The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

Essaadia, Adaze; Algouti, Abdellah; Algouti, Ahmed; Abdelouahed, Farah; kamal, Elbadaoui

doi:10.1016/j.heliyon.2022.e12204

Cited by 13 publications

(7 citation statements)

References 40 publications

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“…To determine the vegetation condition of the study area, we determined the NDVI, a relative indicator of the amount of photosynthetic active biomass, which is used in modern research in the field of agriculture [1,6,17]. The state of vegetation was determined by remote methods based on the NDVI.…”

Section: Methodsmentioning

confidence: 99%

“…Over the last decade, researchers have utilized vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI), for large-scale monitoring of evergreen forests [12][13][14]. The NDVI, owing to its minimal dependence on soil properties and ease of calculation, has proven to be a preferred method for ecosystem monitoring [15][16][17].…”

Section: Current Techniques and Limitationsmentioning

confidence: 99%

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Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

Sagynbayeva,

Mambetov,

Kalachev

et al. 2023

IJDNE

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This study addresses the pressing and pertinent issue of establishing a space-based monitoring system to track forest condition changes. Utilizing geographic information system technologies and the Normalized Difference Vegetation Index (NDVI), it examines the evolution and dynamics of pine forest regeneration in the Semey Ormany State Forest Natural Reserve (Zhanasemey branch), Kazakhstan. Landsat-5 satellite data were employed to map and identify terrestrial vegetation changes within the Zhanasemey branch. Satellite imagery for June 2007 and September 2008 were specifically utilized for this investigation. Post-acquisition, the satellite images were subjected to corrections, followed by radiometric adjustments to minimize atmospheric interference. Subsequently, NDVI classifications, an established method for plant classification, were applied to each image. The study focused on a 15-year timespan (2008 to 2022) to monitor changes in vegetation intensity, classified according to NDVI, and to track the reforestation process in areas affected by fire. Analysis of the regeneration of the pine forest post the 2008 fire, conducted at five-year intervals, revealed an increase in the restored forest area from 6,773.49 ha in 2013 to 10,721.07 ha in 2018, and finally to 14,742.54 ha in 2022. In 2008, the fire-impacted area with reduced vegetation spanned 7,015.41 ha, while by 2022, the restored area encompassed 14,742.54 ha, indicating a significant enhancement in the pine forest restoration process. The findings underscore the critical role of remote sensing in forest fire modelling and identifying areas at risk, suggesting its potential utility in large-scale monitoring and management efforts. The study demonstrates the feasibility and efficiency of using space-based technologies to monitor long-term changes in forest conditions and to track reforestation dynamics in fire-affected regions.

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Section: Methodsmentioning

confidence: 99%

Section: Current Techniques and Limitationsmentioning

confidence: 99%

Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

Sagynbayeva,

Mambetov,

Kalachev

et al. 2023

IJDNE

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“…The satellite image dated October 26, 2021 shows accelerated process of moisture accumulation in plants on Plot 1 and in the north-eastern part of Plot 3, that is determined by the relief of the plot. Due to an insufficient level of moisture content that determines extreme conditions of agriculture in the Steppe zone, the macro-stage of autumn tillering had lasted in the experimental field by the time of formation of the fourth tiller (BBCH [20][21][22][23][24]. During this period, on Plot 1, there was a relatively high level of water balance in winter wheat crops, the NDWI values over the period of tillering increased from -0.28 to -0.08; the maximum value at the end of the macro-stage of autumn tillering equaled 0.11, autumn vegetation ceased that was confirmed by the NDVI values and plants started going dormant.…”

Section: Examination Of Vegetative Formation Of Water Balance In Wint...mentioning

confidence: 99%

The Impact of Pre-Crops on the Formation of Water Balance in Winter Wheat Agrocenosis and Soil Moisture in the Steppe Zone

Pichura,

Potravka,

Domaratskiy

et al. 2024

J. Ecol. Eng.

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Under climate change, the issue of selection and correction of crop cultivation systems in the zone of moisture deficit and risky farming to ensure profitability of production is still topical. In particular, crop rotations are a practice aimed at increasing resistance of soil systems to abiotic and biotic stresses in the zone of moisture deficit. Therefore, the purpose of the research is to identify spatio-temporal regularities of vegetative formation of water balance in winter wheat agrocenoses depending on a pre-crop according to the unified BBCH scale. Spatio-temporal processes of vegetation and water balance formation in winter wheat agrocenosis depending on a pre-crop according to the unified BBCH scale were examined on the basis of the data of decoded satellite image series of the spacecraft Sentinel and calculation of the NDWI and the NDVI values. The research was conducted in the natural-climatic conditions of the Steppe zone of Ukraine, in the territory of Yelanets district, Mykolaiv region, during the vegetative phase of winter wheat variety Driada 1: autumn 2021 and winter, spring and the beginning of summer 2022. It was established that activeness of water balance formation in winter wheat agrocenosis with pea as a pre-crop according to seasonal-phenological stages of plant growth is 3.0-9.0 times higher than with a grain crop (spring barley) and sunflower as pre-crops. In particular, with pea as a pre-crop, the NDVI vegetation of winter wheat plants is 1.6-1.7 times more intensive, the rate of moisture supply NDWI in the plant leaf at the macro-stages BBCH 10-61 is 1.54 and 1.82 times higher, productivity is 1.43-1.56 times higher. We observed a 30.5-34.3% reduction in water consumption for the formation of a ton of winter wheat grain with pea as a pre-crop in comparison with other pre-crops that resulted in an increase in productive moisture reserves at the end of vegetation in a meter soil layer by 20%. It was established that using pea as a pre-crop has economic and environmental benefits that manifest themselves in increasing resistance of soil systems, a reduction in environmental pollution and a rise in profitability of production.

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“…One of the promising areas of agricultural science research is the use of the remote sensing data to study the state of the crops based on the normalized difference vegetation index (NDVI) (Ding et al, 2022;Essaadia et al, 2022). Spatiotemporal differentiation of the agrocenoses vegetation index indicates the state of plant development in different phenological stages depending on the natural and climatic conditions, the scope and kind of precipitation, the degree of agrotechnical measures.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal patterns and vegetation forecasting of sunflower hybrids in soil and climatic conditions of the Ukrainian Steppe zone

Pichura,

Potravka,

Domaratskiy

et al. 2023

UBSRAS

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Long-term studies of tillage and crop management are essential in finding out which crop production practices would contribute to sustainable yields and profits. In the conditions of climate change, such issues as selection, forecasting and adjustment of crop cultivation systems in the zone of moisture deficit and agricultural risk management are especially relevant. Therefore, the aim of the study was to establish spatiotemporal patterns of vegetative development of sunflower hybrids and predict their productivity in the soil and climatic conditions of the Ukrainian Steppe. A detailed analysis of seasonal changes in the values of the normalized difference vegetation index in sunflower hybrid crops during the 2019-2021 time period was carried out with the help of space images from the Sentinel 2 satellite device, and then processed with the ArcGis 10.6 licensed software product. The credibility of the achieved results of the condition of crops in different phases of plant vegetation on the basis of NDVI and the possibility of their use for forecasting the yield of agricultural crops have been proven. The adjustment capabilities of various sunflower hybrids to the STeppe soil and climate conditions were determined, particularly in regards of such hybrids as Oplot, Hektor, DSL403, P64GE133, 8X477KL. A model of the yield forecasting function for each sunflower hybrid was developed according to the annual level of moisture supply. The level of data approximation of the forecasting models was 97.2-99.9%. It is suggested to use system functional models developed specifically for different moisture supply and plant nutrition conditions in order to forecast of the yield of sunflower hybrids according to a particular situation. The results can be used to improve the methodology of researching the vegetation of agricultural crops, to validate crop rotation, to choose the best practical ways for the use of multifunctional growth-regulating substances, to define the climatic adjustment of cultivars and hybrids, to manage resources, to develop adaptive climate technologies in agriculture and crop production, to calculate their efficiency, to forecast the yield and to ensure the profitability of agricultural production in the moisture deficit zone and managing a high-risk farming

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The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

Cited by 13 publications

References 40 publications

Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies

The Impact of Pre-Crops on the Formation of Water Balance in Winter Wheat Agrocenosis and Soil Moisture in the Steppe Zone

Spatiotemporal patterns and vegetation forecasting of sunflower hybrids in soil and climatic conditions of the Ukrainian Steppe zone

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