. The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario. Biogeosciences 2017, 14, 4355–4374.
  9. Kalisa, W.; Igbawua, T.; Henchiri, M.; Ali, S.; Zhang, S.; Bai, Y.; Zhang, J. Assessment of climate impact on vege-tation dynamics over East Africa from 1982 to 2015. Sci. Rep. 2019, 9, 1–20.
  10. Plisnier, P.D.; Serneels, S.; Lambin, E.F. Impact of ENSO on East African ecosystems: A multivariate analysis based on climate and remote sensing data. Glob. Ecol. Biogeogr. 2000, 9, 481–497.
  11. Pettorelli, N.A.; Chauvenet, J.P. Duffy Tracking the effect of climate change on ecosystem functioning using protected areas: Africa as a case study. Ecol. Indic. 2012, 20, 269–276.
  12. Pricope, N.G.; Husak, G.; Lopez-Carr, D.; Funk, C.; Michaelsen, J. The climate-population nexus in the East Af-rican Horn: Emerging degradation trends in rangeland and pastoral livelihood zones. Glob. Environ. Chang. 2013, 23, 1525–1541.
  13. Wang, X.; Wu, C.; Peng, D.; Gonsamo, A.; Liu, Z. Snow cover phenology affects alpine vegetation growth dy-namics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers. Agric. For. Meteorol. 2018, 256, 61–74.
  14. IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge UK; New York, NY, USA, 2013; 1535p.
  15. Wu, Z.; Wu, J.; Liu, J.; He, B.; Lei, T.; Wang, Q. Increasing terrestrial vegetation activity of ecological restoration program in the Beijing–Tianjin Sand Source Region of China. Ecol. Eng. 2013, 52, 37–50.
  16. Hua, L.; Wang, H.; Sui, H.; Wardlow, B.; Hayes, M.J.; Wang, J. Mapping the spatial-temporal dynamics of vege-tation response lag to drought in a semi-arid region. Remote Sens. 2019, 11, 1873.
  17. Turner, M.G. Disturbance and landscape dynamics in a changing world. Ecology 2010, 91, 2833–2849.
  18. Wang, X.; Piao, S.; Ciais, P.; Li, J.; Friedlingstein, P.; Koven, C.; Chen, A. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proc. Natl. Acad. Sci. USA 2011, 108, 1240–1245.
  19. Chen, C.; He, B.; Yuan, W.; Guo, L.; Zhang, Y. Increasing interannual variability of global vegetation greenness. Environ. Res. Lett. 2019, 14, 124005.
  20. Frederiksen, J.S.; Frederiksen, C.S.; Osbrough, S.L.; Sisson, J.M. Changes in Southern Hemisphere rainfall, circulation and weather systems. In Proceedings of the 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011; pp. 2712–2718.
  21. Cai, W.; Cowan, T.; Thatcher, M. Rainfall reductions over Southern Hemisphere semi-arid regions: The role of subtropical dry zone expansion. Sci. Rep. 2012, 2, 702.
  22. Friend, A.D.; Lucht, W.; Rademacher, T.T.; Keribin, R.; Betts, R.; Cadule, P.; Ciais, P.; Clark, D.B.; Dankers, R.; Falloon, P.D.; et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci. USA 2014, 111, 3280–3285.
  23. Zhang, Y.; Qi, W.; Zhou, C.; Ding, M.; Liu, L.; Gao, J.; Bai, W.; Wang, Z.; Zheng, D. Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982. J. Geogr. Sci. 2014, 24, 269–287.
  24. Zeng, Z.; Li, Y.; Wu, W.; Zhou, Y.; Wang, X.; Huang, H.; Li, Z. Spatio-Temporal Variation of Drought within the Vegetation Growing Season in North Hemisphere (1982–2015). Water 2020, 12, 2146.
  25. Zhao, X.; Zhu, H.; Dong, K.; Li, D. Plant Community and Succession in Lowland Grasslands under Saline-Alkali Conditions with Grazing Exclusion. Agron. J. 2017, 109, 2428–2437.
  26. Vijith, H.; Hurmain, A.; Dodge-Wan, D. Impacts of land use changes and land cover alteration on soil erosion rates and vulnerability of tropical mountain ranges in Borneo. Remote Sens. Appl. Soc. Environ. 2018, 12, 57–69.
  27. Pearson, R.G.; Phillips, S.J.; Loranty, M.; Beck, P.S.A.; Damoulas, T.; Knight, S.J.; Goetz, S. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Chang. 2013, 3, 673–677.
  28. Friend, A.D.; Arneth, A.; Kiang, N.Y.; Lomas, M.; Ogee, J.; Rödenbeck, C.; Running, S.W.; Santaren, J.-D.; Sitch, S.; Viovy, N.; et al. FLUXNET and modelling the global carbon cycle. Glob. Chang. Biol. 2007, 13, 610–633.
  29. Kogan, F.; Salazar, L.; Roytman, L. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. Int. J. Remote Sens. 2011, 33, 2798–2814.
  30. Afuye, G.A.; Ojeh, V.N.; Okunlola, B.A.; Adejokun, V.F. Heat-Sum Calculation in Forecasting Maize Phenological Stages and Harvesting Date in Lagos South West, Nigeria. J. Geogr. Environ. Earth Sci. Int. 2018, 1, 1–12.
  31. Afuye, G.A.; Ojeh, V.N. Temporal Variations in Ambient Carbon Monoxide Concentrations between Weekdays and Weekends in Akure Central Business District, South West Nigeria. Phys. Sci. Int. J. 2017, 1, 1–2.
  32. Wu, M.; Schurgers, G.; Rummukainen, M.; Smith, B.; Samuelsson, P.; Jansson, C.; Siltberg, J.; May, W. Vegetation–climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth Syst. Dyn. 2016, 7, 627–647.
  33. Klein, C.; Bliefernicht, J.; Heinzeller, D.; Gessner, U.; Klein, I.; Kunstmann, H. Feedback of observed interannual vegetation change: A regional climate model analysis for the West African monsoon. Clim. Dyn. 2017, 48, 2837–2858.
  34. Marston, R.A. Geomorphology and vegetation on hill slopes: Interactions, dependencies, and feedback loops. Geomorphology 2010, 116, 206–217.
  35. Nagy, L.; Artaxo, P.; Forsberg, B.R. Interactions between Biosphere, Atmosphere, and Human Land Use in the Amazon Basin: An Introduction. In Interactions between Biosphere, Atmosphere and Human Land Use in the Amazon Basin; Springer: Berlin/Heidelberg, Germany, 2016; pp. 3–15.
  36. Jiang, L.; Bao, A.; Guo, H.; Ndayisaba, F. Vegetation dynamics and responses to climate change and human ac-tivities in Central Asia. Sci. Total Environ. 2017, 599, 967–980.
  37. Singh, R.P.; Roy, S.; Kogan, F. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int. J. Remote Sens. 2003, 24, 4393–4402.
  38. Djebou, D.C.S.; Singh, V.P.; Frauenfeld, O.W. Vegetation response to precipitation across the aridity gradient of the Southwestern United States. J. Arid. Environ. 2015, 115, 35–43.
  39. Zhang, Y.; Zhang, C.; Wang, Z.; Chen, Y.; Gang, C.; An, R.; Li, J. Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region, China from 1982 to 2012. Sci. Total Environ. 2016, 563-564, 210–220.
  40. Penuelas, J.; Poulter, B.; Sardans, J.; Ciais, P.; Van Der Velde, M.; Bopp, L.; Janssens, I.A. Human-induced ni-trogen–phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 2013, 4, 1–10.
  41. Xiao, J.; Moody, A. Geographical distribution of global greening trends and their climatic correlates: 1982–1998. Int. J. Remote Sens. 2005, 26, 2371–2390.
  42. Forster, S.; Kuhlmann, B.; Lindenschmidt, K.E.; Bronstert, A. Assessing flood risk for a rural detention area. Nat. Hazards Earth Syst. Sci. 2008, 8, 311–322.
  43. Wang, Z.; Huang, Z.; Li, J.; Zhong, R.; Huang, W. Assessing impacts of meteorological drought on vegetation at catchment scale in China based on SPEI and NDVI. Trans. Chin. Soc. Agric. Eng. 2016, 32, 177–186.
  44. Adepoju, K.; Adelabu, S.; Fashae, O. Vegetation Response to Recent Trends in Climate and Landuse Dynamics in a Typical Humid and Dry Tropical Region under Global Change. Adv. Meteorol. 2019, 2019, 1–15.
  45. Peng, S.; Chen, A.; Xu, L.; Cao, C.; Fang, J.; Myneni, R.; Pinzon, J.E.; Tucker, C.J.; Piao, S. Recent change of vegetation growth trend in China. Environ. Res. Lett. 2011, 6, 044027.
  46. Hadramout and Al-Mahara. Damage, Losses and Needs Assessment October, Tropical Storm and Floods; Republic of Yemen, Government of the People’s Democratic Republic of Yemen, International Federation of Red Cross and Red Crescent Socie-ties (IFRC), World Bank, United Nations International Strategy for Disaster Reduction (UNISDR) 2009, 217, 1–187. Available online: (accessed on 6 October 2009).
  47. Gratani, L. Plant Phenotypic Plasticity in Response to Environmental Factors. Adv. Bot. 2014, 2014, 1–17.
  48. Tessema, Z.; de Boer, W.; Baars, R.; Prins, H. Changes in soil nutrients, vegetation structure and herbaceous biomass in response to grazing in a semi-arid savanna of Ethiopia. J. Arid. Environ. 2011, 75, 662–670.
  49. Eldridge, D.J.; Poore, A.G.; Ruiz-Colmenero, M.; Letnic, M.; Soliveres, S. Ecosystem structure, function, and composition in rangelands are negatively affected by livestock grazing. Ecol. Appl. 2016, 26, 1273–1283.
  50. IPCC. Climate Change 2014–Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects; Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge UK; New York, NY, USA, 2014; p. 688.
  51. Dube, T.; Moyo, P.; Ncube, M.; Nyathi, D. The Impact of Climate Change on Agro-Ecological Based Livelihoods in Africa: A Review. J. Sustain. Dev. 2016, 9, 256.
  52. Olsson, L.; Opondo, M.; Tschakert, P.; Agrawal, A.; Eriksen, S.; Ma, S. Livelihoods and Poverty: Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; pp. 793–832.
  53. Eckstein, D.; Künzel, V.; Schäfer, L.; Winges, M. Global Climate Risk Index 2020; Germanwatch: Bonn, Germany, 2019; Available online: (accessed on 12 December 2019).
  54. Handmer, J.; Honda, Y.; Kundzewicz, Z.W.; Arnell, N.; Benito, G.; Hatfield, J.; Mohamed, I.F.; Peduzzi, P.; Wu, S.; Sherstyukov, B.; et al. Changes in impacts of climate extremes: Human systems and ecosystems. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge UK; New York, NY, USA, 2012; pp. 231–290.
  55. Cramer, W.; Yohe, G.; Auffhammer, M.; Huggel, C. Detection and Attribution of Observed Impacts. In Climate Change 2014 Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects. Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge UK; New York, NY, USA, 2014; pp. 979–1037.
  56. Piya, L.; Maharjan, K.L.; Joshi, N.P. Socioeconomic Issues of Climate Change: A Livelihood Analysis from Nepal; Socioeconomic Issues of Climate Change; Springer: Singapore, 2019; pp. 153–160.
  57. Hulme, P.E. Addressing the threat to biodiversity from botanic gardens. Trends Ecol. Evol. 2011, 26, 168–174.
  58. McMichael, A.J.; Lindgren, E. Climate change: Present and future risks to health, and necessary responses. J. Intern. Med. 2011, 270, 401–413.
  59. Qian, W.; Zhu, Y. Climate Change in China from 1880 to 1998 and its Impact on the Environmental Condition. Clim. Chang. 2001, 50, 419–444.
  60. Kurukulasuriya, P.; Rosenthal, S. Climate Change and Agriculture: A Review of Impacts and Adaptations; 2013; pp. 3–106. Available online: (accessed on 6 June 2012).
  61. Debortoli, N.S.; Camarinha, P.I.M.; Marengo, J.A.; Rodrigues, R.R. An index of Brazil’s vulnerability to expected increases in natural flash flooding and landslide disasters in the context of climate change. Nat. Hazards 2017, 86, 557–582.
  62. Knowlton, J.L.; Graham, C.H. Using behavioural landscape ecology to predict species’ responses to land-use and climate change. Biol. Conserv. 2010, 143, 1342–1354.
  63. Bouwer, L.M. Observed and projected impacts from extreme weather events: Implications for loss and damage. In Loss and Damage from Climate Change; Springer: Cham, Switzerland, 2019; pp. 63–82.
  64. Marengo, J.A.; Espinoza, J.C. Extreme seasonal droughts and floods in Amazonia: Causes, trends and impacts. Int. J. Clim.. 2016, 36, 1033–1050.
  65. Wamsler, C.; Niven, L.; Beery, T.; Bramryd, T.; Ekelund, N.; Jönsson, K.I.; Osmani, A.; Palo, T. St Operationalizing ecosystem-based adaptation: Harnessing ecosystem services to buffer communities against climate change. Ecol. Soc. 2016, 21.
  66. Kittel, T.G. The Vulnerability of Biodiversity to Rapid Climate Change. Vulnerability of Ecosystems to Climate; Seastedt, T.R., Suding, K., Eds.; Elsevier Inc., Academic Press: Oxford, UK, 2013; pp. 185–201.
  67. Nath, P.K.; Behera, B. A critical review of impact of and adaptation to climate change in developed and developing economies. Environ. Dev. Sustain. 2011, 13, 141–162.
  68. Mugambiwa, S.S.; Tirivangasi, H.M. Climate change: A threat towards achieving Sustainable Development Goal number two’ (end hunger, achieve food security and improved nutrition and promote sustainable agriculture) in South Africa. J. Disaster Risk Stud. 2017, 9, 1–6.
  69. Sintayehu, D.W. Impact of climate change on biodiversity and associated key ecosystem services in Africa: A sys-tematic review. Ecosyst. Health Sustain. 2018, 4, 225–239.
  70. Wessels, K.J.; van den Bergh, F.; Scholes, R.J. Limits to detectability of land degradation by trend analysis of veg-etation index data. Remote Sens. Environ. 2012, 125, 10–22.
  71. Thornton, P.K.; Ericksen, P.J.; Herrero, M.; Challinor, A.J. Climate variability and vulnerability to climate change: A review. Glob. Chang. Biol. 2014, 20, 3313–3328.
  72. Mantyka-pringle, C.S.; Martin, T.G.; Rhodes, J.R. Interactions between climate and habitat loss effects on bio-diversity: A systematic review and meta-analysis. Glob. Chang. Biol. 2012, 18, 1239–1252.
  73. Chapin Iii, F.S.; Zavaleta, E.S.; Eviner, V.T.; Naylor, R.L.; Vitousek, P.M.; Reynolds, H.L.; Hooper, D.U.; Lavorel, S.; Sala, O.E.; Hobbie, S.E.; et al. Consequences of changing biodiversity. Nat. Cell Biol. 2000, 405, 234–242.
  74. Oliver, T.H.; Heard, M.S.; Isaac, N.J.; Roy, D.B.; Procter, D.; Eigenbrod, F.; Bullock, J.M. Biodiversity and re-silience of ecosystem functions. Trends Ecol. Evol. 2015, 30, 673–684.
  75. Seidl, R.; Thom, D.; Kautz, M.; Martin-Benito, D.; Peltoniemi, M.; Vacchiano, G.; Wild, J.; Ascoli, D.; Petr, M.; Honkaniemi, J.; et al. Forest disturbances under climate change. Nat. Clim. Chang. 2017, 7, 395–402.
  76. Cunha, A.P.M.A.; Alvalá, R.C.S.; Kubota, P.Y.; Vieira, R.M.S.P. Impacts of land use and land cover changes on the climate over Northeast Brazil. Atmos. Sci. Lett. 2015, 16, 219–227.
  77. De Groot, R.S.; Fisher, B.; Christie, M.; Aronson, J.; Braat, L.; Haines-Young, R.; Gowdy, J.; Maltby, E.; Neuville, A.; Polasky, S.; et al. Integrating the ecological and economic dimensions in biodiversity and ecosystem service valuation. In The Economics of Ecosystems and Biodiversity (TEEB): Ecological and Economic Foundations; Earthscan, Routledge: Oxfordshire, UK, 2010; pp. 9–40.
  78. Kehinde, T.; Amusan, B.; Ayansola, A.; Oyelade, S.; Adu, W. Status of insect diversity conservation in Nigeria: A review. J. Sci. 2014, 16, 319–330.
  79. Kittel, T.G.; Howard, S.G.; Horn, H.; Kittel, G.M.; Fairbarns, M.; Iachetti, P. A vulnerability-based strategy to incorporate climate change in regional conservation planning: Framework and case study for the British Columbia Central Interior. J. Ecosyst. Manag. 2011, 12, 7–35.
  80. Morelli, T.L.; Barrows, C.W.; Ramirez, A.R.; Cartwright, J.M.; Ackerly, D.D.; Eaves, T.D.; Ebersole, J.L.; Krawchuk, M.A.; Letcher, B.H.; Mahalovich, M.F.; et al. Climate-change refugia: Biodiversity in the slow lane. Front. Ecol. Environ. 2020, 18, 228–234.
  81. Coad, L.; Leverington, F.; Knights, K.; Geldmann, J.; Eassom, A.; Kapos, V.; Kingston, N.; de Lima, M.; Zamora, C.; Cuardros, I.; et al. Measuring impact of protected area management interventions: Current and future use of the Global Database of Protected Area Management Effectiveness. Philos. Trans. R. Soc. B Biol. Sci. 2015, 370, 20140281.
  82. Walz, Y.; Min, A.; Dall, K.; Duguru, M.; de Leon, J.-C.V.; Graw, V.; Dubovyk, O.; Sebesvari, Z.; Jordaan, A.; Post, J. Monitoring progress of the Sendai Framework using a geospatial model: The example of people affected by agricultural droughts in Eastern Cape, South Africa. Prog. Disaster Sci. 2020, 5, 100062.
  83. Skarbit, N.; Stewart, I.D.; Unger, J.; Gál, T. Employing an urban meteorological network to monitor air temperature conditions in the ‘local climate zones’ of Szeged, Hungary. Int. J. Clim. 2017, 37, 582–596.
  84. Pan, Y.; Birdsey, R.A.; Phillips, O.L.; Jackson, R.B. The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 2013, 44, 593–622.
  85. Loranty, M.M.; Abbott, B.W.; Blok, D.; Douglas, T.A.; Epstein, H.E.; Forbes, B.C.; Jones, B.M.; Kholodov, A.L.; Kropp, H.; Malhotra, A.; et al. Reviews and syntheses: Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 2018, 15, 5287–5313.
  86. Dong, S.X.; Davies, S.J.; Ashton, P.S.; Bunyavejchewin, S.; Supardi, M.N.; Kassim, A.R.; Moorcroft, P.R. Var-iability in solar radiation and temperature explains observed patterns and trends in tree growth rates across four tropical forests. Proc. R. Soc. B Biol. Sci. 2012, 279, 3923–3931.
  87. Zhou, J.; Zhang, F.; Xu, Y.; Gao, Y.; Zhao, X. Relationship Between Vegetation Coverage and Rural Settlements and Anti-desertification Strategies in Horqin Left Back Banner, Inner Mongolia, China. In International Conference on Computer and Computing Technologies in Agriculture; Springer: Cham, Switzerland, 2016; Volume 478, pp. 125–142.
  88. MacLean, I.M.D.; Suggitt, A.J.; Wilson, R.; Duffy, J.P.; Bennie, J.J. Fine-scale climate change: Modelling spatial variation in biologically meaningful rates of warming. Glob. Chang. Biol. 2016, 23, 256–268.
  89. Kumar, M.A.N.O.J. Analysing Impacts of Climate Change on Forests of Uttarakhand Using Dynamic Vegetation Model. Ph.D. Thesis, Forest Research Institute Deemed to be University, Dehradun, Uttarakhand, India, 2018.
  90. Wu, D.H.; Zhao, X.; Liang, S.L.; Zhou, T.; Huang, K.C.; Tang, B.J.; Zhao, W.Q. Time-lag effects of global vegetation responses to climate change. Glob. Chang. Biol. 2015, 21, 3520–3531.
  91. IPCC. Climate Change IPCC, WGI Fourth Assessment Report, Summary for Policy Makers; IPCC: Geneva, Switzerland, 2007; pp. 104–116.
  92. Hou, W.; Gao, J.; Wu, S.; Dai, E. Interannual variations in growing-season NDVI and its correlation with climate variables in the southwestern karst region of China. Remote Sens. 2015, 7, 11105–11124.
  93. Du, J.; Quan, Z.; Fang, S.; Liu, C.; Wu, J.; Fu, Q. Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform. Environ. Sci. Pollut. Res. 2020, 27, 1144–1159.
  94. Mahowald, N.; Albani, S.; Kok, J.; Engelstaeder, S.; Scanza, R.; Ward, D.S.; Flanner, M.G. The size distribution of desert dust aerosols and its impact on the Earth system. Aeolian Res. 2014, 15, 53–71.
  95. Baldi, G.; Texeira, M.; Murray, F.; Jobbágy, E.G. Vegetation Productivity in Natural vs. Cultivated Systems along Water Availability Gradients in the Dry Subtropics. PLoS ONE 2016, 11, e0168168.
  96. Buyantuyev, A.; Wu, J. Urbanization alters spatiotemporal patterns of ecosystem primary production: A case study of the Phoenix metropolitan region, USA. J. Arid. Environ. 2009, 73, 512–520.
  97. Li, Z.; Chen, Y.; Li, W.; Deng, H.; Fang, G. Potential impacts of climate change on vegetation dynamics in Central Asia. J. Geophys. Res. Atmos. 2015, 120, 12345–12356.
  98. Propastin, P.; Kappas, M.; Muratova, N.; Propastin, P.; Kappas, M.; Muratova, N. A remote sensing based monitoring system for discrimination between climate and human-induced vegetation change in Central Asia. Manag. Environ. Qual. Int. J. 2008, 19, 579–596.
  99. Wisner, B.; Blaikie, P.; Blaikie, P.M.; Cannon, T.; Davis, I. At Risk: Natural Hazards, People’s Vulnerability and Disasters; Psychology Press: London, UK, 2004.
  100. Forsius, M.; Anttila, S.; Arvola, L.; Bergström, I.; Hakola, H.; Heikkinen, H.I.; Keskinen, T. Impacts and adaptation options of climate change on ecosystem services in Finland: A model based study. Curr. Opin. Environ. Sustain. 2013, 5, 26–40.
  101. Piao, S.; Liu, Q.; Chen, A.; Janssens, I.A.; Fu, Y.; Dai, J.; Liu, L.; Lian, X.; Shen, M.; Zhu, X. Plant phenology and global climate change: Current progresses and challenges. Glob. Chang. Biol. 2019, 25, 1922–1940.
  102. Jamieson, M.A.; Trowbridge, A.; Raffa, K.F.; Lindroth, R.L. Consequences of Climate Warming and Altered Precipitation Patterns for Plant-Insect and Multitrophic Interactions. Plant Physiol. 2012, 160, 1719–1727.
  103. Lovejoy, S. Return periods of global climate fluctuations and the pause. Geophys. Res. Lett. 2014, 41, 4704–4710.
  104. Nyingi, W.; Oguge, N.; Dziba, L.; Chandipo, R.; Didier, T.A.; Gandiwa, E.; Von Maltitz, G.P. Direct and Indirect Drivers of Change in Biodiversity and Nature’s Contributions to People; The IPBES regional assessment report on biodiversity and ecosystem services for Africa; Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES): Bonn, Germany, 2018; pp. 207–296. Available online: (accessed on 18 March 2018).
  105. Zhao, P.; Yang, S.; Yu, R. Long-Term Changes in Rainfall over Eastern China and Large-Scale Atmospheric Circulation Associated with Recent Global Warming. J. Clim. 2010, 23, 1544–1562.
  106. Ali, S.; Henchiri, M.; Yao, F.; Zhang, J. Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011. Environ. Sci. Pollut. Res. 2019, 26, 11470–11481.
  107. Dyosi, M.; Kalumba, A.M.; Magagula, H.; Zhou, L.; Orimoloye, I.R. Drought conditions appraisal using geoinformatics and multi-influencing factors. Environ. Monit. Assess. 2021, 193, 1–19.
  108. Germer, S.; Kaiser, K.; Bens, O.; Hüttl, R.F. Water balance changes and responses of ecosystems and society in the Berlin-Brandenburg region–a review. J. Geogr. Soc. Berl. 2011, 142, 65–95.
  109. Pinzon, J.E.; Tucker, C.J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 2014, 6, 6929–6960.
  110. Nzabarinda, V.; Bao, A.; Xu, W.; Uwamahoro, S.; Jiang, L.; Duan, Y.; Nahayo, L.; Yu, T.; Wang, T.; Long, G. Assessment and Evaluation of the Response of Vegetation Dynamics to Climate Variability in Africa. Sustainability 2021, 13, 1234.
  111. Du, J.; Shu, J.; Yin, J.; Yuan, X.; Jiaerheng, A.; Xiong, S.; He, P.; Liu, W. Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China. Int. J. Appl. Earth Obs. Geoinf. 2015, 38, 216–228.
  112. Anazawa, M.; Saito, G.; Sawada, Y.; Sawada, H. In Proceedings of the Vegetation Monitoring Study using Leaf Water Content Index (LWCI) and NDVI. Presented at the 22nd Asian Conference on Remote Sensing. Singapore, 5–9 November 2001; Volume 5, p. 9.
  113. Liang, S.; Yi, Q.; Liu, J. Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecol. Indic. 2015, 58, 64–76.
  114. Peng, D.; Zhang, H.; Yu, L.; Wu, M.; Wang, F.; Huang, W.; Liu, L.; Sun, R.; Li, C.; Wang, D.; et al. Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy. Int. J. Remote Sens. 2018, 39, 8022–8040.
  115. Pei, F.; Wu, C.; Liu, X.; Li, X.; Yang, K.; Zhou, Y.; Xia, G. Monitoring the vegetation activity in China using veg-etation health indices. Agric. For. Meteorol. 2018, 248, 215–227.
  116. Tsiros, E.; Domenikiotis, C.; Spiliotopoulos, M.; Dalezios, N.R. Use of NOAA/AVHRR-based vegetation condition index (VCI) and temperature condition index (TCI) for drought monitoring in Thessaly, Greece. In Proceedings of the EWRA Symposium on Water Resources Management: Risks and Challenges for the 21st Century, Izmir, Turkey, 2–4 September 2004; pp. 2–4.
  117. Karnieli, A.; Bayasgalan, M.; Bayarjargal, Y.; Agam, N.; Khudulmur, S.; Tucker, C.J. Comments on the use of the Vegetation Health Index over Mongolia. Int. J. Remote Sens. 2006, 27, 2017–2024.
  118. Rhyma, P.P.; Norizah, K.; Hamdan, O.; Faridah-Hanum, I.; Zulfa, A.W. Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sens. Appl. Soc. Environ. 2020, 17, 100280.
  119. Iizuka, A. Developing capacity for disaster risk reduction: Lessons learned from a case of Sri Lanka. Prog. Disaster Sci. 2020, 6, 100073.
  120. Rivera, J.D.; Ceesay, A.A.; Sillah, A. Challenges to disaster risk management in The Gambia: A preliminary investigation of the disaster management system’s structure. Prog. Disaster Sci. 2020, 6, 100075.
  121. Popoola, O.O.; Monde, N.; Yusuf, S.F.G. Perception and Adaptation Responses to Climate Change: An Assessment of Smallholder Livestock Farmers in Amathole District Municipality, Eastern Cape Province. S. Afr. J. Agric. Ext. 2019, 47, 46–57.
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to :
View Times: 845
Revisions: 3 times (View History)
Update Date: 30 Sep 2021