Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia

Abstract

This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of 'self-referencing' normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ∆rNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e.g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).

Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon

“…Against this background, this study has two main objectives: (1) The mapping of forest disturbances caused by selective logging in seven sites under sustainable forest management (SFM) within the Brazilian State of Amazonas and (2) the comparison of the effectiveness of Landsat 8 data versus Sentinel-2 imagery for such purpose. To accomplish these objectives, we applied the "∆rNBR approach"-a modification of the normalized burn ratio (NBR) index-which has successfully been used to map forest disturbances in (semi-)evergreen forests in continental Southeast Asia [50]. Instead of working with single satellite scenes, this approach uses a time series analysis of all available satellite images and calculates the changes between two periods.…”

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

“…Passive optical remote sensing assessments at resolutions as fine as 20-30 m have been used to classify logged forests with moderate accuracy. However, they have struggled to quantify the extent, intensity, and duration of logging damage because of the lack of regular cloud-free repeat observations and because rapid canopy regeneration masks logging effects after as few as three years [25][26][27]. Moreover, a 30 m spatial resolution is not sufficient to resolve mortality of a single canopy tree [28].…”

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

“…Landsat TOA reflectance-based EVI values were generally higher than surface reflectance (SR) based values [78] and therefore TOA data showed greater potential to provide accurate phenology-based classification [53,79,84,85]. Several studies have successfully applied TOA data for mapping the paddy rice [63,78,79,86,87] and mapping the vegetation distribution [17,22,53,84,88] using phenology-based methods. These studies have suggested that TOA data can be used for mapping with higher accuracy.…”

“…In recent years, GEE has been widely used for global-scale applications such as characterizing global forest cover change; forest expansion, loss, and gain from 2000 using large collections of Landsat scenes [5]; and crop yield estimation [21,22]. Other studies have also confirmed the ease of integrating various sources of temporal satellite imagery data and automating image classification routines for vegetation and land cover mapping using the GEE [17,[21][22][23][24][25][26].…”

“…Consequently, surface and vegetation phenology-based methods in tropical regions represent potentially significant sources of land cover information and may provide more accurate classification maps [29,30]. Several studies have attempted to classify the land use categories using the threshold-based approach by applying phenology methods with the aid of remote sensing data [6,22,27,[31][32][33][34][35][36]. The combined remote sensing and vegetation phenology approaches can measure heterogeneous seasonality in vegetation, because remote sensing can provide time series data at any scale; therefore, these approaches are essential for understanding the role of vegetation in climate change [17,[37][38][39][40][41][42].…”