A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms

Abstract

Abstract Wetlands are important for their peat reservoir, dynamic land cover and natural resources, ecological and hydrological regimes, fossil fuels reservoir, and crucial carbon storage. The global wetlands are decreasing since 1800 due to climatic phenomena and human activities. Wetland mapping with satellite data is not new but an ongoing challenge due to its precision relies on data quality and data classification schemes. The accuracy of such mapping is emerging due to the gradual establishment of satellite data and subsequent data modeling technologies, i.e., big data modeling with machine learning (ML) algorithms. Our study introduced a simple, scalable, and robust wetland classification by applying unsupervised (K-means cluster – KMC) and supervised (Support vector machine classification – SVMc) ML algorithms. We used Landsat optical data to model normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), as the primary inputs for KMC. Later KMC data were supervised by SVMc with training data from 20 field observations. The accuracy tests insights that both optical indices have considerably less error and all SVMc models have about 99% accuracy. The 1 to 1 validation insights that our wetland classification presented more detail wetland cover areas than reference data. The test case SVMc showed that Class 1 and 2 are optimally fitting, Class 3 and 4 are overfitting, and Class 5 is underfitting. Furthermore, the sensitivity analysis insight that all SVMc models are optimally fitting and SVMc is more sensitive to NDVI than NDWI. From SVMc models we can see that Selisoo bog in Estonia lost a considerable amount of wetland covers including water bodies, and more large forest covers are taking wetland areas, i.e., mixed forest and coniferous trees. Our methodological approach insights a simple and robust wetland classification based on advanced unsupervised and supervised ML algorithms despite some unavoidable limitation.