As climate change continues to exert increasing pressure upon the livelihoods and agricultural sector of many developing and developed nations, a need exists to understand and prioritise at the sub national scale which areas and communities are most vulnerable. The purpose of this study is to develop a robust, rigorous and replicable methodology that is flexible to data limitations and spatially prioritizes the vulnerability of agriculture and rural livelihoods to climate change. We have applied the methodology in Vietnam, Uganda and Nicaragua, three contrasting developing countries that are particularly threatened by climate change. We conceptualize vulnerability to climate change following the widely adopted combination of sensitivity, exposure and adaptive capacity. We used Ecocrop and Maxent ecological models under a high emission climate scenario to assess the sensitivity of the main food security and cash crops to climate change. Using a participatory approach, we identified exposure to natural hazards and the main indicators of adaptive capacity, which were modelled and analysed using geographic information systems. We finally combined the components of vulnerability using equal-weighting to produce a crop specific vulnerability index and a final accumulative score. We have mapped the hotspots of climate change vulnerability and identified the underlying driving indicators. For example, in Vietnam we found the Mekong delta to be one of the vulnerable regions due to a decline in the climatic suitability of rice and maize, combined with high exposure to flooding, sea level rise and drought. However, the region is marked by a relatively high adaptive capacity due to developed infrastructure and comparatively high levels of education. The approach and information derived from the study informs public climate change policies and actions, as vulnerability assessments are the bases of any National Adaptation Plans (NAP), National Determined Contributions (NDC) and for accessing climate finance.

Monitoring forest–agriculture mosaics is crucial for understanding landscape heterogeneity and managing biodiversity. Mapping these mosaics from remotely sensed imagery remains challenging, since ecological gradients from forested to agricultural areas make characterizing vegetation more difficult. The recent synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a great opportunity to monitor forest–agriculture mosaics due to their high spatial and temporal resolutions. However, while a few studies have used the temporal resolution of S-2 time series alone to map land cover and land use in cropland and/or forested areas, S-1 time series have not yet been investigated alone for this purpose. The combined use of S-1 & S-2 time series has been assessed for only one or a few land cover classes. In this study, we assessed the potential of S-1 data alone, S-2 data alone, and their combined use for mapping forest–agriculture mosaics over two study areas: a temperate mountainous landscape in the Cantabrian Range (Spain) and a tropical forested landscape in Paragominas (Brazil). Satellite images were classified using an incremental procedure based on an importance rank of the input features. The classifications obtained with S-2 data alone (mean kappa index = 0.59–0.83) were more accurate than those obtained with S-1 data alone (mean kappa index = 0.28–0.72). Accuracy increased when combining S-1 and 2 data (mean kappa index = 0.55–0.85). The method enables defining the number and type of features that discriminate land cover classes in an optimal manner according to the type of landscape considered. The best configuration for the Spanish and Brazilian study areas included 5 and 10 features, respectively, for S-2 data alone and 10 and 20 features, respectively, for S-1 data alone. Short-wave infrared and VV and VH polarizations were key features of S-2 and S-1 data, respectively. In addition, the method enables defining key periods that discriminate land cover classes according to the type of images used. For example, in the Cantabrian Range, winter and summer were key for S-2 time series, while spring and winter were key for S-1 time series.

In the agricultural frontiers of Brazil, the distinction between forested and deforested lands traditionally used to map the state of the Amazon does not reflect the reality of the forest situation. A whole gradient exists for these forests, spanning from well conserved to severely degraded. For decision makers, there is an urgent need to better characterize the status of the forest resource at the regional scale. Until now, few studies have been carried out on the potential of multisource, freely accessible remote sensing for modelling and mapping degraded forest structural parameters such as aboveground biomass (AGB). The aim of this article is to address that gap and to evaluate the potential of optical (Landsat, MODIS) and radar (ALOS-1 PALSAR, Sentinel-1) remote sensing sources in modelling and mapping forest AGB in the old pioneer front of Paragominas municipality (Para state). We derived a wide range of vegetation and textural indices and combined them with in situ collected AGB data into a random forest regression model to predict AGB at a resolution of 20 m. The model explained 28% of the variance with a root mean square error of 97.1 Mg·ha −1 and captured all spatial variability. We identified Landsat spectral unmixing and mid-infrared indicators to be the most robust indicators with the highest explanatory power. AGB mapping reveals that 87% of forest is degraded, with illegal logging activities, impacted forest edges and other spatial distribution of AGB that are not captured with pantropical datasets. We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. IntroductionDeforestation and forest degradation are major sources of greenhouse gas emissions [1,2], contributing to forest carbon losses [3], global climate change, affecting biodiversity [4] and the entire forest ecosystem. While deforestation refers to the rapid conversion from forest to non-forest areas, degradation implies changes in the forest structure with no change in land use [5,6]. In Amazonia, over the last decades, deforestation and forest degradation have shaped the rural landscape, resulting in a complex mosaic of fragmented forests associated with agricultural lands [7]. A total area of 766,448.5 km 2 was cleared in 2015 [8], representing 20% of Amazonia [9,10].Since 2005, deforestation in Brazil has drastically decreased thanks to coercive measures taken by the Brazilian government associated with private initiatives (soy and beef moratoria), among other factors [11]. However, these measures are not effective for reducing forest degradation [12,13]. Most of the remaining forested lands are degraded due to the accumulation over time and space of severe degradation processes mainly triggered by anthropogenic impacts through unsustainable logging practices, fire, shifting cultivation ...

Abstract:In the Brazilian Amazon, multiple logging activities are undergoing, involving different actors and interests. They shape a disturbance gradient bound to the intensity and frequency of logging, and forest management techniques. However, until now, few studies have been carried out at the landscape scale taking into account these multiple types of logging and this disturbance gradient. Here we address this issue of how to account for the multiple logging activities shaping the current forest landscape. We developed an inexpensive and efficient remote sensing methodology based on Landsat imagery to detect and track logging activity based on the monitoring of canopy openings. Then, we implemented a set of remote sensing indicators to follow the different trajectories of forest disturbance through time. Using these indicators, we emphasized five major spatial and temporal disturbance patterns occurring in the municipality of Paragominas (State of Pará, Brazilian Amazon), from well-managed forests to highly over-logged forests. Our disturbance indicators provide observable evidence for the difference between legal and illegal patterns, with some illegal areas having suffered more than three explorations in fifteen years. They also clearly underlined the efficiency of Reduced Impact Logging (RIL) techniques applied under Forest Stewardship Council (FSC) guidelines to reduce the logging impacts in terms of canopy openings. For these reasons, we argue the need to promote legal certified logging to conserve forests, as without them, many actors mine the forest resources without any concerns for future stocks. Finally, our remote tracking methodology, which produces easy to interpret disturbance indicators, could be a real boon to forest managers, including for conservationists working in protected areas and stakeholders dealing with international trade rules such as RBUE (Wood regulation of European Union) or FLEGT (Forest Law for Enforcement, Governance and Trade).

16Degraded tropical forests dominate agricultural frontiers and their management is becoming an 17 urgent priority. This calls for a better understanding of the different forest cover states and cost-18 efficient techniques to quantify the impact of degradation on forest structure. Canopy texture 19 analyses based on Very High Spatial Resolution (VHSR) optical imagery provide proxies to assess forest 20 structures but the mechanisms linking them with degradation have rarely been investigated. To 21 address this gap, we used a lightweight Unmanned Aerial Vehicle (UAV) to map 739 ha of degraded 22 forests and acquire both canopy VHSR images and height model. Thirty-three years of degradation 23 history from Landsat archives allowed us to sample 40 plots in undisturbed, logged, over-logged and 24 burned and regrowth forests in tropical forested landscapes (Paragominas, Pará, Brazil). Fourier 25 (FOTO) and lacunarity textures were used to assess forest canopy structure and to build a typology 42 43

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Question Tropical forests are subject to disturbances by logging, gathering of fuelwood, and fires. Can degradation trajectories (i.e. cumulative disturbances events over a period of timer) be identified using remote‐sensing Landsat time series? Location Paragominas (Pará, Brazil), a municipality covering 19,395 km² in the north‐eastern Amazon. Methods We used Landsat annual imagery from 2000 to 2015 and spectral mixture analysis to derive time series of the fraction of soil (S), active photosynthetic vegetation (PV), and non‐photosynthetic vegetation (senescent) (NPV) indicators. Results The NPV values over a 16‐year period revealed five different degradation trajectories (i.e., cumulative disturbances in space and over time): undisturbed forest, selectively logged forest (with a management plan), overlogged forest (no management plan), overlogged forest (charcoal production) and burned forest. The variance of NPV calculated per pixel over the same period is useful to map forest degradation over Paragominas municipality, highlighting the role of disturbance factors (logging, fuelwood gathering and fire). Conclusions The fractional cover of NPV obtained from spectral mixture analysis can be used to differentiate degradation trajectories and to map forest degradation.

The phenology of tropical forests is tightly related to climate conditions. In the Amazon, the seasonal greening of forests is conditioned by solar radiation and rainfall. Yet, increasing anthropogenic pressures (e.g. logging and wildfires), raise concerns about the impacts of forest degradation on the functioning of forest ecosystems, especially in a climate change context. In this study, we relied on remote sensing data to assess the contribution of solar radiation and precipitation to forest greening in mature and fire degraded forests, with a focus on the 2015 drought event. Our results showed that forest greening is more dependent on water resources in degraded forests than in mature forests. As a consequence, the expected increase in drought episodes and associated fire occurrences under climate change could lead to a long-term drying of tropical forests.