Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Olmos-Trujillo, Edith; Gonzalez-Trinidad, Julián; Júnez-Ferreira, Hugo Enrique; Pacheco-Guerrero, Anuard; Bautista-Capetillo, Carlos; Avila-Sandoval, Claudia; Galván-Tejada, Eric

doi:10.3390/su12051939

Cited by 34 publications

(16 citation statements)

References 55 publications

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“…Multiple works have demonstrated transparent relationships between climate and VIs response [ 8 , 76 ]. Our results agree with [ 9 ], who showed a negative relationship between temperature and NDVI.…”

Section: Discussionmentioning

confidence: 99%

The Vegetation–Climate System Complexity through Recurrence Analysis

Almeida-Ñauñay

Benito

Quemada

et al. 2021

Entropy

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Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity.

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Section: Discussionmentioning

confidence: 99%

The Vegetation–Climate System Complexity through Recurrence Analysis

Almeida-Ñauñay

Benito

Quemada

et al. 2021

Entropy

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“…The vegetation indices were derived from different spectral bands of satellite data and the equations are presented in Table 1 as well as Figure 4,b respectively. Normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized difference moisture index (NDMI) are commonly employed in assessing the vegetation status and the soil conditions (Olmos‐Trujillo et al, 2020) were derived. NDVI is the most commonly used index and uses the reflectance of near‐infrared band and red band to represent characters of active vegetation (Rodríguez‐Moreno & Bullock, 2013).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

Thakur

Dutta²,

Bijalwan

et al. 2022

Land Degrad Dev

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The present study attempts to understand land use land cover change dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur District in Madhya Pradesh, India through geospatial techniques. Land use and land cover (LULC) change detection analysis was performed digitally classifying LANDSAT-5 (2001) as well as LANDSAT-8 (2020) satellite data using a maximum likelihood algorithm. Results revealed that area under dense native vegetation decreased drastically (by 13.74 km 2 ) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 km 2 ). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico-chemical parameters. Studies suggested that soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty-year time-scale indicates that the reclamation activities are still in its infancy.A land degradation vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60% of the area is highly vulnerable to degradation. The land degradation index (LDI) inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001-2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.coal mining, decadal changes, heat plot, land degradation vulnerability index, land use land cover | INTRODUCTIONThe Anthropocene is a time of great risk for humanity due to emerging as well as rapidly evolving environmental consequences (Crutzen, 2002;Kareiva, 2014;Keys et al., 2019). Global climate change, degradation of natural resources, biodiversity loss, deterioration of ecosystem services pose huge challenge for all species on

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“…The work from Barati et al 39 presents the accuracy of different vegetation indices for estimation of vegetation cover including SAVI and MSAVI. Olmos-Trujillo et al 40 used vegetation indices (both SAVI and MSAVI) to perform a geostatistical analysis of rainfall and temperature.…”

Section: Data Preprocessingmentioning

confidence: 99%

AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

Zulfiqar

Ghaffar

Shahzad

et al. 2021

J. Appl. Rem. Sens.

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Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand-crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In contrast to traditional methods, we present AI-ForestWatch, an end to end framework for forest estimation and change analysis. The proposed approach uses deep convolution neural network-based semantic segmentation to process multi-spectral spaceborne images to quantitatively monitor the forest cover change patterns by automatically extracting features from the dataset. Our analysis is completely data driven and has been performed using extended (with vegetation indices) Landsat-8 multi-spectral imagery from 2014 to 2020. As a case study, we estimated the forest area in 15 districts of Pakistan and generated forest change maps from 2014 to 2020, where major afforestation activity is carried out during this period. Our critical analysis shows an improvement of forest cover in 14 out of 15 districts. The AI-ForestWatch framework along with the associated dataset will be made public upon publication so that it can be adapted by other countries or regions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Cited by 34 publications

References 55 publications

The Vegetation–Climate System Complexity through Recurrence Analysis

The Vegetation–Climate System Complexity through Recurrence Analysis

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

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