Assessing the spatiotemporal dynamics of vegetation cover as an indicator of desertification in Egypt using multi-temporal MODIS satellite images

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

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The use of index images for decoding the vegetation cover of inner mountain Dagestan, Russia

South of Russia ecology development ◽

10.18470/1992-1098-2020-4-126-136 ◽

2021 ◽

Vol 15 (4) ◽

pp. 126-136

Author(s):

R. T. Radzhabova ◽

N. A. Alekseenko ◽

B. M. Kuramagomedov ◽

Z. Sh. Tazhudinova ◽

Z. M. Sultanov

Keyword(s):

Vegetation Cover ◽

Satellite Images ◽

Spatial Information ◽

High Spatial Resolution ◽

Imaging System ◽

Field Research ◽

Digital Processing ◽

Google Earth ◽

Multi Temporal ◽

Sentinel 2

Aim. Selection and analysis of index images suitable for deciphering the vegetation cover in the conditions of inner mountain Dagestan.Methods. The study was carried out on the basis of multi-temporal satellite images of high spatial resolution, obtained by the imaging system of the Sentinel-2 series satellite, using methods of digital processing of geoimages. Processing was carried out using the capabilities of the Google Earth Engine geoservice.Results. Multi-temporal index images were obtained for the territory of inner mountain Dagestan. The time series of seasonal changes in the indices (NDVI, SAVI, EVI) were analyzed, making it possible to reveal the phenological patterns of vegetation and to map the vegetation cover on this basis. Schemes for decoding vegetation have been created by which areas are distinguished according to the following characteristics: devoid of vegetation, herbaceous vegetation of varying degrees of density or woody (deciduous and coniferous).Conclusion. When studying vegetation cover using index images in a range of natural conditions, it is necessary to take into account the natural features of the territory, as well using additional sources of spatial information including field research methods.

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Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

Remote Sensing ◽

10.3390/rs13112126 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2126

Author(s):

Yuliang Wang ◽

Mingshi Li

Keyword(s):

Standard Deviation ◽

Vegetation Cover ◽

Vegetation Index ◽

Dynamic Range ◽

Dynamic Change ◽

Estimation Model ◽

Sparse Vegetation ◽

Fractional Vegetation Cover ◽

Dynamic Mapping ◽

Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

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