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Abstract Illicit cattle ranching and coca farming have serious negative consequences on the Colombian Amazon’s land systems. The underlying causes of these land activities include historical processes of colonization, armed conflict, and narco-trafficking. We aim to examine how illicit cattle ranching and coca farming are driving forest cover change over the last 34 years (1985–2019). To achieve this aim, we combine two pixel-based approaches to differentiate between coca farming and cattle ranching using hypothetical observed patterns of illicit activities and a deep learning algorithm. We found evidence that cattle ranching, not coca, is the main driver of forest loss outside the legal agricultural frontier. There is evidence of a recent, explosive conversion of forests to cattle ranching outside the agricultural frontier and within protected areas since the negotiation phase of the peace agreement. In contrast, coca is remarkably persistent, suggesting that crop substitution programs have been ineffective at stopping the expansion of coca farming deeper into protected areas. Countering common narratives, we found very little evidence that coca farming precedes cattle ranching. The spatiotemporal dynamics of the expansion of illicit land uses reflect the cumulative outcome of agrarian policies, Colombia’s War on Drugs, and the 2016 peace accord. Our study enables the differentiation of illicit land activities, which can be transferred to other regions where these activities have been documented but poorly distinguished spatiotemporally. We provide an applied framework that could be used elsewhere to disentangle other illicit land uses, track their causes, and develop management options for forested land systems and people who depend on them.

Remotely sensed data have the potential to monitor natural hazards and their consequences on socioeconomic systems. However, in much of the world, inadequate validation data of disaster damage make reliable use of satellite data difficult. We attempt to strengthen the use of satellite data for one application—flood index insurance—which has the potential to manage the largely uninsured losses from floods. Flood index insurance is a particularly challenging application of remote sensing due to floods’ speed, unpredictability, and the significant data validation required. We propose a set of criteria for assessing remote sensing flood index insurance algorithm performance and provide a framework for remote sensing application validation in data-poor environments. Within these criteria, we assess several validation metrics—spatial accuracy compared to high-resolution PlanetScope imagery (F1), temporal consistency as compared to river water levels (Spearman's ρ), and correlation to government damage data (R2)—that measure index performance. With these criteria, we develop a Sentinel-1 flood inundation time series in Bangladesh at high spatial (10 m) and temporal (∼weekly) resolution and compare it to a previous Sentinel-1 algorithm and a Moderate Resolution Imaging Spectroradiometer (MODIS) time series used in flood index insurance. Results show that the adapted Sentinel-1 algorithm (F1avg = 0.925, ρavg = 0.752, R2 = 0.43) significantly outperforms previous Sentinel-1 and MODIS algorithms on the validation criteria. Beyond Bangladesh, our proposed validation criteria can be used to develop and validate better remote sensing products for index insurance and other flood applications in places with inadequate ground truth damage data.

Illicit supply networks (ISNs) are composed of coordinated human actors that source, transit, and distribute illicitly traded goods to consumers, while also creating widespread social and environmental harms. Despite growing documentation of ISNs and their impacts, efforts to understand and disrupt ISNs remain insufficient due to the persistent lack of knowledge connecting a given ISN’s modus operandi and its patterns of activity in space and time. The core challenge is that the data and knowledge needed to integrate it remain fragmented and/or compartmentalized across disciplines, research groups, and agencies tasked with understanding or monitoring one or a few specific ISNs. One path forward is to conduct comparative analyses of multiple diverse ISNs. We present and apply a conceptual framework for linking ISN modus operandi to spatial-temporal dynamics and patterns of activity. We demonstrate this through a comparative analysis of three ISNs – cocaine, illegally traded wildlife, and illegally mined sand – which range from well-established to emergent, global to domestic in geographic scope, and fully illicit to de facto legal. The proposed framework revealed consistent traits related to geographic price structure, value capture at different supply chain stages, and key differences among ISN structure and operation related to commodity characteristics and their relative illicitness. Despite the diversity of commodities and ISN attributes compared, social and environmental harms inflicted by the illicit activity consistently become more widespread with increasing law enforcement disruption. Drawing on these lessons from diverse ISNs, which varied in their histories and current sophistication, possible changes in the structure and function of nascent and/or low salience ISNs may be anticipated if future conditions or law enforcement pressure change.

Monitoring floods is a major issue in water resources management and risk mitigation, especially in the Global South. Optical and radar observations, even providing a fine spatial resolution, are still limited by cloud cover interaction or insufficient temporal resolution. On the other hand, passive microwave (PMW) sensors collect information on a daily frequency with minor cloud cover interaction, but they have been historically limited in terms of spatial resolution. Here, we evaluate the capability of an enhanced spatial resolution PMW dataset (3.125 km) in monitoring spatio-temporal evolution of flood events, focusing on a major flood event that occurred in October 2005 in Bangladesh. We apply an algorithm aimed to remove the seasonal variability of surface temperature from the PMW timeseries, exploiting the difference in emissivity between dry and water-covered pixels. We assess the capability of the algorithm in capturing flood evolution and extension through the comparison with quantities obtained from optical data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and water level measurements. We also compare the enhanced product with the historical coarser resolution dataset by means of a variogram-based analysis to evaluate the improvements in terms of spatial representation. Finally, we evaluate the possibility to extract the water fraction within a single pixel by using an Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) emissivity dataset and compare the estimates with MODIS-derived water fractions. Our results show that the enhanced PMW product outperforms the coarser one when compared to flood mapped from optical data based on information content, indicating that it is possible to integrate such a product into the mapping of floods at a global scale on a daily basis.