Approximately 2.5 × 10 ⁶ square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year ⁻¹ ), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year ⁻¹ ). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

Landscape characteristics have been shown to influence health outcomes, but few studies have examined their relationship with cancer survival. We used data from the National Land Cover Database to examine associations between regional-stage colon cancer survival and 27 different landscape metrics. The study population included all adult New Jersey residents diagnosed between 2006 and 2011. Cases were followed until 31 December 2016 (N = 3949). Patient data were derived from the New Jersey State Cancer Registry and were linked to LexisNexis to obtain residential histories. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI95) for the different landscape metrics. An increasing proportion of high-intensity developed lands with 80–100% impervious surfaces per cell/pixel was significantly associated with the risk of colon cancer death (HR = 1.006; CI95 = 1.002–1.01) after controlling for neighborhood poverty and other individual-level factors. In contrast, an increase in the aggregation and connectivity of vegetation-dominated low-intensity developed lands with 20–<40% impervious surfaces per cell/pixel was significantly associated with the decrease in risk of death from colon cancer (HR = 0.996; CI95 = 0.992–0.999). Reducing impervious surfaces in residential areas may increase the aesthetic value and provide conditions more advantageous to a healthy lifestyle, such as walking. Further research is needed to understand how these landscape characteristics impact survival.

Recent regional research has taken an ‘infrastructure turn’ where scholars have called for examining the transformative ability of different infrastructures in causing systemic inequities beyond the spatial conception of ‘urban and the other’. This research examines the interconnected impact of infrastructure systems on existing spatial inequities through a study in metropolitan Philadelphia, Pennsylvania. This study investigates whether the urban-rural (U-R) gradient concept can enhance understanding of the spatial relationship between socioeconomic indicators and infrastructure systems. Indicators of spatial inequalities were regressed against infrastructure variables and imperviousness, as a proxy for the U-R gradient, using multivariate and spatial regression methods. The models show that imperviousness has a positive correlation with the concentration of racialized minorities and a negative correlation with access to health insurance. The study also shows that the predictive power of multiple infrastructures varies across space and does not adhere to urban boundaries or the U-R gradient. The complex interactions among different infrastructures shape inequities and require further inquiry in urban regions around the world.

Spatial resources accessible for the derivation of biodiversity indicators of the class ecosystem structure are sparse and disparate, and their integration into computer algorithms for biodiversity monitoring remains problematic. We describe ecochange as an R‐package that integrates spatial analyses with a monitoring workflow for computing routines necessary for biodiversity monitoring. The ecochange comprises three modules for data integration, statistical analysis and graphics. The first module currently downloads and integrates diverse remote sensing products belonging to the essential biodiversity class of structure. The module for statistical analysis calculates RasterStack ecosystem‐change representations across areas of interest; this module also allows focusing on species habitats while deriving changes in a variety of indicators, including ecosystem areas, conditional entropy and fractal dimension indices. The graphics module produces level and bar plots that ease the development of indicator reports. Its functionality is described with an example workflow to calculate ecosystem‐class areas and conditional entropy across an area of interest contained in the package documentation. We conclude that ecochange features procedures necessary to derive ecosystem structure indicators integrating the retrieval of spatially explicit data with the use of workflows to calculate/visualize biodiversity indicators at the national/regional scales.

Change assessment is a central and active area of inquiry in remote sensing. Broadly adopted probabilistic methods discriminate between change and no change based on image differencing, normalization and aggregation into a single band metric that is assumed to follow a Chi-square distribution. The adoption of the Chi-square distribution requires the application of band transformation to original data that is computer expensive and that operates under an untested assumption of multivariate distribution of pixel values in input bands. Despite the wide adoption of the Chi-square distribution, its appropriateness for discriminating between change and no change remains an open question. Here, we test the performance of the Chi-square distribution for change assessment compared to the use of the more-generic Gamma distribution. For this purpose, we implement an algorithm that iteratively removes observations labelled as change according to a pre-defined probabilistic distribution and a probability change threshold. We implement the algorithm in three study areas in tropical and subtropical regions representing contrasting ecological conditions and land cover types and changes. We also test whether input multispectral data meets the assumption of multivariate normality required for band transformation and for the use of the Chi-square distribution. We found that the Gamma distribution applied to untransformed data consistently performs more robustly to discriminate between change and no change compared to the application of band transformation and subsequent use of the Chi-square distribution. We also found that, in none of the evaluated cases, input multispectral data meet the assumption of multivariate normality required for band transformation. Our results suggest that assumptions about multivariate normality can affect the robustness of probabilistic change assessment in multispectral remote sensing. We encourage the remote sensing community to adopt the Gamma distribution applied to untransformed data as a probabilistic approach to differentiate between change and no change.