Detecting abrupt change in land cover in the eastern Hindu Kush region using Landsat time series (1988–2020)

Abstract

Land cover change in the semi-arid environment of the eastern Hindu Kush region is driven by anthropogenic activities and environmental change impacts. Natural hazards, such as floods presumably influenced by climatic change, cause abrupt change of land cover. So far, little research has been conducted to investigate the spatiotemporal aspects of this abrupt change in the valleys. In order to explore the abrupt change in land cover and floods as its possible drivers in the eastern Hindu Kush, a semi-arid mountain region characterized by complex terrain, vegetation variation, and precipitation seasonality, we analyzed long-term Landsat image time series from 1988 to 2020 using Breaks For Additive Seasonal and Trend (BFAST). Overall, BFAST effectively detected abrupt change by using Landsat-derived Modified Soil Adjusted Vegetation Index (MSAVI). The results of our study indicate that approximately 95% of the study area experienced at least one abrupt change during 1988–2020. The years 1991, 1995, 1998, 2007, and 2016 were detected as the peak years, with the peaks occurring in different seasons. The annual trend of abrupt change is decreasing for the study area. The seasonality of abrupt change at the catchment level shows an increasing trend in the spring season for the southern catchments of Panjkora and Swat. The spatial distribution patterns show that abrupt change is primarily concentrated in the floodplains indicating that flooding is the primary driver of the land cover change in the region. We also demonstrated the accurate detection of past flood events (2015) based on the two case examples of Ayun, Rumbur, and Kalash valleys. The detection of the flood events was verified by fieldwork and historical high-resolution Google Earth imagery. Finally, our study provides an example of applying Landsat time series in a dry mountain region to detect abrupt changes in land cover and analyze impact of natural hazards such as floods.

Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia

“…The BFAST algorithm was applied for all pixels in the study area through the bfast package [62] using R program version 4.2.2. The irregular time series were extracted, transformed into daily time series, and finally to monthly time series [63]. BFAST was run using the dummy model that focuses on trend change detection rather than temporal shifts in land surface phenology.…”

“…The BFAST algorithm was applied for all pixels in the study area through the bfast package [63] using R program version 4.2.2. The irregular time series were extracted, transformed into daily time series, and finally to monthly time series [64].…”

Disaster risk reduction in mountain areas: a research overview

“…Changes in land cover are significant factors influencing hillslope instability and water balance changes. This is the case of the eastern Hindu Kush region, a semi-arid mountain region characterized by complex terrain, vegetation variation, and precipitation seasonality, which was analysed by Khan et al (2022). Using Landsat time series from 1988 to 2020, they recognise the significant change in land cover and floods and the consequent occurrence of debris flows and river erosion.…”