Mapping the Eucalyptus spp woodlots in communal areas of Southern Africa using Sentinel-2 Multi-Spectral Imager data for hydrological applications

Sibanda, Mbulisi; Buthelezi, Siphiwokuhle; Ndlovu, Helen S.; Mothapo, Mologadi C.; Mutanga, Onisimo

doi:10.1016/j.pce.2021.102999

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…The relatively high accuracies achieved using RF (87.83%) and SVM (86.31%) in the current study demonstrated that the high-resolution Sentinel-2 data is capable of discriminating Tamarix from other vegetation land covers at 10 m spatial resolution. Our results are similar to the overall accuracy (OA) of 85.07% obtained to discriminate invasive Eucalyptus from other land covers using Sentinel-2 data by Sibanda et al (2021) . The OA obtained in this study is also relatively higher than the 77% obtained when Sentinel-2 data was used to map the invasive Baccharis halimifolia in Spain ( Calleja et al, 2019 ).…”

Section: Discussionsupporting

confidence: 86%

A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

Newete

Mayonde

Kekana

et al. 2023

PeerJ

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Background The management of invasive Tamarix genotypes depends on reliable and accurate information of their extent and distribution. This study investigated the utility of the multispectral Sentinel-2 imageries to map infestations of the invasive Tamarix along three riparian ecosystems in the Western Cape Province of South Africa. Methods The Sentinel-2 image was acquired from the GloVis website (http://glovis.usgs.gov/). Random forest (RF) and support vector machine (SVM) algorithms were used to classify and estimate the spatial distribution of invasive Tamarix genotypes and other land-cover types in three riparian zones viz. the Leeu, Swart and Olifants rivers. A total of 888 reference points comprising of actual 86 GPS points and additional 802 points digitized using the Google Earth Pro free software were used to ground-truth the Sentinel-2 image classification. Results The results showed the random forest classification produced an overall accuracy of 87.83% (with kappa value of 0.85), while SVM achieved an overall accuracy of 86.31% with kappa value of 0.83. The classification results revealed that the Tamarix invasion was more rampant along the Olifants River near De Rust with a spatial distribution of 913.39 and 857.74 ha based on the RF and SVM classifiers, respectively followed by the Swart River with Tamarix coverage of 420.06 ha and 715.46 hectares, respectively. The smallest extent of Tamarix invasion with only 113.52 and 74.27 hectares for SVM and RF, respectively was found in the Leeu River. Considering the overall accuracy of 85% as the lowest benchmark for a robust classification, the results obtained in this study suggests that the SVM and RF classification of the Sentinel-2 imageries were effective and suitable to map invasive Tamarix genotypes and discriminate them from other land-cover types.

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Section: Discussionsupporting

confidence: 86%

A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

Newete

Mayonde

Kekana

et al. 2023

PeerJ

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“…The red edge spectral band can be used as a sensitive discriminator of Eucalyptus plantations [26]. We therefore applied the normalized difference red edge index (NDV I Re ) [27] to enhance the differences in spectral information between Eucalyptus plantations and other forest types.…”

Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

Changes in Water-Use Efficiency of Eucalyptus Plantations and Its Driving Factors in a Small County in South China

Yao,

Huang,

et al. 2023

Water

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Ecosystem water-use efficiency (WUE) has been central in revealing the variability in terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus plantations can simultaneously impact net primary production (NPP) and actual evapotranspiration (ETa), components of WUE, resulting in changes in terrestrial carbon and water cycles. However, there are few detailed studies on the changes in the WUE of Eucalyptus plantations at the catchment scale with high spatial remote sensing imagery. Here, we present the changes in the WUE of Eucalyptus plantations and its driving factors (i.e., NPP and ETa) using satellite-based models combined with 5-m spatial resolution RapidEye imagery in a small county in South China. The increases in ETa of Eucalyptus plantations are primarily the result of climate warming and result in low WUE of Eucalyptus plantations. The management practice used (short rotation in this study) can enhance the effect of climate warming on WUE by varying the NPP of Eucalyptus plantations. A high value of NPP leads to a high WUE of Eucalyptus plantations at the end of a short rotation, while a low value of NPP results in a low WUE at the beginning of another short rotation. Changes in the WUE of Eucalyptus plantations indicated large spatial and temporal variability, associated with climate warming and short-rotation practices.

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“…The ESA and European Commission's policies makes Sentinel's data easily accessible [46]. More detailed technical specifications of both Sentinel-1 SAR and Sentinel-2A MSI data are available in previous studies [47,48] and will not be repeated here.…”

Section: Datamentioning

confidence: 99%

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Zhang

et al. 2021

Remote Sensing

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Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

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Mapping the Eucalyptus spp woodlots in communal areas of Southern Africa using Sentinel-2 Multi-Spectral Imager data for hydrological applications

Cited by 11 publications

References 38 publications

A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

A rapid and accurate method of mapping invasive Tamarix genotypes using Sentinel-2 images

Changes in Water-Use Efficiency of Eucalyptus Plantations and Its Driving Factors in a Small County in South China

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

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