Use of time series normalized difference vegetation index (NDVI) to monitor fall armyworm (
            <i>Spodoptera frugiperda</i>
            ) damage on maize production systems in Africa

Adan, Marian; Tonnang, Henri E. Z.; Greve, Klaus; Borgemeister, Christian; Goergen, G.

doi:10.1080/10106049.2023.2186492

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…One of the intuitive approaches to solving the multi‐class problem is to split it into one‐to‐all binary classification problems (Elkano et al 2015), thus building independent models for each of the four vegetation coverage levels. The vegetation coverage levels were divided into four classes using ArcGIS 10.8.1 based on natural breaks threshold (Adan et al 2023): (1) bare or sparse vegetation (i.e. NDVI < 0.08); (2) low vegetation (0.09 < NDVI < 0.22); (3) medium vegetation (0.23 < NDVI < 0.49); and (4) high vegetation (i.e.…”

Section: Methodsmentioning

confidence: 99%

Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Liu

GUI

et al. 2023

Restoration Ecology

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Precise vegetation restoration is critical in drylands, as some inappropriate restoration attempts have even increased water scarcity and degradation in afforestation areas. Potential natural vegetation (PNV) is widely used to provide a reference for the appropriate location and vegetation type of restoration programs while the appropriate restored areas remain unknown. Therefore, we proposed a PNV–potential normalized difference vegetation index (PNDVI) coupling framework based on multiple machine learning (ML) algorithms for precise dryland vegetation restoration. Taking the lower Tarim River Basin (LTRB) with a total area of 1,182 km2 as a case study, its present suitable restoration locations, area, and appropriate planting species were quantitatively estimated. The results showed that the model developed by incorporating PNDVI into PNV with easily measurable and available data such as temperature and soil properties can accurately identify dryland restoration patterns. In LTRB, the potentially suitable habitats of trees and grass are closer to the riverbank, while shrubby habitats are further away from the course, covering 1.88, 2.96, and 25.12 km2, respectively. There is still enormous land potential for further expansion of the current trees and grass in the LTRB, with 2.56 and 1.54% of existing land supposed to be trees and grass, respectively. This study's novel aspect is combining PNV and PNDVI to quantify and estimate precise restoration patterns through multiple ML algorithms. The model developed here can be used to evaluate the suitable reforestation locations, area, and vegetation types in drylands and to provide a basis for precise vegetation restoration.

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Section: Methodsmentioning

confidence: 99%

Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Liu

GUI

et al. 2023

Restoration Ecology

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“…Z 1−α/2 represents the value corresponding to the standard normal distribution function at a confidence level of α. Based on the significance testing method and referring to relevant literature [53][54][55], |Z s | = 1.96 is chosen as the criterion for significance division. When |Z s | ≤ 1.96, it indicates that the vegetation change is not significant, and when |Z s | > 1.96 it indicates that the vegetation change is significant.…”

Section: Trend Analysismentioning

confidence: 99%

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Shi,

Lin,

Jing

et al. 2023

Sustainability

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(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

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Strategic Analysis of Collaborative Networks in Spodoptera frugiperda (Lepidoptera: Noctuidae) Research for Improved Pest Management Strategies

Karakkottil,

Pulamte,

Kumar

2024

Neotrop Entomol

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Use of time series normalized difference vegetation index (NDVI) to monitor fall armyworm ( Spodoptera frugiperda ) damage on maize production systems in Africa

Cited by 7 publications

References 48 publications

Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

Strategic Analysis of Collaborative Networks in Spodoptera frugiperda (Lepidoptera: Noctuidae) Research for Improved Pest Management Strategies

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