Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder, Niger

Abstract

In the modern era, vegetation dynamics is an important aspect of climate change studies. The present study examined spatiotemporal changes of (NDVI) normalized difference vegetation index in the Korama basin (Southern Zinder of Niger) from 2000 to 2018, and their correlation with climatic factors was predicted. To analyze the change of vegetation cover, geographical information system, MODIS_NDVI, remote sensing, and climate variables (e.g., temperature and precipitation) datasets were used. Further, the correlation was performed for different years of vegetation types during the growing season (June–October). Our results show an increasing trend in average maximum annual NDVI across the Korama River Basin in the years 2000 and 2018. Conversely, significantly increasing trends in most of the areas were reported. Moreover, in downstream the vegetation cover is increased in Matameye and Magaria, but with a smaller increase in the upstream rate in Mirriah. Furthermore, a decrease in the surface water was observed in the Tessaoua, Matameye, and Magaria sections of the study region in 2000 and 2018, while a rise in water surface area was observed in Matameye and Magaria in the years 2006 and 2012. During rainy and dry seasons, NDVI correlated differently with temperature and precipitation with strong seasonal variations, while the mean vegetation period of NDVI does not show any significant change. In addition, moderate increase was observed in years 2000 and 2012 (r: 0.22; P: 0.50; R2: 0.05; r: 0.31; P: 0.34, R2: 0.10, respectively), and weak decrease in 2006 and 2018 (r: 0.61; P: 0.04; R2: 0.37; r: 0.58; P: 0.06, R2:0.33, respectively). The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting the vegetation growth. Indeed, the trends calculated by the correlation analysis showed that as climate factors increased (July, August, and September), the NDVI value increased at a rate of 0.16, reflecting the best growth in vegetation and rise in water bodies, although significantly decreased during years. This study would be highly useful in choice-making for sustainable water resource management in the Korama watershed in Southern Zinder, Niger.

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

“…Although Plant Functional Types (PFTs) have relatively similar spectral properties (high absorption in RED and high reflection in NIR), they have specific properties in different ecoregions depending on the vegetation canopy structure. The spatial and temporal variability of spectral properties and consequently LAI are verified for different ecosystems of the world such as in Niger [38], China [39,40], Switzerland [41], and Australia [42]. The LAI estimates are affected by the environmental conditions and mainly through climatic (e.g., precipitation and temperature), environmental stressors (e.g., insect damage, drought, weeds, and nutrient deficiency), and phenological conditions.…”

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

“…Although plant functional types (PFTs) have relatively similar spectral properties (high absorption in RED and high reflection in NIR), they have specific properties in different ecoregions depending on the vegetation canopy structure. The spatial and temporal variability of spectral properties and, consequently, LAI, are verified for different ecosystems of the world, such as in Niger [37], China [38,39], Switzerland [40], and Australia [41]. The LAI estimates are affected by the environmental conditions and mainly through climatic (e.g., precipitation and temperature), environmental stressors (e.g., insect damage, drought, weeds, and nutrient deficiency), and phenological conditions.…”

Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt

“…So as to verify whether the classification results are accurate for change detection, a quantitative accuracy evaluation was performed. The most common technique to assess the accuracy of land cover maps is the confusion matrix [52,53]. The confusion matrix contrasts a classified image with its corresponding reference image on a category-by-category basis.…”