Landsat-8 based coastal ecosystem mapping in South Africa using random forest classification in Google Earth Engine

Bessinger, Mariel; Lück‐Vogel, Melanie; Skowno, Andrew; Conrad, Ferozah

doi:10.1016/j.sajb.2022.08.014

Cited by 10 publications

(7 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the literature indicates that commonly used indices such as the NDVI, EVI, and SAVI, usually improve the accuracy of LULC classification based on satellite images [45,47], in this study, except for NDVI, these indices had little relevance-some of them were even excluded from the classification because of their lack of relevance, which was the case for EVI. The mNDWI also proved to be of little relevance, possibly because of its limitation in separating water from shaded surfaces, typical of the regions with "inselbergs" distributed throughout the region.…”

Section: Discussionmentioning

confidence: 86%

“…The relative importance of each variable was based on the training samples to generate a random-forest model, with different trees sizes and different variables per split. We relied on the Scikit-Learn module [44], which determined the variable importance by analyzing how much the nodes that use each variable reduced the impurity across all trees on average by weighting the number of training samples reaching each node [45]. The importance of a feature was computed as the normalized total reduction of the criterion brought by that feature, also known as the Gini importance or decrease in Gini impurity (DGI) [45].…”

Section: Feature Selection (Fs) Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Macarringue,

Bolfe,

Duverger

et al. 2023

IJGI

View full text Add to dashboard Cite

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.

show abstract

Section: Discussionmentioning

confidence: 86%

Section: Feature Selection (Fs) Methodsmentioning

confidence: 99%

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Macarringue,

Bolfe,

Duverger

et al. 2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Beyond the illustrative example of mangroves, forests in general can be part of disaster risk reduction efforts in the face of hydrometeorological hazards, such as floods (van Noordwijk, Tanika and Lusiana, 2017;Tembata et al, 2020) as discussed in the previous section, storm surge (Kayum, Shimatani and Minagawa, 2022), landslides (Forbes and Broadhead, 2011), avalanches (Zurbriggen et al, 2014), and erosion of riverbanks and coastlines (Bessinger et al, 2022).…”

Section: Forests Protect Communities From Hazardsmentioning

confidence: 99%

Forest-based adaptation: transformational adaptation through forests and trees

2022

View full text Add to dashboard Cite

The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned.The views expressed in this information product are those of the author(s) and do not necessarily reflect the views or policies of FAO.

show abstract

“…However, recent advancements in machine learning (ML) have led to the development of deep learning classification techniques for plant species classification, such as Convolutional Neural Networks (CNN) [17,18], Long Short-Term Memory Networks (LSTMs) [19,20], Recurrent Neural Networks (RNNs) [21,22] and Multilayer Perceptrons (MLPs) [23]. For this study, the Random Forest was selected since it is considered to be one of the most widely used algorithms for land cover classification using remote sensing data [24][25][26][27][28][29][30]. This is confirmed by the meta-analysis conducted by Tamiminia et al 2020 [31], based on 349 peer-reviewed GEE articles published in 146 journals between 2010 and October 2019, showing that the Random Forest was the most frequently used algorithm for satellite imagery processing.…”

Section: Introductionmentioning

confidence: 99%

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

show abstract

Landsat-8 based coastal ecosystem mapping in South Africa using random forest classification in Google Earth Engine

Cited by 10 publications

References 52 publications

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Forest-based adaptation: transformational adaptation through forests and trees

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Contact Info

Product

Resources

About