Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Abstract

There are several techniques for mapping wetlands. In this study, we examined four statistical models to assess the potential distribution of wetlands in the South-Kivu province by combining optical and SAR images. The approach involved integrating topographic, hydrological, and vegetation indices into the four most used classifiers, namely Artificial Neural Network (ANN), Random Forest (RF), Boosted Regression Tree (BRT), and Maximum Entropy (MaxEnt). A wetland distribution map was generated and classified into 'wetland' and 'non-wetland.' The results showed variations in predictions among the different models. RF exhibited the most accurate predictions, achieving an overall classification accuracy of 95.67% and AUC and TSS values of 82.4%. Integrating SAR data improved accuracy and precision, particularly for mapping small inland wetlands. Our estimations indicate that wetlands cover approximately 13.5% (898,690 ha) of the entire province. BRT estimated wetland areas to be ~ 16% (1,106,080 ha), while ANN estimated ~ 14% (967,820 ha), MaxEnt ~ 15% (1,036,950 ha), and RF approximately ~ 10% (691,300 ha). The distribution of these areas varied across different territories, with higher values observed in Mwenga, Shabunda, and Fizi. Many of these areas are permanently flooded, while others experience seasonal inundation. Through digitization, the delineation process revealed variations in wetland areas, ranging from tens to thousands of hectares. The geographical distribution of wetlands generated in this study will serve as an essential reference for future investigations and pave the way for further research on characterizing and categorizing these areas.

Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan

“…The likelihood of land use change in every pixel is a function of the value of the explanatory variable at the same pixel. LR is employed to retrieve the variable conversion of wetlands in Kubu Raya Regency (Géant et al, 2023).…”

Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020

“…Noteworthy studies include that by Mwita et al [26], which identified 51 small wetlands in Kenya and Tanzania, producing detailed distribution maps. Geant et al [27] evaluated the potential distribution of small wetlands in the South Kivu Province. Gxokwe et al [28] detected and mapped semi-arid season wetlands in South Africa.…”

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

“…Various studies have extensively discussed the challenges in remote sensing classification and have proposed various solutions: multi-source data fusion that includes the integration of radar and optical data, as well as the use of higher-resolution datasets and auxiliary sources for training data [43,[49][50][51][52]. These approaches can solve problems related to varying spectral signatures and misclassification of land use land cover classes and improve the accuracy of the classification by employing advanced machine learning.…”

“…Therefore, by using the data from Sentinel-2 and Sentinel-1, this research aims to classify the study area into five common level-1 land use land covers and evaluate the potential of freely available sentinel products for mapping smallholder agricultural crop distribution and estimating acreage. In addition to investigating the suitability of sentinel products, the LULC classification techniques were tested to select the most accurate one for each investigated landscape [50,72,73]. The machine learning algorithms used in these experiments were RF, SVM, Classification and Regression Trees (CART), and GTB-chosen for their ability to discriminate between different classes, handle noisy data, and be applied with limited samples.…”