A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the Cameroonians Shores of Lake Chad and Its Hinterland

Gadal, Sébastien; Gbetkom, Paul Gérard; Mfondoum, Alfred Homère Ngandam

doi:10.5220/0010521200250036

Cited by 7 publications

(3 citation statements)

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“…This methodology has been proven to provide good results, achieving an overall land cover classification accuracy of 94% (Gadal et al, 2021), a performance that can be attributed to the spectral complexity of Sentinel-2 data, particularly the rededge bands which give room for separability of erosion classes. Low separability is a common limitation to the applicability of classification methods.…”

Section: Discussionmentioning

confidence: 99%

“…Principal Component Analysis (PCA) is used for the integration of Sentinel-2 data with other remotely sensed data in a pan-sharpening approach (Wang et al, 2016). A first linear approach was used to determine the weight of each index and then used standard deviation to classify into categories (Gadal et al, 2021) in a Stepwise Multiple Linear Regression (SMLR) (Nzuza et al, 2021).…”

Section: Sentinel 2 Datamentioning

confidence: 99%

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Remote Mapping of Soil Erosion Risk in Iceland

Fernández

Adermann

Pizzolato

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of remote-sensing based methods for soil erosion assessment has been increasing in recent years thanks to the availability of free access satellite data, and it has repeatedly proven to be successful. Its application to the Arctic presents a number of challenges, due to its peculiar soils with short growing periods, winter storms, wind, and frequent cloud and snow cover. However, the benefits of applying these techniques would be especially valuable in arctic areas, where ground local information can be hard to obtain due to hardly accessible roads and lands. Here we propose a solution which uses a Support Vector machine classification model and ground truth samples to calibrate the processed remote images over a specific area, in order to then automate the analysis for larger, less accessible areas. This solution is being developed for soil erosion studies of Iceland specifically, using Sentinel 2 satellite data combined with local assessment data from Iceland’s Soil Conservation Services department.

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Section: Discussionmentioning

confidence: 99%

Section: Sentinel 2 Datamentioning

confidence: 99%

Remote Mapping of Soil Erosion Risk in Iceland

Fernández

Adermann

Pizzolato

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…These specificities are challenging to spatialize at a point that sub-Saharan African ecosystems have played a key role in the development of remote sensing of vegetation for decades [9][10][11]. Nowadays, several satellite-based models provide scalable spectral information relevant to vegetation distribution and changes, physiology, and phenology, in broad terms, to monitor and combat land degradation, especially in African drylands [12][13][14][15]. As such, numerous spectral indices measure the vegetation parameters [16].…”

Section: Introductionmentioning

confidence: 99%

Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote Sensing-Based Multi-Angles Methodology Using Google Earth Engine

Mfondoum¹,

Petcheu²,

Tchatchouang³

et al. 2023

GIS and Spatial Analysis

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This chapter proposes a remote sensing multi-angles methodology to assess the transition at the interface of the forest-savanna land cover. On Sentinel2-A median images of successive dry seasons, three referential and nine analytical spectral indices were computed. The change vector analysis (CVA) was performed, selecting further one magnitude per index. The averaged moving standard deviation index (aMSDI) was proposed to compare spatial intensity of anomalies among selected CVA, and then statistically assessed through spatial and no-spatial autoregression tests. The cross-correlation and simple linear combination (SCL) computations spotted the overall anomaly extent. Three machine learning algorithms, i.e., classification and regression trees (CART), random forest (RF), and support vector machine (SVM), helped mapping the distribution of each specie. As result, the CVA confirmed each index ability to add new information. The aMSDI gave the harmonized interval [0–0.083] among CVA, confirmed with all p−values=0, z−scores>2.5, clustering of anomaly pixel,and adjusted R2≤0.19. Three trends of vegetation distribution were distinguished with 88.7% overall accuracy and 0.86 kappa coefficient. Finally, extremely affected areas were spotted in upper latitudes towards Sahel and desert.

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Use of Geostatistical Methods Involving Standard Deviation and Semivariogram to Assess the Accuracy of Water Surface Extraction by Supervised Classification

Bendib,

Boutrid

2023

J Indian Soc Remote Sens

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A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the Cameroonians Shores of Lake Chad and Its Hinterland

Cited by 7 publications

References 0 publications

Remote Mapping of Soil Erosion Risk in Iceland

Remote Mapping of Soil Erosion Risk in Iceland

Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote Sensing-Based Multi-Angles Methodology Using Google Earth Engine

Use of Geostatistical Methods Involving Standard Deviation and Semivariogram to Assess the Accuracy of Water Surface Extraction by Supervised Classification

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