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.
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. | [34] |
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. | [22] |
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. | [140] |
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. | [141] |
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. | [142] |
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. | [143] |
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. | [144] |
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. | [145] |
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. | [146] |
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) |
[32] | 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 | [124] | 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) |
[78] | 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) | [66] | 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 | [94] | 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) |
[81] | 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 |
2004–2017 (13 years) |
[147] | 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) |
[115] | 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 (1-year) |
[148] | 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 | [149] | Multistage sampling procedure, snowball sampling approach statistical program |
A cross-sectional household survey, Simple descriptive statistical tools |
This entry is adapted from the peer-reviewed paper 10.3390/su13137265