The retrieval of forest above-ground biomass (AGB, or biomass hereafter) using remote sensing data has received increasing attention in the last decades for several reasons: primarily, for the ability of remote sensing to spatially extrapolate point field information on forest parameters, enabling AGB mapping over large areas; for the increased availability of different Earth Observation data types; and, in a global scenario of climate change and biodiversity loss, for the relevance of forest biomass with respect to global carbon accounting and reporting, conservation and management of natural resources, and environmental modeling.¹ 2. 3. 4. 5. 6. ^{– 7}

Policies to mitigate climate change and biodiversity loss often assume that protecting carbon‐rich forests provides co‐benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, and particularly large knowledge gaps exist for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi‐taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees (BRTs) to assess the relationship between above‐ground live carbon stocks and (a) taxon‐specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species’ responses to changing above‐ground carbon stocks and to detect change‐points in species composition along the carbon‐stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win‐win and trade‐off species (i.e., species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win‐win species gradually replaced trade‐off species with increasing carbon, without clear thresholds along the above‐ground carbon gradient, suggesting that community‐level surrogates (e.g., richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co‐benefits between carbon and biodiversity in temperate forest may require stand‐scale management that prioritizes either biodiversity or carbon in order to maximize co‐benefits at broader scales. Importantly, this contrasts with tropical forests, where climate and biodiversity objectives can be integrated at the stand scale, thus highlighting the need for context‐specificity when managing for multiple objectives. Accounting for critical change‐points of target taxa can help to deal with this specificity, by defining a safe operating space to manipulate carbon while avoiding biodiversity losses.

Since adequate information on the distribution of biodiversity is hardly achievable, biodiversity indicators are necessary to support the management of ecosystems. These surrogates assume that either some habitat features, or the biodiversity patterns observed in a well-known taxon, can be used as a proxy of the diversity of one or more target taxa. Nevertheless, at least for certain taxa, the validity of this assumption has not yet been sufficiently demonstrated.We investigated the effectiveness of both a habitat- and a taxa-based surrogate in six European beech forests in the Apennines. Particularly, we tested: (1) whether the stand structural complexity and the herb-layer species richness were good predictors of the fine-scale patterns of species richness of five groups of forest-dwelling organisms (beetles, saproxylic and epigeous fungi, birds and epiphytic lichens); and (2) the cross-taxon congruence in species complementarity and composition between herb-layer plants and the target taxa.We used Generalized Linear Mixed Models (GLMMs), accumulation curves and Procrustes analysis to evaluate the effectiveness of these surrogates when species richness, complementarity and composition were considered, respectively.Our results provided a limited support to the hypothesis that the herb-layer plants and the stand structural complexity were good surrogates of the target taxa. Although the richness of the herb-layer plants received a stronger support from the data than structural complexity as a predictor for the general patterns of species richness, the overall magnitude of this effect was weak and distinct taxa responded differently. For instance, for increasing levels of herb-layer richness, the richness of lichens showed a marked increase, while the richness of saproxylic fungi decreased. We also found significantly similar complementarity patterns between the herb-layer plants and beetles, as well as a significant congruence in species composition between herb-layer plants and saproxylic fungi. Finally, when different stand structural attributes were considered singularly, only the total amount of deadwood received support from the data as a predictor of the overall species richness.At the fine scale of this study, herb-layer plants and stand structural complexity did not prove to be effective surrogates of multi-taxon biodiversity in well-preserved southern European beech forests. Rather than on weak surrogates, these results suggest that sound conservation decisions should be supported by the information provided by comprehensive multi-taxonomic assessments of forest biodiversity

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.

Poplars are one of the most widespread fast-growing tree species used for forest plantations. Owing to their distinct features (fast growth and short rotation) and the dependency on the timber price market, poplar plantations are characterized by large inter-annual fluctuations in their extent and distribution. Therefore, monitoring poplar plantations requires a frequent update of information – not feasible by National Forest Inventories due to their periodicity – achievable by remote sensing systems applications. In particular, the new Sentinel-2 mission, with a revisiting period of 5 days, represents a potentially efficient tool for meeting this need. In this paper, we present a deep learning approach for mapping poplar plantations using Sentinel-2 time series. A reference dataset of poplar plantations was available for a large study area of more than 46,000 km² in Northern Italy and served as training and testing data. Two classification methods were compared: (1) a fully connected neural network (also called multilayer perceptron), and (2) a traditional logistic regression. The performance of the two approaches was estimated through bootstrapping procedure with a confidence interval of 99%. Results indicated for deep learning an omission error rate of 2.77%±2.76%, showing improvements compared to logistic regression, omission error rate = 8.91%±4.79%.