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. 2022 Dec 10;8(12):e12204. doi: 10.1016/j.heliyon.2022.e12204

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

Adaze Essaadia 1,∗, Algouti Abdellah 1, Algouti Ahmed 1, Farah Abdelouahed 1, Elbadaoui Kamal 1
PMCID: PMC9758424  PMID: 36536903

Abstract

This study presents vegetation land cover mapping based on Remote Sensing (RS) data processing, including satellite imagery scenes from Sentinel-2A, ASTER, and Landsat, in order to show the spatio-temporal correlation between the climate and environmental setting in Morocco. The main objective of this study is to contribute on land use planning and ecosystem protection. The distribution of vegetation in the study area was analyzed based on the satellite image processing and computing Normalized Difference Vegetation Index (NDVI). The study was conducted on different satellite imagery (Sentinel-2A, ASTER and the Landsat TM5, ETM+, and OLI) using SAGA GIS and SNAP software, to achieve two specific objectives: (1) conducting a comparative analysis of the NDVI calculation results from three satellite images (i.e., sentinel-2A, ASTER, Landsat 8); (2) highlighting the dynamics of the NDVI index from 1984 to 2020 by processing 35 satellite images from Landsat data Archive using SAGA GIS software, and then correlating these changes with other vegetation indices for the Marrakech-Haouz region provided by NOAA, namely: Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and NDVI indices. This study shows a repetitive fluctuation in the decrease of NDVI mean value from 1984 to 2020, due to the contrasting climate setting of the study area and the influence of the alternation of humid and dry periods in the High Atlas of Marrakech. In addition, the presented results underline that open-source software, such as SAGA GIS and SNAP can provide satisfactory results for vegetation coverage dynamics.

Keywords: High Atlas, Marrakech, NDVI, SAGA GIS, SNAP, Satellite imagery

High Atlas; Marrakech; NDVI; SAGA GIS; SNAP; Satellite imagery.

1. Introduction

Vegetation cover plays a major role in the ecosystem's balance. Vegetation provides soil protection and erosion control. It reduces the action of falling rain-drops, increases the capacity of soil infiltration, and reduces the speed of surface runoff (Maimouni et al., 2021; Vásquez-Méndez et al., 2010). Vegetation also conserves biodiversity and protects catchments against natural hazards, such as landslides and floods (Mohajane et al., 2017). Therefore, preserving vegetation cover is of great importance, especially in semi-arid and arid regions (Maimouni et al., 2021).

Morocco faces many climate change problems and extreme droughts (Maimouni et al., 2012). According to Morocco's National Directorate for Meteorology, average temperatures are expected to rise between 2 and 5 °C by the end of this century, while rainfall is predicted to decline by 20–30%. These climate conditions limit the growth of vegetation cover, especially in the High Atlas Mountains of Morocco (Maimouni et al., 2021).

The spatio-temporal dynamics of vegetation cover is largely driven by climate factors, such as climate change and human activities, such as deforestation/reforestation, and topographic parameters such as elevation and slope, according to many studies that used the Normalized Difference vegetation index (NDVI) as a remote sensing tool, for monitoring vegetation dynamics (Adole et al., 2016; Kogo et al., 2019; Chu et al., 2019; Lin et al., 2020; Matas-Granados et al., 2022; Prăvălie et al., 2022; Zhang et al., 2018; Duarte et al., 2018; Huang et al., 2021). These researchers and many others (Höpfner and Scherer, 2011; Kalisa et al., 2019; Nse et al., 2020; Nejadrekabi et al., 2022) have studied the response of vegetation coverage to climate factors at different spatial and temporal scales based on NDVI time series, and the results show a high dependence of vegetation dynamics on precipitation (Ezaidi et al., 2022), especially in arid and semi-arid regions (Hou et al., 2013; Maimouni et al., 2021).

Human activities also have an important effect on NDVI (Wang, 2016), they can decrease or increase NDVI depending on human interaction. Irrational human farming, excessive grazing, deforestation, and urbanization reduce vegetation coverage (Gao et al., 2022). On the other hand, carefully planned human activities, such as afforestation and reforestation, can play a favorable role in protecting and improving vegetation coverage. In addition to climate change and human activities, Maimouni et al. (2021) found that topographic factors influence vegetation cover distribution in the High Atlas. The low-slope and low-altitude terrains are useable by the local population for agricultural practices. However, the higher altitude areas with forest cover are inaccessible to the population. Therefore, the forest cover is more preserved from deforestation and human activities (Maimouni et al., 2021; Zhan et al., 2012; Jin et al., 2009; Hammi et al., 2007).

The NDVI has certain advantages over other vegetation indices, since it is less dependent on soil properties (Lemenkova, 2015), and can be simply calculated from canopy reflectance in infrared and near-infrared bands (Rouse et al., 1974; Carlson and Ripley, 1997). Thus, NDVI represents the most widely used in the literature for ecosystem monitoring. On the one hand, NDVI can reflect the changing distribution and characteristics of regional vegetation, and on the other hand, it provides valuable information for researchers and decisions makers in land planning and protecting ecosystems.

In this context, our main purpose, based on Earth Observation data processing, is to identify the vegetation dynamics in the Zat valley and to highlight the climate change impact on the vegetation dynamics. The specific objectives of this study are: (1) to demonstrate the potential of SAGA GIS and SNAP open-source software in the calculation and mapping of the NDVI. (2) To show the spatiotemporal dynamics of the NDVI index from 1984 to 2020 by processing Landsat TM, ETM+, and OLI images.

2. Materials and methods

2.1. Characteristics of the study area

The Zat drainage basin is located on the northern slope of the High Atlas of Marrakech, it is a part of the hydraulic system of the large watershed of Tensift. The Zat river originates in the high altitudes south of Marrakech and then crosses the Haouz plain to end in the Tensift River (Figure 1).

Figure 1.

Figure 1

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Location of the study area.

The upstream part of the Zat river basin drains the Precambrian basement of the High Atlas, which is consisted of crystalline rocks, while the downstream part flows over the geological terrains belonging to the Meso-Cenozoic cover (Hadach et al., 2015, 2017).

Rainfall increases from north to south of the Atlas chain according to the altitudinal gradient (Amaya et al., 2014). Indeed, the average annual rainfall is about 200 mm on the plain and more than 800 mm on the high peaks. This rainfall is irregular from one year to another and is generally accompanied by violent thunderstorms in summer and autumn (Ait Mlouk et al., 2018). The Zat basin is influenced by an arid and semi-arid climate, thus, the wettest months are usually at the end of winter (February and March). The average monthly temperatures vary between 13 and 28 °C on the plain and between 2 and 18 °C in the high mountains, with July and August being the hottest months and January and February the coldest (Ait Mlouk et al., 2016). According to the Tensift Hydraulic Basin Agency (ABHT), the rainfall trend in the Zat watershed over 34 years shows that the annual rainfall is highly heterogeneous, with a maximum of 497.2 mm of rainfall and a minimum of 133.4 mm (Figure 2). On the ridges of high altitudes, cold and snow prevail in winter and spring, but the valleys often show a warmer and drier conditions. This topoclimatic contrast is also reflected in the composition and distribution of the vegetation cover, such that a large part of the basin is dominated by forest located in the mountainous area, and the low altitude area is used by the population for agricultural practices.

Figure 2.

Figure 2

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Average annual precipitation and temperature of the Zat valley (1983–2017) (ABHT).

The diversity and complexity of the distribution of plant formations within our study area are essentially controlled by natural factors. Although the distribution of many plant formations is mostly related to climatic factors, the role of the geological substratum is not negligible. Indeed, soil lithology favors the distribution of forests on the Triassic silty-sandstone formations. In contrast, the bedrock crystalline basement plant cover is very rare with only a few fragmentary distributed trees and thorny and herbaceous plants.

2.2. Data and methodology

The methodology adopted in this study is illustrated in Figure 3. To carry out the comparative analysis of the NDVI, three scenes from three different satellites (Landsat 8, Sentinel-2, and ASTER) were used. For a relevant comparison, these images should be taken under the same observation conditions (time, % cloud cover, sunshine...). The images, generated from the three sensors in June 2017 (Table 1), have the optimal conditions of application, namely identical sensing time, and the cloud cover is <7%.

Figure 3.

Figure 3

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Methodological procedure for calculating the NDVI index using SAGA GIS and SNAP.

Table 1.

Characteristics of the three used scenes for the comparative analysis.

Scene 1: Landsat 8

  • Name: LC08_L1TP_202038_20170601_20170615_01_T1

  • Product type: LANDSAT_8_OLI_TIRS_L1TP

  • Sensing time: 01-JUN-2017
Scene 2: Sentinel 2A

  • Name: S2A_MSIL2A_20170601T110651_N0205_R137_T29RPQ _20170601T111225

  • Product type: S2_MSI_Level-2Ap

  • Sensing time: 01-JUN-2017
Scene 3: ASTER

  • Granule ID: ASTB170508112147

  • Satellite Sensors: ASTER

  • Observation Period: 08-JUN-2017
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These images were processed using two open-source software: SAGA GIS (System for Automated Geoscientific Analyses) (Conrad et al., 2015) and SNAP (Sentinel Application Platform). The first presents multitasking algorithms (remote sensing, geomorphology, terrain analysis, hydrogeology), and the second is designed for remote sensing, processing, and analysis of satellite images.

The selected Landsat data, obtained from the USGS data server was used to demonstrate the dynamics of the NDVI index (Zhu, 2017) from 1984 to 2020. This data includes a total of 35 images from Landsat 5 (TM5), Landsat 7 (ETM+), and Landsat 8 (OLI). These Landsat images were processed exclusively with SAGA GIS.

The NDVI is obtained with the formula NDVI=(NIR–R)/(NIR+R) (Rouse et al., 1974). This formula involves the red and near-red bands of each scene. The numbers of these spectral reflectances are different for different satellites, but the wavelengths are almost identical for the red and near-red bands, respectively.

The methodological workflow of NDVI mapping consists of several working steps. These include, for instance, atmosphere image correction using the “Top of Atmosphere reflectance” algorithm, and clipping the study area. Atmosphere image correction is necessary for the Landsat TM scene while can be omitted for sentinel-2A and ASTER.

SAGA GIS enables the conversion of the reflectance of Landsat images by removing disturbances due to the effects of solar radiation. This procedure is simplified by the use of the metadata file (MTL) provided with Landsat scene downloads. The Landsat ETM + images collected after 2003 have an SLC-OFF problem. As a result, they have data gaps, and therefore corrections to remove the “bad values” are required. These corrections have been performed using the «Close Gaps with Spline » algorithm (Figure 3).

The ASTER images are not superimposable on other imagery since they are georeferenced in the WGS84 Geographic Longitudes/Latitudes angular system. Their coordinates have been transformed into the UTM WGS84 coordinate systems.

After all these pre-processing operations, the clipping study area from all the images is carried out by cutting out the polygon representing the region of interest.

With SNAP software, atmospheric correction of the Landsat 8 scenes is performed by running the ICOR L-8 tool (De Keukelaere et al., 2018) (Figure 3).

High-resolution optical images of Sentinel-2 are provided on several levels, including level 2A, which requires no correction. The ASTER's optical sensors are not recognized by the SNAP, so the bands making up an ASTER scene are imported separately. It is therefore necessary to make an assembly (Collocation) of the tapes and then a reprojection to the system (UTM, WGS84, Zone 29N). Selecting the study area has been performed using masking (Land/Sea Mask) from the vector shapefile of the study area.

3. Results and discussions

The NDVI is sensitive to vegetation density and plant chlorophyll activity (Alhumaima and Abdullaev, 2020). It has been calculated using the two spectral reflectances at wavelengths of 655–665 and 835–865 nm. The NDVI map (Figure 4) shows the density of vegetation cover over the median area of the study region and in the alluvial valleys. High values of the NDVI index indicate forests, agriculture, and polycultures (orchards), while low values indicate bare soil or fields without crops (Mohajane et al., 2018).

Figure 4.

Figure 4

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NDVI map of the study area based on Sentinel-2 (June 2017). Values less than 0.2 represent bare soil (Brown color) and values greater than 0.2 reflect vegetative coverage (Green color).

NDVI mapping is based on using satellite optical sensors and software approaches. The comparison of the obtained results (Table 2) reflects a relative similarity for the means, minimum, maximum, and standard deviations of all sensors, except those of the uncorrected Landsat and ASTER images. Thus, the NDVI computations should be based on the pre-processed data (Zhu, 2017). Indeed, atmospheric effects can significantly alter the processing performed (Myneni and Asrar, 1994; Che and Price, 1992). As for the SAGA GIS and SNAP approaches, the results present identical values except for the non-preprocessed Landsat-8 images.

Table 2.

NDVI results obtained using SAGA GIS and SNAP.

Satellite
 L8
 L8_Cor
 S2
 ASTER
Program SAGA CPAWS SAGA CPAWS SAGA CPAWS SAGA CPAWS
Minimum –0.04 –0.30 –0.06 –0.07 –0.29 –0.34 0.07 –0.13
Maximum 0.50 0.54 0.69 0.75 0.77 0.78 0.79 0.79
Mean 0.15 –0.02 0.23 0.25 0.21 0.21 0.33 0.31
StdDev 0.06 0.10 0.09 0.11 0.11 0.11 0.11 0.11
Median 0.13 –0.05 0.20 0.22 0.17 0.17 0.30 0.27
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Classifications of NDVI were made by choosing well-defined class intervals with a specific color for each class. The comparison between the different NDVI results is easier and more relevant. Thus, the comparison of the statistical histograms on the frequency of data distribution shown in Figure 5–d supports this observation with a class distribution shifted towards low values for uncorrected Landsat-8. The ASTER scene demonstrates the opposite results, with the data distribution peak in the highest values. The graphical representations of the Sentinel-2 and corrected Landsat-8 are similar except for a small deviation of the modal classes representative of the two histograms. This similarity is well visualized in the scatter plots shown in Figure 6a–d). Indeed, the R2 correlation coefficient is equal to 95.31% with variables from Sentinel-2 and Landsat-8 corrected values (Figure 6a).

Figure 5.

Figure 5

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Frequency histograms of the NDVI using SAGA GIS. (a) Landsat 8, (b) Landsat 8_corrected, (c) ASTER, (d) Sentinel 2.

Figure 6.

Figure 6

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Scatter plots of NDVI indices and correlations between: (a) Landsat 8_corrected Vs Sentinel 2, (b) Landsat 8 Vs Sentinel 2, (c) Landsat 8_corrected Vs ASTER, (d) ASTER Vs Sentinel 2.

The frequency histograms generated by SNAP (Figure 7a–d) are comparable to those of the SAGA GIS except that the uncorrected Landsat graphical representation reveals an abnormal dominance of the negative values. The histograms of the Landsat 8 (Figure 7c) and Sentinel-2A scenes (Figure 7d) demonstrated almost identical shapes after the atmospheric correction performed by the ICOR-L8 tool.

Figure 7.

Figure 7

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Frequency histograms of the NDVI obtained using SNAP. (a) ASTER, (b) Landsat 8, (c) Sentinel 2, (d) Landsat 8_corrected.

The second objective of this study was to demonstrate the NDVI variations during the period ranging from 1984 to 2020. Sentinel-2 images are only available from 2015 onwards, therefore, we decided to use the Landsat TM, ETM+, and OLI images (Roy et al., 2014). The ICOR-L8 tool integrated in the SNAP software can only perform atmospheric correction on the Landsat 8 imagery, so the SAGA GIS was used to calculate the NDVI indices.

According to the obtained results, the minimum values of the NDVI are negative and therefore reflect, mainly, the presence of snow on the high peaks (Modica et al., 2016), which is normal during April in the case of the High Atlas summits. The maximum values are very high and exceed 0,7. This can only be explained by the density of vegetation in the forests and crops. The average values vary between 0.20 and 0.33, which is representative of the continuous forest canopy (USGS, 2018). The variations reflect occasional droughts that mainly affect grass crops, annual plants, and pastures in the High Atlas mountains or the Haouz plain (Figure 8a and b).

Figure 8.

Figure 8

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NDVI maps (a: the year of 1988, with developed vegetation cover; b: the drier year of 2002, with limited vegetation cover representing forests and alluvial valleys).

The repetitive fluctuations in the decrease of the mean NDVI values and their significance from a drought perspective are shown in Figure 9.

Figure 9.

Figure 9

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Variation of the mean NDVI values from 1984 to 2020.

There are several indices including the Temperature Condition Index (TCI) and the Vegetation Condition Index (VCI) for drought detection and monitoring (Kogan, 1995). The TCI is used for the determination of thermal stress (Quiring and Ganesh, 2010; Kogan et al., 2003). TCI values higher than 60 define the conditions favorable for a healthy vegetation cover while values below 40 indicate unfavorable conditions. The VCI can detect the health of vegetation coverage (Kogan, 1995). Values of VCI above 60 indicate very suitable conditions and values below 40 indicate conditions unfavorable for vegetation health.

To evaluate changes in the NDVI of the study area, a comparison was made with data provided by the National Oceanic and Atmospheric Administration (NOAA-AVHRR) (Rahimzadeh Bajgiran et al., 2008) concerning the NDVI, TCI, and VCI of the Marrakech-Haouz region. The comparative analysis demonstrated changes in drought over the study area. For instance, years with higher precipitation levels created favorable climate settings for the distribution of vegetation. Other years indicated more or less severe drought (Figure 10a).

Figure 10.

Figure 10

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Variation curves of NDVI (a), TCI (b), and VCI (c) indices for the Marrakech-Haouz region provided by NOAA-AVHRR.

The TCI index clearly shows a relative correlation with the NDVI index, specifically for years that have optimal temperature conditions for vegetation and others that reflect thermally stressful conditions (Figure 10b). A detailed observation of the TCI evolution (Figure 10c) shows a clear dominance of moisture conditions during the 1990s and late 1980s. On the other hand, after the year 2000, a relative drought, more or less accentuated, was detected in the High Atlas of Marrakech and the Haouz plain.

4. Conclusions

In this study, we have performed vegetation cover mapping based on Remote Sensing (RS) data processing, using a simple methodology based on open-source software. The NDVI, in addition to other vegetation indices, enabled us to detect changes in vegetation coverage for monitoring climate change and its influence on vegetation cover. This study shows a repetitive fluctuation in the decrease of NDVI mean value during most period of the last 40 years, due to the contrasting climate setting of the study area and the influence of the alternation of humid and dry periods in the High Atlas of Marrakech. In addition, the presented results underline that open-source software, such as SAGA GIS and SNAP combined with the NOAA's AVHRR sensors, constitute effective tools for climate change detection and mapping vegetation health with resolutions from 1 to 4 km. The use of Landsat with its various scenes and its well-stocked archives delivers appropriate results with resolution of 30 m. However, the results of the calculation of NDVI presents the limitation of being dependent on the resolution of satellite imagery; the higher is the resolution, the better are the results. Another limitation is related to cloud coverage, which restricts the number of available imageries for this study. Regarding the remote sensing tools, namely SAGA GIS and SNAP software, a powerful hardware is required to avoid potential overload crushes when processing a large amount of data sets. This study has highlighted the importance of the NDVI dynamics, to understand climate change impacts on the Zat valley. NDVI is computationally simple, efficient, and can be associated to other vegetation indices for future detection and investigation of drought effects, especially in arid areas in Morocco.

Declarations

Author contribution statement

Adaze Essaadia, Algouti Abdellah, Farah Abdelouahed: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Ahmed Algouti, Elbadaoui Kamal: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Data associated with this study has been deposited at https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php.

Declaration of interest's statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

References

  1. Adole T., Dash J., Atkinson P.M. A systematic review of vegetation phenology in Africa. Ecol. Inf. 2016;34:117–128. doi: 10.1016/j.ecoinf.2016.05.004. [DOI] [Google Scholar]
  2. Ait Mlouk M., Algouti Ab., Algouti A., Ourhzif Z. Utilisation des images satellitaires du Landsat dans l’étude de la dégradation des berges des oueds : exemple des berges des oueds R’Dat, Zat et Tensift lors de la crue de Novembre 2014 (Marrakech, Maroc) Int. J. Innovat. Sci. Res. 2016;27(1):119–129. [Google Scholar]
  3. Ait Mlouk M., Algouti Ab., Algouti A., Ourhzif Z. Assessment of riverbank erosion in semi-arid climate regions using remote sensing, and GIS data: a case study of Rdat River, Marrakech, Morocco. Estud. Geol. 2018;74(2):81. [Google Scholar]
  4. Alhumaima A.S., Abdullaev S.M. Tigris Basin Landscapes: sensitivity of Vegetation Index NDVI to climate variability derived from observational and reanalysis data. Earth Interact. 2020;24(7):1–18. doi: 10.1175/EI-D-20-0002.1. [DOI] [Google Scholar]
  5. Amaya A., Algouti Ab., Algouti A. The use of remote sensing and GIS to identify water erosion risks areas in the Moroccon High Atlas. The case study of the N’fis wadi watershed. Intermt. J. Sci. 2014;3:43–51. [Google Scholar]
  6. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Rem. Sens. Environ. 1997;62:241–252. [Google Scholar]
  7. Che N., Price J.C. Survey of radiometric calibration results and methods for visible and near-infrared channels of NOAA-7, -9, and -11 AVHRRs. Remote Sens. Environ. 1992;41:19–27. [Google Scholar]
  8. Chu H., Venevsky S., Wu C., Wang M. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Sci. Total Environ. 2019;650:2051–2062. doi: 10.1016/j.scitotenv.2018.09.115. [DOI] [PubMed] [Google Scholar]
  9. Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Böhner J. System for automated geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. (GMD) 2015;8:1991–2007. doi: 10.5194/gmd-8-1991-2015. [DOI] [Google Scholar]
  10. De Keukelaere L., Sterckx S., Adriaensen S., Knaeps E., Reusen I., Giardino G., Brescani M., Hunter P., Van der Zande D., Vaicute D. iCOR image correction for atmospheric effects, Results for Landsat-8 OLI and Sentinel-2 MSI. Eur. J. Remote Sens. 2018;51:525–542. [Google Scholar]
  11. Duarte L., Teodoro A.C., Monteiro A.T., Cunha M., Gonçalves H. QPhenoMetrics: an open-source software application to assess vegetation phenology metrics. Comput. Electron. Agric. 2018;148:82–94. doi: 10.1016/j.compag.2018.03.007. [DOI] [Google Scholar]
  12. Ezaidi S., Aydda A., Kabbachi B., Althuwaynee O.F., et al. Multi-temporal Landsat-derived NDVI for vegetation cover degradation for the period 1984-2018 in part of the Arganeraie Biosphere Reserve (Morocco) Remote Sens. Appl.: Soc. Environ. 2022;27 doi: 10.1016/j.rsase.2022.100800. [DOI] [Google Scholar]
  13. Gao W., Zheng C., Liu X., Lu Y., Chen Y., et al. NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: a case study in the Mu Us Sand Land. Ecol. Indicat. 2022;137 [Google Scholar]
  14. Hadach F., Algouti A., Algouti Ab. Sédimentologie et paléogéographie du Lias et Dogger de la cuvette d'Ouanina, Haut Atlas Occidental, Maroc. Int. J. Innovat. Appl. Stud. 2017;24(1):31–58. [Google Scholar]
  15. Hadach F., Algouti A., Algouti Ab. Triassic and Jurassic sedimentological study of the High Atlas of Marrakesh, Morocco: contribution to the localization of levels favourable to the artisanal exploitation of pottery. Eur. Sci. J. 2015;11(21):308–333. [Google Scholar]
  16. Hammi S., Simonneau V., Alifriqui M., Auclair L., Montes N. Évolution des recouvrement forestiers et de l’occupation des sols entre 1964 et 2002 dans la haute vallée des Ait Bouguemez (Haut Atlas central, Maroc) Sécheres. 2007;18:1–7. [Google Scholar]
  17. Höpfner C., Scherer D. Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data. Biogeosciences. 2011;8:3359–3373. doi: 10.5194/bg-8-3359-2011. [DOI] [Google Scholar]
  18. Hou M.T., Zhao H.Y., Wang Z., Yan X.D. Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: a review. Clim. Environ. Res. 2013;18:353–364. (In chinese and abstract in english) [Google Scholar]
  19. Huang S., Tang L., Hupy J.P., Wang Y., Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 2021;32:1–6. doi: 10.1007/s11676-020-01155-1. [DOI] [Google Scholar]
  20. Jin X., Wan L., Zhang Y.K., Hu G., Schaepman M.E., Clevers J.G.P.W., Su Z.B. Quantifcation of spatial distribution of vegetation in the Qilian Mountain area with MODIS NDVI. Int. J. Rem. Sens. 2009;30(21):5751–5766. [Google Scholar]
  21. Kalisa W., Igbawua T., Henchiri M., Ali S., Zhang S., Bai Y., Zhang J. Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015. Sci. Rep. 2019;9 doi: 10.1038/s41598-019-53150-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kogan F., Gitelson A., Zakarin E., Spivak L., Lebed L. AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: calibration and validation. Photogramm. Eng. Rem. Sens. 2003;69(8):899–906. [Google Scholar]
  23. Kogan F.N. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 1995;15:91–100. [Google Scholar]
  24. Kogo B.K., Kumar L., Koech R. Forest cover dynamics and underlying driving forces affecting ecosystem services in western Kenya. Remote Sens. Appl.: Soc. Environ. 2019;14:75–83. doi: 10.1016/j.rsase.2019.02.007. [DOI] [Google Scholar]
  25. Lemenkova P. Geoecology and Sustainable Use of Mineral Resources, from Science to Practice Proceedings of the 3 Rd International Conference of Young Scientists. 2015. Analysis of Landsat NDVI time series for detecting degradation of vegetation; pp. 978–983. [Google Scholar]
  26. Lin X., Niu J., Berndtsson R., Yu X., Zhang L., Chen X. NDVI dynamics and its response to climate change and reforestation in northern China. Rem. Sens. 2020;12:4138. doi: 10.3390/rs12244138. 2020. [DOI] [Google Scholar]
  27. Maimouni S., Daghor L., Oukassou M., El Moutaki S., Lhissou R. Evaluate the effect of topographic factors and lithology on forest cover distribution: a case study of the Moroccan high Atlas. Environ. Model. Assess. 2021;26:787–801. doi: 10.1007/s10666-021-09785-3. [DOI] [Google Scholar]
  28. Maimouni S., El-Harti A., Bannari A., Bachaoui E.-M. Water erosion risk mapping using derived parameters from digital elevation model and remotely sensed data. Geo Spatial Inf. Sci. 2012;15(3) [Google Scholar]
  29. Matas-Granados L., Pizarro M., Cayuela L., Domingo D., Gómez D., García M.B. Long-term monitoring of NDVI changes by remote sensing to assess the vulnerability of threatened plants. Biol. Conserv. 2022;265 doi: 10.1016/j.biocon.2021.109428. [DOI] [Google Scholar]
  30. Modica G., Solano F., Merlino A., Di Fazio S., Barreca F., Laudari L., Fichera C.R. Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: a case study in Calabria (Italy) J. Agric. Eng. 2016;47(4):205–215. [Google Scholar]
  31. Mohajane M., Essahlaoui A., Oudija F., El Hafyani M., Cláudia Teodoro A. Mapping forest species in the central middle Atlas of Morocco (azrou forest) through remote sensing techniques. ISPRS Int. J. Geo-Inf. 2017;6:275. doi: 10.3390/ijgi6090275. [DOI] [Google Scholar]
  32. Mohajane M., Essahlaoui A., Oudija F., Hafyani M.E., Hmaidi A.E., Ouali A.E., Randazzo G., Teodoro A.C. Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments. 2018;5:131. [Google Scholar]
  33. Myneni R.B., Asrar G. Atmospheric effects and spectral vegetation indices. Remote Sens. Environ. 1994;47:390–402. [Google Scholar]
  34. Nejadrekabi M., Eslamian S., Zareian M.J. Use of spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province. Int. J. Environ. Sci. Technol. 2022;19:6573–6594. doi: 10.1007/s13762-021-03852-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Nse O.U., Okolie C.J., Nse V.O. Dynamics of land cover, land surface temperature and NDVI in Uyo City, Nigeria. Sci. Afr. 2020;10 doi: 10.1016/j.sciaf.2020.e%2000599. [DOI] [Google Scholar]
  36. Prăvălie R., Sîrodoev I., Nita I.A., Cristian P., Monica D., Bogdan R., et al. NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018. Ecol. Indicat. 2022;136 doi: 10.1016/j.ecolind.2022.108629. [DOI] [Google Scholar]
  37. Quiring S.M., Ganesh S. Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas. Agric. Meteorol. 2010;150:330 339. doi: 10.1016/j.agrformet.2009.11.015. [DOI] [Google Scholar]
  38. Rahimzadeh Bajgiran P., Darvishsefat A.A., Khalili A., Makhdoum M.F. Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. J. Arid Environ. 2008;72:1086–1096. [Google Scholar]
  39. Rouse J.W., Haas R.H., Scheel J.A., Deering D.W. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, vol. 1. 1974. Monitoring vegetation systems in the great plains with ERTS; pp. 48–62.https://ntrs.nasa.gov/citations/19740022614 [Google Scholar]
  40. Roy D.P., Wulder M.A., Loveland T.R., Woodcock C.E., Allen R.G., Anderson M.C., et al. Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 2014;145:154–172. [Google Scholar]
  41. USGS. 2018. https://www.usgs.gov/special-topics/remote-sensing-phenology
  42. Vásquez-Méndez R., Ventura-Ramos E., Oleschko K., Hernández- Sandoval L., Parrot J.F., Nearing M.A. Soil erosion and runof in diferent vegetation patches from semiarid Central Mexico. Catena. 2010;80(3):162–169. doi: 10.1016/j.catena.2009.11.003. [DOI] [Google Scholar]
  43. Wang T. Vegetation NDVI change and its relationship with climate change and human activities in Yulin, shaanxi Province of China. J. Geosci. Environ. Protect. 2016;4:28–40. [Google Scholar]
  44. Zhan Z.Z., Liu H.B., Li H.M., Wu W., Zhong B. The relationship between NDVI and terrain factors—a case study of Chongqing. Procedia Environ. Sci. 2012;12:765–771. doi: 10.1016/j.proenv.2012.01.347. [DOI] [Google Scholar]
  45. Zhang Z., Zinda J.A., Yang Z., Yin M., Ou X., Xu Q., Yu Q. Effects of topographic attributes on landscape pattern metrics based on redundancy ordination gradient analysis. Landsc. Ecol. Eng. 2018;14(1):67–77. doi: 10.1007/s11355-016-0322-6. [DOI] [Google Scholar]
  46. Zhu Z. Change detection using Landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J. Photogrammetry Remote Sens. 2017;130:370–384. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data associated with this study has been deposited at https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php.

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