Increasing demands for livelihood resources in tropical rural areas have led to progressive clearing of biodiverse natural forests. Restoration of abandoned farmlands could counter this process. However, as aims and modes of restoration differ in their ecological and socio-economic value, the assessment of achievable ecosystem functions and benefits requires holistic investigation. Here we combine the results from multidisciplinary research for a unique assessment based on a normalization of 23 ecological, economic and social indicators for four restoration options in the tropical Andes of Ecuador. A comparison of the outcomes among afforestation with native alder or exotic pine, pasture restoration with either low-input or intense management and the abandoned status quo shows that both variants of afforestation and intense pasture use improve the ecological value, but low-input pasture does not. Economic indicators favour either afforestation or intense pasturing. Both Mestizo and indigenous Saraguro settlers are more inclined to opt for afforestation.

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data.

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Diatoms are the major marine primary producers on the global scale and, recently, several methods have been developed to retrieve their abundance or dominance from satellite remote sensing data. In this work, we highlight the importance of the Southern Ocean (SO) in developing a global algorithm for diatom using an Abundance Based Approach (ABA). A large global in situ data set of phytoplankton pigments was compiled, particularly with more samples collected in the SO. We revised the ABA to take account of the information on the penetration depth (Z pd ) and to improve the relationship between diatoms and total chlorophyll-a (TChla). The results showed that there is a distinct relationship between diatoms and TChla in the SO, and a new global model (ABA Zpd ) improved the estimation of diatoms abundance by 28% in the SO compared with the OPEN ACCESS Remote Sens. 2014, 6 10090 original ABA model. In addition, we developed a regional model for the SO which further improved the retrieval of diatoms by 17% compared with the global ABA Zpd model. As a result, we found that diatom may be more abundant in the SO than previously thought. Linear trend analysis of diatom abundance using the regional model for the SO showed that there are statistically significant trends, both increasing and decreasing, in diatom abundance over the past eleven years in the region.

Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio‐economic objectives in decision‐making. To test the effect of accounting for both ES and socio‐economic objectives in land‐use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi‐objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision‐makers, who may have different expectations about the future. When optimizing only socio‐economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land‐use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio‐economic objectives has heterogeneous effects on land‐use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non‐natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.