As technological advancements in forest monitoring – such as remote sensing and commodity supply chain tracking – allow for the generation and analysis of increasingly large datasets, forest policy makers and practitioners are looking for innovative yet practical ways for information transparency to transform forest governance. Especially in tropical forest countries looking to address the continuing deforestation and forest degradation through climate finance commitments and timber trade agreements, the access to information agenda has been placed at the fore of both the Reducing Emissions from Deforestation and forest Degradation (REDD+) process and the Forest Law Enforcement, Governance and Trade (FLEGT) Action Plan. This paper explores whether and how the proposed transparency agenda is having an impact (or not) in the Southwest Region of Cameroon. Using semi-structured interviews with civil society organizations, this paper examines how information is currently disclosed in the forest sector and the status of REDD+ and FLEGT transparency agendas at the local level.

Vegetation phenological events, especially peak foliage coloration, are among the ecological phenomena that are most sensitive to climate change. Compared to spring seasonally recurring events, fall phenology remains much less understood. Remotely sensed monitoring of fall phenology provides a wealth of opportunities to understand the underlying processes and mechanisms. However, the gradual change of foliage color in the fall season makes it challenging to remotely estimate critical phenological transition dates. Particularly, the transition date for foliage peak coloration cannot be adequately captured via conventional curve fitting-based phenological models. Also the lack of consensus among the conventional models makes it desirable to explore new remotely sensed representations of the fall phenological process. In this study, we developed an innovative complex network-based phenological model, namely “pheno-network”, to estimate the fall foliage transition date for peak coloration. The pheno-network model characterizes the phenological process through analyzing the collective changes of spectral signatures along the temporal trajectory. A network measure, moving average bridging coefficient, is newly designed to estimate the phenological transition date. With Harvard Forest and Hubbard Brook Forest as reference sites, the results demonstrated that the transition date estimated through the devised pheno-network model corresponds well with the peak coloration period of the reference sites. The unique structure of the pheno-network formulated via spectral similarities differentiates the various roles of vegetation spectral signatures at different phenological stages. This study is the first attempt at introducing network science to time series remote sensing in modeling the complex phenological processes of vegetation. The innovative network-based phenological representation shows great potential in improving remotely sensed phenological monitoring and shedding light on the subsequent modeling of vegetation phenological responses to climate change.

SUMMARY The study reported here aimed at presenting the structuring of a complex problem that emerges from contrasting perspectives of different stakeholders on the use and conservation of native forests in a context where regulations restrict their management, as occurs in Santa Catarina State, Brazil. The methodology adopted in this work consisted both in the construction of a causal map, based on interviews with stakeholders of Santa Catarina native forests, and in the analysis of the map using techniques of the Strategic Options Development and Analysis (SODA) approach. The analyses carried out indicated that the economic valuation of forest resources as well as the monitoring of forest cover are key issues for the management of Santa Catarina's native forests. In addition, the information generated by the causal map analysis can assist not only the process of designing innovative and all-inclusive policies for the management of native forests, but also the modeling process based on Systems Dynamics in order to evaluate the impacts of policies on the dynamics that govern the conservation and use of the resources of native forests. The adopted SODA approach also proved to be effective in structuring the complex problem situation addressed in this study.

The Decree on Climate 2019 represents an innovative and concrete framework for applying the international recommendations aimed at preventing and mitigating the effects of climate change. It focuses, in addition to many environment-related aspects, on the old-growth forests, recognizing them as forest ecosystems of high environmental value, defining their main ecological traits. According to this legislation, the extent of these forests in Italy is important, since many forest ecosystems have been left unmanaged from more than 60 years. Even if these stands are not always characterized by high level of naturalness, they are currently evolving towards more complex structures, due to the absence of human-related disturbance. Old-growth forests are unique ecosystems with a high structural complexity and peculiarities that are absent in managed forests. They are also an essential point of reference for sustainable forest management and environmental monitoring, in terms of conservation of biological diversity and ecological processes. For these reasons, they represent a unique benchmark for developing silvicultural models that incorporate knowledge of structural complexity (vertical and spatial) and developmental processes, duration of development and particularly the role of disturbances in creating structural legacies that become key elements of the post-disturbance stands. These forests, as the new Decrete underlines, must be protected, preserved and monitored in a long-term perspective, in order to safeguard their biodiversity, avoiding the structural simplification, which often characterizes the managed forests.

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area.

The increasing demand for tree and forest health monitoring due to ongoing climate change requires new future-oriented and nondestructive measurement techniques. Electrical resistivity (ER) tomography represents a promising and innovative approach, as it allows insights into living trees based on ER levels and ER cross-sectional distribution patterns of stems. However, it is poorly understood how external factors, such as temperature, tree water status, and electrode installation affect ER tomograms. In this study, ER measurements were carried out on three angiosperms (Betula pendula, Fagus sylvatica, Populus nigra) and three conifers (Larix decidua, Picea abies, Pinus cembra) exposed to temperatures between −10 and 30°C and to continuous dehydration down to −6.3 MPa in a laboratory experiment. Additionally, effects of removal of peripheral tissues (periderm, phloem, cambium) and electrode installation were tested. Temperature changes above the freezing point did not affect ER distribution patterns but average ER levels, which increased exponentially and about 2.5-fold from 30 to 0°C in all species. In contrast, freezing of stems caused a pronounced raise of ER, especially in peripheral areas. With progressive tree dehydration, average ER increased in all species except in B. pendula, and measured resistivities in the peripheral stem areas of both angiosperms and conifers were clearly linearly related to the tree water status. Removal of the periderm resulted in a slight decrease of high ER peaks. Installation of electrodes for a short period of 32–72 h before conducting the tomography caused small distortions in tomograms. Distortions became serious after long-term installation for several months, while mean ER was only slightly affected. The present study confirms that ER tomography of tree stems is sensitive to temperature and water status. Results help to improve ER tomogram interpretation and suggest that ER analyses may be suitable to nondestructively determinate the hydraulic status of trees. They thus provide a solid basis for further technological developments to enable presymptomatic detection of physiological stress in standing trees.

Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). We compute the STBI by fitting a Gaussian distribution using monthly brightness temperature data from SMOS; the normal assumption is tested using the Shapior-Wilk test. Our results indicate that the assumption of normally distributed brightness temperature data is valid at the 0.05 significance level. The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite derived SSM indices, one from SMOS and one from the ESA CCI soil moisture project and a standardized precipitation index based on in situ data from the European Climate Assessment & Dataset (ECA&D) project. When comparing the temporal dynamics of the STBI to the LDAS-Monde drought index we find that it has equal correlation skill to that of the ESA CCI soil moisture product ( 0.71 ). However, in addition the STBI provides improved spatial coverage because no masking has been applied over regions with dense boreal forest. Finally, we evaluate the STBI in a case study of the 2018 Nordic drought. The STBI is found to provide improved spatial and temporal coverage when compared to the drought index created from satellite derived SSM over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is qualitatively able to capture the 2018 drought onset, severity and spatial extent. We did see that the STBI was unable to detect the 2018 drought recovery for some areas in the Nordic countries. This false drought detection is likely linked to the recovery of vegetation after the drought, which causes an increase in the passive microwave brightness temperature, hence the STBI shows a dry anomaly instead of normal conditions, as seen for the other drought indices. We argue that the STBI could provide additional information for drought monitoring in regions where the SSM retrieval problem is not well defined. However, it then needs to be accompanied by a vegetation index to account for the recovery of the vegetation which could cause false drought detection. | Monitoring Soil Moisture Drought over Northern High Latitudes from Space