Assessment of vegetation dynamics and forest loss using google earth engine and multi-temporal sentinel-2 imagery Article Swipe
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· 2022
· Open Access
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· DOI: https://doi.org/10.4314/as.v21i2.10
This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015 to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into four land cover classes; water bodies, non-vegetated, grassland /cropland /shrubs, and forest using NDVI threshold values of < 0.01, 0.01-0.20, 0.20-0.30 and > 0.30, respectively. The classified maps allowed for the assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that non-vegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to 6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass, grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4314/as.v21i2.10
- https://www.ajol.info/index.php/as/article/download/227211/214472
- OA Status
- diamond
- Cited By
- 2
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283712538
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283712538Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4314/as.v21i2.10Digital Object Identifier
- Title
-
Assessment of vegetation dynamics and forest loss using google earth engine and multi-temporal sentinel-2 imageryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-22Full publication date if available
- Authors
-
Dahir M. Omar, Mohammed Oludare Idrees, H. Ahmadu, A. Yusuf, O. A. Ipadeola, A. Babalola, A. AbdulyekeenList of authors in order
- Landing page
-
https://doi.org/10.4314/as.v21i2.10Publisher landing page
- PDF URL
-
https://www.ajol.info/index.php/as/article/download/227211/214472Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.ajol.info/index.php/as/article/download/227211/214472Direct OA link when available
- Concepts
-
Normalized Difference Vegetation Index, Grassland, Vegetation (pathology), Environmental science, Land cover, Enhanced vegetation index, Physical geography, Remote sensing, Forestry, Land use, Hydrology (agriculture), Leaf area index, Geography, Vegetation Index, Ecology, Geology, Biology, Pathology, Geotechnical engineering, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.less | 186 |
| abstract_inverted_index.loss | 136 |
| abstract_inverted_index.maps | 87 |
| abstract_inverted_index.mean | 39 |
| abstract_inverted_index.than | 187 |
| abstract_inverted_index.that | 105 |
| abstract_inverted_index.were | 45 |
| abstract_inverted_index.with | 189 |
| abstract_inverted_index.(0.21 | 147 |
| abstract_inverted_index.(GEE) | 16 |
| abstract_inverted_index.0.01, | 78 |
| abstract_inverted_index.0.30, | 83 |
| abstract_inverted_index.2020. | 102 |
| abstract_inverted_index.6.78% | 125 |
| abstract_inverted_index.75.68 | 199 |
| abstract_inverted_index.Using | 32 |
| abstract_inverted_index.about | 164 |
| abstract_inverted_index.cover | 62 |
| abstract_inverted_index.earth | 14 |
| abstract_inverted_index.index | 22 |
| abstract_inverted_index.level | 48 |
| abstract_inverted_index.mass, | 174 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.total | 172 |
| abstract_inverted_index.using | 12, 47, 72 |
| abstract_inverted_index.water | 64, 140, 196 |
| abstract_inverted_index.years | 154 |
| abstract_inverted_index.(NDVI) | 23 |
| abstract_inverted_index.17.00% | 135 |
| abstract_inverted_index.18.53% | 110 |
| abstract_inverted_index.23.76% | 130 |
| abstract_inverted_index.25.00% | 118 |
| abstract_inverted_index.274.85 | 191 |
| abstract_inverted_index.42.56% | 114 |
| abstract_inverted_index.6.70%) | 169 |
| abstract_inverted_index.770.79 | 178 |
| abstract_inverted_index.Google | 13 |
| abstract_inverted_index.Result | 103 |
| abstract_inverted_index.across | 152 |
| abstract_inverted_index.bodies | 141, 197 |
| abstract_inverted_index.engine | 15 |
| abstract_inverted_index.forest | 71, 121, 158 |
| abstract_inverted_index.gain), | 119 |
| abstract_inverted_index.images | 56 |
| abstract_inverted_index.showed | 104 |
| abstract_inverted_index.values | 75 |
| abstract_inverted_index.yearly | 38, 93 |
| abstract_inverted_index.0.21%). | 202 |
| abstract_inverted_index.371.131 | 166 |
| abstract_inverted_index.43.04%) | 194 |
| abstract_inverted_index.50.00%, | 150 |
| abstract_inverted_index.allowed | 88 |
| abstract_inverted_index.between | 8, 99 |
| abstract_inverted_index.bodies, | 65 |
| abstract_inverted_index.changes | 7, 98 |
| abstract_inverted_index.derived | 24 |
| abstract_inverted_index.reduced | 123 |
| abstract_inverted_index.whereas | 139 |
| abstract_inverted_index./shrubs, | 69 |
| abstract_inverted_index.50.07%), | 181 |
| abstract_inverted_index.classes; | 63 |
| abstract_inverted_index.compared | 128 |
| abstract_inverted_index.computed | 46 |
| abstract_inverted_index.constant | 146 |
| abstract_inverted_index.dynamics | 5 |
| abstract_inverted_index.forest); | 138 |
| abstract_inverted_index.measured | 131 |
| abstract_inverted_index.occupied | 176, 198 |
| abstract_inverted_index.platform | 17 |
| abstract_inverted_index.product. | 50 |
| abstract_inverted_index.regional | 3 |
| abstract_inverted_index.remained | 144 |
| abstract_inverted_index.slightly | 185 |
| abstract_inverted_index.studied. | 155 |
| abstract_inverted_index./cropland | 68 |
| abstract_inverted_index.0.20-0.30 | 80 |
| abstract_inverted_index.catalogue | 28 |
| abstract_inverted_index.estimated | 161 |
| abstract_inverted_index.evaluated | 2 |
| abstract_inverted_index.grassland | 67 |
| abstract_inverted_index.increased | 108 |
| abstract_inverted_index.threshold | 74 |
| abstract_inverted_index.variation | 94 |
| abstract_inverted_index.0.01-0.20, | 79 |
| abstract_inverted_index.Presently, | 156 |
| abstract_inverted_index.assessment | 91 |
| abstract_inverted_index.capability | 35 |
| abstract_inverted_index.classified | 58, 86 |
| abstract_inverted_index.difference | 20 |
| abstract_inverted_index.imageries. | 31 |
| abstract_inverted_index.normalized | 19 |
| abstract_inverted_index.relatively | 145 |
| abstract_inverted_index.sentinel-2 | 30 |
| abstract_inverted_index.vegetation | 4, 21, 96 |
| abstract_inverted_index.Subsequently, | 51 |
| abstract_inverted_index.computational | 34 |
| abstract_inverted_index.non-vegetated | 106, 182 |
| abstract_inverted_index.respectively) | 151 |
| abstract_inverted_index.respectively. | 84 |
| abstract_inverted_index.multi-petabyte | 27 |
| abstract_inverted_index.non-vegetated, | 66 |
| abstract_inverted_index.grassland/cropland/shrubs | 143, 175 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.52725535 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |