Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series


Tropical dry forests provide globally important ecosystem services and host exceptionally high biodiversity. These biomes are currently under immense pressure, particularly for conversion to agriculture, and already experience high global deforestation rates. Miombo forests in Southern Angola are affected by deforestation, fragmentation and degradation, caused mainly by an increasing rural population who follows a traditional farming system of shifting cultivation with slash-and-burn agriculture. After the termination of the civil war in 2002, population growth and resettlements have accelerated the use of woody resources, selective logging and clearing for cultivation purposes and led to an exceedance of sustainability thresholds. Large scale projects are expected to put further pressure on the forests and increase the potential of conflicts regarding land resources and competition with local subsistence farming. We use an existing time series segmentation tool (LandTrendr) with a time series of Normalized Burn Ratio (NBR) data in combination with adapted temporal metrics to provide information about the dynamics of different cultivation patterns, to gain insight into historical developments and to assess temporal cultivation characteristics. We define cleared areas and cultivation time on a pixel-by-pixel basis providing temporal and spatial information on current and past changes from 1989 to 2013 using data from Landsat 5–8. Overall accuracy for the disturbance detection is 72%. We can follow the effect of armed conflicts on agricultural expansion with a drop in deforestation rate of more than 70% from 12,000 to 4000 ha per year (1994–1998) and subsequently tripling to 12,000 ha per year again after 2002. Deforestation patterns are in accordance with previous multi-temporal studies, although time series segmentation reveals more detailed information on deforestation and cultivation dynamics. We successfully separate areas of different historic backgrounds and agricultural dynamics, e.g. areas that were severely affected during the civil war, which transition from shifting to semi-permanent and permanent systems. We provide recommendations for the assessment of agricultural dynamics in similar areas where ground data and basic information is missing.

Remote Sensing of Environment