Accuracy assessment and uncertainty of the 2020 10-meter resolution land use land cover maps at local scale. Case: talassemtane national park, Morocco

Chemchaoui, Abdelazziz; Brhadda, Najiba; Alaoui, Hassana Ismaili; Souad, El Gazi; Bouchra, El Asri; Rabea, Ziri

doi:10.21203/rs.3.rs-2953599/v2

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Previous research [3,[21][22][23], particularly in European and Asian regions, has extensively utilized GEE and various algorithms for land cover classification, providing valuable insights into the effectiveness of remote sensing technologies and machine learning algorithms in identifying land cover dynamics. The research community has employed GEE to examine diverse landscapes and land use patterns, contributing to an understanding of environmental changes and ecosystem dynamics.…”

Section: Related Workmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Ouchra,

Belangour,

Erraissi

2024

ISI

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This study embarks on an evaluation of the efficacy of six supervised machine learning algorithms in the classification of land cover in Casablanca, Morocco, utilizing Landsat satellite imagery. Employing the Google Earth Engine (GEE) platform for data collection, the research encompasses meticulous pre-processing steps and the application of various supervised algorithms, followed by a comprehensive evaluation of their performance. The city of Casablanca, characterized by rapid urbanization and evolving land-use patterns, presents an exemplary case for scrutinizing the algorithms' ability to accurately classify different land zones. These zones encompass water bodies, urban areas, agricultural lands, barren terrains, and forests. The algorithms under scrutiny include Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), Minimum Distance (MD), Decision Tree (DT), and Gradient Tree Boosting (GTB). The assessment of classification outcomes leverages multiple accuracy indicators, namely overall accuracy (OA), Kappa coefficient, user accuracy (UA), and producer accuracy (PA). Results indicate that the Random Forest algorithm exhibits superior performance, achieving an accuracy of 95.42%, while the Support Vector Machine algorithm lags with a lower accuracy of 83%. This investigation underscores the critical role of advanced machine learning algorithms in land cover classification, a pivotal aspect for urban and regional planning, natural resource management, and risk assessment in rapidly changing environments.

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Section: Related Workmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Ouchra,

Belangour,

Erraissi

2024

ISI

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“…Although the literature review [14,[22][23][24] indicates extensive research on land cover classification using GEE and various algorithms in European and Asian regions, there is little specific research for Morocco. This review is an opportunity for researchers to ex-plore land cover classification in Casablanca, Morocco, using supervised and unsupervised algorithms by exploiting the advantages of the GEE platform [13,25] .…”

Section: Related Workmentioning

confidence: 99%

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Ouchra,

Belangour,

Erraissi

2023

j. of environ. & earth. sci.

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Satellite image classification is crucial in various applications such as urban planning, environmental monitoring, and land use analysis. In this study, the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification, focusing on a case study in Casablanca using Landsat 8 imagery. This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment. The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city, the authors extract relevant features and partition them into training and test sets, and then use random forest (RF), SVM (support vector machine), classification, and regression tree (CART), gradient tree boost (GTB), decision tree (DT), and minimum distance (MD) algorithms. Through a series of experiments, the authors evaluate the performance of each machine learning method in terms of accuracy, and Kappa coefficient. This work shows that random forest is the best-performing algorithm, with an accuracy of 95.42% and 0.94 Kappa coefficient. The authors discuss the factors of their performance, including data characteristics, accurate selection, and model influencing.

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Assessment of Systematic Errors in Mapping Electricity Access Using Night-Time Lights: A Case Study of Rwanda and Kenya

Raji,

Taneja,

Williams

2024

Remote Sensing

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Remotely sensed nighttime light data have become vital for electrification mapping in data-scarce regions. However, uncertainty persists regarding the veracity of these electrification maps. This study investigates how characteristics of electrified areas influence their detectability using nighttime lights. Utilizing a dataset comprising the locations, installation date, and electricity purchase history of thousands of electric meters and transformers from utilities in Rwanda and Kenya, we present a systematic error assessment of electrification maps produced with nighttime lights. Descriptive analysis is employed to offer empirical evidence that the likelihood of successfully identifying an electrified nighttime light pixel increases as characteristics including the time since electrification, the number of meters within a pixel, and the total annual electricity purchase of meters in a pixel increase. The performance of models trained on various temporal aggregations of nighttime light data (annual, quarterly, monthly, and daily) was compared, and it was determined that aggregation at the monthly level yielded the best results. Additionally, we investigate the transferability of electrification models across locations. Our findings reveal that models trained on data from Rwanda demonstrate strong transferability to Kenya, and vice versa, as indicated by balanced accuracies differing by less than 5% when additional data from the test location are included in the training set. Also, models developed with data from the centralized grid in East Africa were found to be useful for detecting areas electrified with off-grid systems in West Africa. This research provides valuable insight into the characterization of sources of nighttime lights and their utility for mapping electrification.

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Accuracy assessment and uncertainty of the 2020 10-meter resolution land use land cover maps at local scale. Case: talassemtane national park, Morocco

Cited by 3 publications

References 23 publications

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Supervised Machine Learning Algorithms for Land Cover Classification in Casablanca, Morocco

Comparison of Machine Learning Methods for Satellite Image Classification: A Case Study of Casablanca Using Landsat Imagery and Google Earth Engine

Assessment of Systematic Errors in Mapping Electricity Access Using Night-Time Lights: A Case Study of Rwanda and Kenya

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