Forest cover dynamics of shifting cultivation in the Democratic Republic of the Congo 2000–2010 (2015 Environ. Res. Lett. 10 094009)

Abstract

An error in the unit conversion from pixels to hectares lead to all the areal quantities in the text being smaller than they should have been. Only the number of hectares were changed; none of the text nor tables were changed. The changes do not affect the overall results or conclusions. Abstract

An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

“…This is an indication of the increasing reliance of the local community in the coast region on agriculture as the main source of livelihood in light of the ongoing conflict since mid-2011. This interpretation is consistent with other studies around the world that have dealt with deforestation in different regions of the world over different time periods, as these studies reported that the change in social and economic factors was the main reason for the decline or deterioration of forest cover [22,27,30,32]. This finding is also in agreement with studies on the changing environment during armed conflicts in other regions around the world, which identified livelihood activities by local and displaced people and unsupervised settlements as the main causes of forest loss [47,[91][92][93].…”

“…Therefore, forest lands are grabbed and cleared despite their ownership by the state, in an attempt by these populations to own small scale farms (plantations) for subsistence and for agricultural production. Similar scenarios have occurred in other regions around the world [7,13,22,27,32].…”

“…In recent years, many studies have been conducted using multitemporal satellite images to monitor and map changes in forest cover around the world. These studies used developed methodologies for this purpose, such as pixel-based classification analyses [1,3,8,13,15,[20][21][22][23][24][25][26][27], vegetation indices analysis [6,20,28,29], and object-based or object-oriented classification analyses [7,9,18,20,21,[30][31][32][33][34]. Meanwhile, many studies based on remotely sensed data and GIS techniques have been conducted to study forest degradation in different regions around the world, such as those studies that used Landsat images and different vegetation indices to estimate the impact of selective logging, forest fires and the intense production of coal on the loss of forest cover as well as to map degraded forests [35][36][37][38][39][40][41].…”

Classifying drivers of global forest loss

“…Although we accurately mapped dominant classes of forest disturbance globally, opportunities remain to disaggregate landscapes further at regional and local scales. For example, we did not map changes in forest condition through time in landscapes dominated by shifting agriculture; further differentiation of primary from secondary forest clearing within this land-use class could improve our understanding of the differences between deforestation and degradation impacts (11). Differentiating key drivers such as row crops from pasturelands in South America, or tree plantations from disturbed natural forests in Southeast Asia (12), would allow for more specific supply chain analyses to identify corporate risk and responsibility from commodity-driven deforestation.…”

The Shifting Cultivation Juggernaut: An Attribution Problem

“…Similarly, empirical studies in Africa show that shifting cultivation in some areas have a lower impact and a smaller footprint than conventional practices. [ 46 ] We believe that the abandonment of traditional ways of life of indigenous peoples due to the misconceptions around shifting cultivation would be a loss for the larger society.…”