Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Sanz, Ernesto; Saá-Requejo, Antonio; Díaz‐Ambrona, Carlos Gregorio Hernández; Ruiz‐Ramos, Margarita; Rodríguez, Alfredo; Martínez, Eva Iglesias; Esteve, Paloma; Soriano, Bárbara; Tarquis, Ana M.

doi:10.3390/rs13050840

Cited by 22 publications

(16 citation statements)

References 46 publications

(58 reference statements)

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“…Our results reach the same conclusion, VCI and VHI showed greater values during the autumn and spring transition phases and achieved low stable values during winter and summer, when temperature and precipitation are limiting. Similar trends for MODIS NDVI were reported by [27,70], suggesting that NDVI is restricted, as well as grassland vegetation, during the summer and winter seasons.…”

Section: Discussionsupporting

confidence: 86%

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Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

et al. 2022

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Drought is a significant challenge to semiarid Mediterranean grasslands, Increasing the accuracy of monitoring allows improving the conservation and management of these vital ecosystems. Meteorological drought is commonly monitored by the Standard Precipitation Index (SPI) or the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agriculture drought is estimated by the Vegetation Health Index (VHI). This work aims to optimise the correlation between both drought types using the best transformation of VHI and the most appropriate time scale. Two drought-vulnerable Mediterranean grasslands were selected to evaluate the performance of the drought indexes. The SPI and the SPEI were calculated using data obtained from nearby weather stations. MODIS data were used to calculate the VHI. This index was standardised, naming it as SVHI. Our results revealed that SPEI was better correlated with VHI compared to SPI. In addition, SVHI obtained better results in the critical vegetation phases than VHI. Overall, SPEI and SVHI were the best correlated indexes. The quarterly scale showed stronger relationships than the monthly scale and the most correlated time frame were Mediterranean spring and autumn. This fact suggests that SPEI and SVHI could provide a plus point for increasing the precision of vegetation monitoring during these periods.

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Section: Discussionsupporting

confidence: 86%

“…Artificial intelligence has been used to measure drought episodes [25,26]. Even more, Sanz et al (2021) [27] related a cumulative AI with cumulative NDVI to characterize the efficient use of water resources in shorter periods than a year.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

et al. 2022

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“…In contrast to the present study, reports witnessed that NDVI change (either increasing or decreasing) is driven by precipitation/temperature in arid and semi-arid areas of Africa [128,129] and in other countries (i.e Spain, Iraq and China) of the world [94,[130][131][132][133]. NDVI controls the growth of vegetation conditions, temporal biomass accumulation, and changes [134].…”

Section: Trends Of Temporal and Seasonal Ndvicontrasting

confidence: 96%

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

Temesgen

Warkineh

Hailemicael

2021

Preprint

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Kafta-sheraro national park (KSNP) is one of the homes of the African elephant has experienced extensive destruction of woodland following regular land use & land cover change in the past three decades, however, up to date, data and documentation detailing for these changes are not addressed. This study aims to evaluate the land use land cover change and drivers of change that occurred between 1988 and 2018. Landsat 5(TM), Landsat7 (ETM+), and Landsat 8 (OLI/TIRs) imagery sensors, field observation, and socio-economic survey data were used. The temporal and spatial Normalized difference vegetation index (NDVI) was calculated and tested the correlation between NDVI and precipitation/temperature. The study computed a kappa coefficient of the dry season (0.90) and wet season (0.845). Continuous decline of woodland (29.38%) and riparian vegetation (47.11%) whereas an increasing trend of shrub-bushland (35.28%), grassland (43.47%), bareland (27.52%), and cultivated land (118.36 km2) were showed over thirty years. More results showed bare land was expanded from wet to drier months, while, cultivated land and grazing land increased from dry to wet months. Based on the NDVI result high-moderate vegetation was decreased by 21.47% while sparse & non-vegetation was expanded by 19.8% & 1.7% (36.5 km2) respectively. Settlement & agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected the park resources. Around KSNP, the local community livelihood depends on farming, expansion of agricultural land is the main driver for woodland dynamics/depletion and this leads to increase resources competition and challenges for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity via encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

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“…In addition, long time series NDVI data directly reflects the vegetation growth status and has shown robust results to identify damaged vegetation [1,10]; however, the saturation property of NDVI posed a known weakness which limited the application of vegetation index in dense vegetation areas, and some scholars did not improve the sensitivity of NDVI conversion to vegetation components greater than 0.6 [40,41]; therefore, other scholars have proposed a remote sensing estimation method based on the combination of vegetation index and mixed pixel decomposition [3]. The pixel dichotomy model was widely selected to establish the transformation relationship between vegetation coverage and NDVI, because the vegetation coverage can not only indicate vegetation growth but also can describe surface vegetation in dense vegetation areas, so it can be used as an indicator for monitoring surface vegetation in the mining areas [42,43].…”

Section: Discussionmentioning

confidence: 99%

“…It was found that the dynamic trend of long time series data and regression analysis can describe the potential status of vegetation degradation more accurately [6,7]. MODIS NDVI and meteorological variables were selected to analyze the characteristics of seasonal or annual vegetation in different regions, and the trend analysis method was chosen to study the robustness 2 of 14 of regional vegetation change [1,[8][9][10]. Meanwhile, combined with higher resolution Landsat NDVI and the grey forecasting model can calculate and predict vegetation coverage better [11,12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

Ding

Zhao

2022

IJGI

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It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne SAR and Landsat OLI images in the Xishan Coalfield. The relative change rate, coefficient of variation, and trend analysis methods were used to compare the vegetation growth trends in the subsidence center, subsidence edge, and non-subsidence zones; and the vegetation coverage was predicted by the pixel dichotomy and grey model from 2021 to 2025. The results indicated that the proportions of vegetation with high fluctuation and serious degradation were 6.60% and 5.64% in the subsidence center, and its NDVI values were about 10% lower than that in the subsidence edge and non-subsidence zones. In addition, vegetation coverage showed a wedge ascending trend from 2013 to 2020, and the prediction values of vegetation coverage obtained by GM (1,1) model also revealed this trend. The residuals of the predicted values were 0.047, 0.047, and 0.019 compared with the vegetation coverage in 2021, and the vegetation coverage was the lowest in the subsidence center, which was consistent with the law obtained by using NDVI. Research suggested that ground subsidence caused by mining activities had a certain impact on the surface vegetation in the mining areas; the closer to the subsidence center, the greater the fluctuation of NDVI, and the stronger the vegetation degradation trend; conversely, the smaller the fluctuation, and the more stable the vegetation growth.

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Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Cited by 22 publications

References 46 publications

Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

Assessment of Drought Indexes on Different Time Scales: A Case in Semiarid Mediterranean Grasslands

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

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