Monitoring environmental supporting conditions of a raised bog using remote sensing techniques

Abstract

Abstract. Conventional methods of monitoring wetlands and detecting changes over time can be time-consuming and costly. Inaccessibility and remoteness of many wetlands is also a limiting factor. Hence, there is a growing recognition of remote sensing techniques as a viable and cost-effective alternative to field-based ecosystem monitoring. Wetlands encompass a diverse array of habitats, for example, fens, bogs, marshes, and swamps. In this study, we concentrate on a natural wetland – Clara Bog, Co. Offaly, a raised bog situated in the Irish midlands. The aim of the study is to identify and monitor the environmental conditions of the bog using remote sensing techniques. Environmental conditions in this study refer to the vegetation composition of the bog and whether it is in an intact (peat-forming) or degraded state. It can be described using vegetation, the presence of water (soil moisture) and topography. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in properties of vegetation. This study uses mid-resolution data from Sentinel-2 MSI, Landsat 8 OLI for VI analysis. An initial study to delineate the boundary of the bog using the combination of edge detection and segmentation techniques namely, entropy filtering, canny edge detection, and graph-cut segmentation is performed. Once the bog boundary is defined, spectra of the delineated area are studied. VIs like NDVI, ARVI, SAVI, NDWI, derived using Sentinel-2 MSI and Landsat 8 OLI are analysed. A digital elevation model (DEM) was also used for better classification. All of these characteristics (features) serve as a basis for classifying the bog into broad vegetation communities (termed ecotopes) that indicate the quality of raised bog habitat. This analysis is validated using field derived ecotopes. The results show that, by using spectral information and vegetation index clustering, an additional linkage can be established between spectral RS signatures and wetland ecotopes. Hence, the benefit of the study is in understanding ecosystem (bog) environmental conditions and in defining appropriate metrics by which changes in the conditions can be monitored.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

“…The most common methods for mapping wetlands and surface waters that use Sentinel-2 mission data, or use Sentinel-1 and Sentinel-2 mission data fusion, are unsupervised classification [47,[208][209][210], supervised classification [47,209,211], change detection using vegetation indices [212,213], object-based classification [47,209,214], index-based classification [209], tile-based image thresholding [215], OTSU algorithm [200,203,216], and the rule-based super pixel (RBSP) approach [217]. In addition to these approaches, machine learning algorithms are being used more and more often.…”

“…Therefore Kaplan et al 2017 [209] propose a new approach that uses both object-based and index-based classifications, which as a result, gives a kappa value of 0.95 and as such is very suitable for mapping and change detection in wetlands. Bhatnagar et al [211] explore the applicability of pixels based on supervised classification, using Bagged Tree, Subspace KNN and SVM. The Bagged Tree classifier proved to be the best classifier for wetland detection with an accuracy of about 84%.…”

Development of Hybrid Models to Estimate Gross Primary Productivity at a Near-Natural Peatland Using Sentinel 2 Data and a Light Use Efficiency Model

“…This is important, as EC infrastructure is expensive, requires regular maintenance, and is limited to ecosystem scale perspectives, making direct measurements across multiple sites and spatial scales unfeasible. Because of this, the use of spectral indices as a proxy for carbon flux estimation are becoming more widely used [14,25,26].…”

“…Vegetation indices derived from satellite data, such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), have been correlated with GPP with varying degrees of success at grassland, cropland, and forested sites [14,16,27]. The use of common MODIS-derived vegetation indices (250-500 m), such as NDVI and EVI, can be challenging for GPP estimation over peatlands because of the narrow red absorption feature and narrow near-infrared reflectance peak which is commonly observed in dominant peat-forming species, such as Sphagnum mosses [25,28]. Additionally, vegetation indices derived from MODIS data might incorporate forestry and grasslands into the peatland signals due to the coarse spatial resolution (250 m-1000 m), leading to overestimation of GPP [29,30].…”

Ecohydrological metrics for vegetation communities in turloughs (ephemeral karstic wetlands)

“…Up to now, the mapping and monitoring of wetlands has largely been carried out manually by field visits to collect data, a process/ activity that is both time and resource intensive. In recent years remote sensing has been increasingly used to map and assess the ecological status of different wetlands: for example, Bourgeau-Chavez et al (2015) have used the seasonal Landsat-8 and microwave satellite data to map wetlands in north America; Dutta et al (2015) have used satellite data to find ecological disturbances in mangrove forests in India; Bhatnagar et al (2018Bhatnagar et al ( , 2020 have used satellite-RS along with spectral indices to map different vegetation communities on wetlands in Ireland. Equally, studies such as Diaz-Delgado et al (2019) have used both drones and satellite imagery for monitoring the ecological status of a marsh-ecosystem in Romania.…”