Peatland degradation: The relationship between raised bog hydrology and normalized difference vegetation index

Abstract

Peatlands are recognized as important landscape elements and their disturbance, followed by the loss of their ecohydrological functions, leads to falling water tables and degradation. Sometimes, the shortage of hydrological monitoring data impedes the detection of degrading peatland areas. The application of cost-effective remote sensing methods for assessment of humidity conditions permits monitoring of a large area and longer periods of time. Therefore, in this study, we assessed the links between hydrological parameters and the normalized difference vegetation index (NDVI) as well as identifying spatio-temporal patterns in the condition of raised bog vegetation, according to NDVI, in the Čepkeliai raised bog, a semipristine peatland in south-eastern Lithuania. Time series of NDVI (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) were obtained from the moderate resolution imaging spectroradiometer and compared with field measurements. A strong positive correlation between NDVI and water table depth was found. Moderate (for the whole raised bog) and strong (for control subbasin) positive correlations between NDVI avg and annual minimum flow over 30 and 7 days were found. This confirms that NDVI could be treated as an appropriate indicator, showing the degradation processes of the raised bog ecosystem and could be applied in its management. Analysis of the interannual NDVI avg variation confirmed the prevalence of stable favourable conditions during the growing season with a slight decrease in NDVI max over last 4 years. Meanwhile, analysis of spatial NDVI variation revealed obvious spatial patterns where possible degradation processes tend to appear. KEYWORDSNDVI, peatland degradation, peatland water resources, raised bog, water table depth

Impact of Hydro-Climatic Changes on Coastal Dunes Landscape According to Normalized Difference Vegetation Index (The Case Study of Curonian Spit)

“…NDVI values increase or remains in the same level from July up to September. There is no decrease in NDVI values in August-September period, like it is observed in the other Lithuanian regions [25]. The maritime climate influences the longer growing season in the Curonian spit.…”

“…There are few research studies related to NDVI responses to hydrological factors. Relationships between NDVI and hydrological values are used to assess the forest ecosystem response to changes in the groundwater lens [23] as well as groundwater flow discharge in wetland ecosystem [24] and raised bog ecosystem degradation patterns [25]. Promising opportunities for identifying potential groundwater dependent vegetation using NDVI [26] as well as riparian vegetation NDVI relationship with climate, surface water and groundwater [17] have been reported.…”

Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

“…Beyond confirming the temperature-GWL-SM relationship, the RF provided insight into the usefulness and the impact of NDVI, as well as RGB spectral indices (BI, CI, GLI, RI, HUE, NDI, SHP, and ERGBVE), directly representing proxies for the estimation of vegetation response towards changes in hydroclimatic conditions [53,75,76]. Moreover, these predictors can be considered appropriate for assessing vegetation conditions in peatlands to characterize temporal and spatial patterns and the degradation process of a peat bog ecosystem [77], primarily NDVI, NDI, ERGBVE, SHP, and GLI. Interestingly, the predictors BI, CI, RI, and HI are important; thus, there seems to be a relationship between these factors, GWL and SM.…”

Peat Forest Health Analysis on Landsat 8 Oli / Tirs Imagery Using Ndvi Method in Kotawaringin Timur Regency

“…The formula for calculating the NDVI value is expressed by the following equation (Lufilah et al, 2017;Carlson and Ripley, 1997;Šimanauskienė et al, 2019 The atmospheric disturbance is one of the weaknesses in using the NDVI algorithm. Atmospheric disturbances can cause NDV values to be very low.…”