Why do environmental defenders continue to participate in community-based efforts to protect the environment despite threats to their lives and minimal success in deterring or punishing offenders? Examining Community Forestry initiatives in Cambodia, we find that forest protection is not only a means to gain environmental, economic, or social benefits, but also a means for forest defenders to assert themselves as political actors in relation to the state and other citizens. Rather than simply repressing and disciplining forest dwelling populations, violence against defenders shapes their subjectivity and re-politicizes their lives. Distinguishing between various forms of violence associated with different illicit forest users, we highlight how forest protection becomes a means of political self-expression and rights-based resistance as forest defenders reinterpret direct violence through experiences and understandings of structural and symbolic violence. Participation in CF thus unlocks ways of being political and contesting oppressive structures while acting within sanctioned institutions. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
