Monitoring Deforestation in Rainforests Using Satellite Data: A Pilot Study from Kalimantan, Indonesia

Abstract

Monitoring large forest areas is presently feasible with satellite remote sensing as opposed to time-consuming and expensive ground surveys as alternative. This study evaluated, for the first time, the potential of using freely available medium resolution (30 m) Landsat time series data for deforestation monitoring in tropical rainforests of Kalimantan, Indonesia, at sub-annual time scales. A simple, generic, data-driven algorithm for deforestation detection based on a consecutive anomalies criterion was proposed. An accuracy assessment in the spatial and the temporal domain was carried out using high-confidence reference sample pixels interpreted with the aid of multi-temporal very high spatial resolution image series. Results showed a promising spatial accuracy, when three consecutive anomalies were required to confirm a deforestation event. Recommendations in tuning the algorithm for different operational use cases were provided within the context of satisfying REDD+ requirements, depending on whether spatial accuracy or temporal accuracy need to be optimized.

Estimation and Study of Forest Loss and Gain Using Spatial Dataset across Districts of Uttarakhand

“…The four commonly used broadband vegetation indices (VI) were initially examined, namely the normalized difference vegetation index (NDVI) [7], enhanced vegetation index (EVI) [8], normalized difference moisture index (NDMI), also known as normalized difference water index [9] and normalized burn ratio (NBR) [10]. It was observed that NDMI shows high sensitivity (most clear signal) in response to deforestation events in the study area, with signal change magnitude most visibly larger than ephemeral noise [11] NDMI was calculated as follows:…”

Spatial–temporal variations in deforestation hotspots in Sumatra and Kalimantan from 2001–2018

“…For instance, the conversion to oil palm plantations has been identified as a driver of forest loss both outside and within the boundaries of the Gunung Leuser National Park in Sumatra (Supriatna et al, 2017). Similarly, deforestation has occurred both outside and within the borders of Kalimantan's PAs (Krasovskii et al, 2018). These too are threatened by illegal logging and oil palm expansion (Nellemann, 2007).…”

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

“…We calculated OA, PA and UA in each disturbance detection (i.e., only with Sentinel-1, only with Landsat 8 and combining Landsat 8 and Sentinel-1). We treated disturbances detected before the actual disturbance date as commission errors of disturbance, in accordance with other studies (e.g., References [20,21,35]). However, disturbances detected within 10 days before the actual date were regarded as correct detections, because the visual interpretation had an uncertainty of 9.1 days (the weighted average interval of available PlanetScope and RapidEye images used in the visual interpretation).…”

“…Dense time series satellite data can provide more detailed information than bi-temporal or annual satellite data [19][20][21][22]. The use of temporally dense satellite data to detect forest disturbances is particularly suitable in the tropics, because rapid vegetation recovery can obscure the signs of disturbance events [23,24].…”