The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability sampling design in 54% of the papers. For approximately 11% of the studies, the sampling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible; that is, they included a probability-based sampling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and sampling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.

The difficulty of defining and quantifying forest degradation is a major constraint in the implementation of the international mitigation mechanism Reduced Emissions from Deforestation and Forest Degradation (REDD+). Our aim is to develop an operational framework for defining and quantifying forest degradation at a local level for early REDD+ projects and for national REDD+ programmes, through a ground level approach. We critically review and discuss national and international definitions of forest and of forest degradation, and then analyze the main difficulties in making these operational, evaluating the key elements and threshold values that are used, and contextualizing them using Mexico as a case study. We conclude that, given the lack of historical biomass data and the limited capability for monitoring degradation using remote sensing, forest degradation is best measured against a local benchmark that represents areas of low or no degradation that have comparable biophysical characteristics. Use of benchmarks of this type could offer a quick-start option for local assessment and construction of reference levels for forest degradation. These could be refined as more data become available and could eventually be integrated into national monitoring systems.

Global commodity chains reshape rural territories through interconnected environmental and socioeconomic processes; these dynamics are often examined through the lens of land-use change. Export-oriented agricultural frontiers illustrate how global market integration can generate landscape and societal transformations. In western Mexico, the expansion of the avocado industry exemplifies commodity-driven territorial change, linking land-use dynamics with transformations in rural economies and livelihoods. This study analyzes how export integration into the international avocado commodity chain was associated with transformations in land systems and socioeconomic dynamics across four municipalities in Michoacán, Mexico, between 2003 and 2023. Using high-resolution land-use maps alongside demographic and socioeconomic indicators, we apply a qualitative, comparative and inductive synthesis of spatial and census data to examine how commodity integration interacted with local conditions to shape landscape and territorial organization. Avocado expansion coincided with territorial transformation, including the conversion of pine-oak forest and traditional agricultural lands, as well as changes in labor arrangements, demographic dynamics, and rural livelihood systems. Land-use analysis shows continued orchard expansion and accelerated land conversion after 2015. Socioeconomic indicators reveal population growth, increased female employment, and persistent income inequality, highlighting the uneven incorporation of rural territories into global commodity chains. We identified four territorial pathways: agricultural specialization, agro-industrial urbanization, transitional diversification, and rural commodity integration. These findings contribute to debates on uneven geographical development and commodity frontier expansion by showing how global commodity integration intersects with ecological, economic, and social processes to generate distinct pathways.