Exploring forest regrowth dynamics in the Peruvian Amazon

By Lynn Barmentlo

Abstract
Deforestation of Amazon rainforest is an increasingly relevant issue, with monitor efforts relying on remote sensing data. Whereas forest disturbance is relatively easy to detect due to its abrupt character, regrowth is more difficult to detect and thus often overlooked. This thesis aims to contribute to a better understanding of forest regrowth dynamics in the Peruvian Amazon, with the Indigenous village of Panjuy and its surrounding areas as case study. Swidden farming is a common practise in the study area, creating a diverse and dynamic forested landscape. Through a systematic literature review, the AVOCADO algorithm was found to be a promising method for tracking forest dynamics in the rapidly changing landscape of Panjuy. Combined with Landsat data from 1984 to 2024, using a strict and a broad definition of regrowth, the AVOCADO algorithm was used to map yearly forest disturbance and regrowth in the study area. The results revealed high rates of disturbance in the entire study area. At the same time, the algorithm showed significantly high rates of regrowth as well. Comparing peaks of disturbance and regrowth throughout the timeseries with expert knowledge on the history of land use showed parallels to potential drivers of land use change, such as an increasing or decreasing popularity of specific crops, or an increasing pressure on the land due to a growing population. Notably, extreme weather events seemed to have an influence on the temporal patterns of regrowth and disturbance as well. The community of Panjuy showed higher proportions of regrowing forest compared to the surrounding area, which could be attributed to the practice of swidden farming compared to mare intensified agriculture. The results of this thesis highlight the importance of including not only forest disturbance, but also regrowth in forest monitoring efforts. Furthermore, it shows the value of integrating expert knowledge to enhance the interpretation of remote sensing-based results, by identifying underlying drivers of change.