Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal

Abstract

While deforestation has traditionally been the focus for forest canopy disturbance detection, forest degradation must not be overlooked. Both deforestation and forest degradation influence carbon loss and greenhouse gas emissions and thus must be included in activity data reporting estimates, such as for the Reduced Emissions from Deforestation and Degradation (REDD+) program. Here, we report on efforts to develop forest degradation mapping capacity in Nepal based on a pilot project in the country’s Terai region, an ecologically complex physiographic area. To strengthen Nepal’s estimates of deforestation and forest degradation, we applied the Continuous Degradation Detection (CODED) algorithm, which uses a time series of the Normalized Degradation Fraction Index (NDFI) to monitor forest canopy disturbances. CODED can detect low-grade degradation events and provides an easy-to-use graphical user interface in Google Earth Engine (GEE). Using an iterative process, we were able to create a model that provided acceptable accuracy and area estimates of forest degradation and deforestation in Terai that can be applied to the whole country. We found that between 2010 and 2020, the area affected by disturbance was substantially larger than the deforested area, over 105,650 hectares compared to 2753 hectares, respectively. Iterating across multiple parameters using the CODED algorithm in the Terai region has provided a wealth of insights not only for detecting forest degradation and deforestation in Nepal in support of activity data estimation but also for the process of using tools like CODED in applied settings. We found that model performance, measured using producer’s and user’s accuracy, varied dramatically based on the model parameters specified. We determined which parameters most altered the results through an iterative process; those parameters are described here in depth. Once CODED is combined with the description of each parameter and how it affects disturbance monitoring in a complex environment, this degradation-sensitive detection process has the potential to be highly attractive to other developing countries in the REDD+ program seeking to accurately monitor their forests.

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon

“…First, to calculate the Land Cover/Land Use and Landscape Integrity indicators, we created two map products. While the methods used to create these map products have significant support in the literature [e.g., 51 , 141 , 142 ], we found that applying them in this specific context had wide margins of error and some unexpected results that provided an opportunity to learn and improve upon these methods. For example, our disturbance maps, which were used for LCLU Indicator 2, showed omission errors in the stable forest class.…”

“…Deforestation was defined as a permanent conversion of forested land to non-forested land. Degradation was defined as a process that does not lead to a change in land cover but shows a reduction in tree cover canopy [ 50 , 51 ]. The changes were mapped for the 5-year period 2010–2015, when most of the interventions occurred.…”

“…The spectral index of choice was the NDFI to be in accordance with CODED and since previous studies have shown that NDFI is more sensitive to disturbances in tropical forests compared to the commonly used Normalized Difference Vegetation Index (NDVI), which tends to show higher variability [ 46 – 47 , 53 ]. We used 3,000 training points labeled as forest or non-forest and tested chi-squared values of both 0.9 and 0.99, which controls the width of the change-detection moving time window [ 51 ]. We defined the required number of sequential out-of-range NDFI values to flag an event as four times.…”

“…Pixels with classification disagreements between maps were reclassified according to the following rules: if at least one of the maps classified the pixel as degradation, we set the merged layer as degradation; if neither of the maps classified a pixel as degradation and at least one classified it as deforestation we set the merged layer value to deforestation; and if both maps classified the pixel as stable forest we left it as stable forest. We used this rule system because degradation was underestimated in previous studies [ 51 , 55 , 58 ].…”

Environmental correlates of the forest carbon distribution in the Central Himalayas

“…patches with a closed canopy (Frolking et al 2009). Human disturbances such as selective tree removal, which is common in Nepal, would thus reduce tree cover (Shrestha et al 2013, Aryal et al 2021) and, therefore, forest carbon stock. In contrast, if these sites naturally have sparse tree cover or only a fraction of the patches with tree cover, we would likely expect these sites to be adjacent to other land uses and have a higher probability of disturbance.…”