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Abstract Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests’ gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.

Chestnut (Castanea sativa Mill.) forests in the Mediterranean region are facing increasing abandonment due to a combination of factors, ranging from climate change to socioeconomic issues. The recovery of chestnut ecosystems and their preservation and valorization are key to ensuring the supply of the wide spectrum of ecosystem services they provide and to preventing detrimental environmental shifts. The study’s objective was to provide evidence on the effects of different management options on the ecophysiology of chestnut forests, with diverse pruning intensities (low, medium, and high intensity versus no pruning) tested in an abandoned chestnut stand in central Italy with the aim of recovering and rehabilitating it for fruit production. Innovative Internet of Things (IoT) ‘Tree Talker’ devices were installed on single trees to continuously monitor and measure ecophysiological (i.e., water transport, net primary productivity, foliage development) and microclimatic parameters. Results show a reduction in water use in trees subjected to medium- and high-intensity pruning treatments, along with a decrease in the carbon sequestration function. However, interestingly, the results highlight that trees regain their usual sap flow and carbon sink activity at the end of the first post-pruning growing season and fully realign during the following year, as also confirmed by the NDVI values. As such, this paper demonstrates the efficacy of recovering and managing abandoned chestnut forests, and the initial setback in carbon sequestration resulting from pruning is rapidly remedied with the advantage of reviving trees for fruit production. Additionally, the reduced water demand induced by pruning could represent a promising adaptation strategy to climate change, bolstering the resilience of chestnut trees to prolonged and intensified drought periods, which are projected to increase under future climate scenarios, particularly in the Mediterranean region.

Abstract The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications.

Phenology monitoring allows a better understanding of forest functioning and climate impacts. Satellite indicators are used to upscale ground phenological observations, but often differential responses are observed, and data availability can be limited. In view of climate impacts, new tools capable to detect rapid phenological changes and to work at single species level are needed. This research compares indices derived by the TreeTalker© (TT + ) below canopy upward-looking spectral data and Sentinel 2 satellite data, used to assess the phenological behavior and changepoints in several European beech forests. Overall, a mismatch between the information derived by the two sensor types is evidenced, with main differences in: start/end and length of season and phenology changepoints; larger variability captured by TT + with respect to Sentinel 2 especially in the leaf on period; mixed signal response from multiple vegetation layers in Sentinel 2 data. The complementarity of satellite and TT + indices allow exploring the phenological responses from different vegetation layers. TT + higher temporal resolution demonstrates precision in capturing the phenological changepoints in beech forests, especially if satellite image availability is limited by cloud cover and leads to miss critical phenological dates. The best settings for TT + data collection and the advantages to have two spectral data sources for improved forest phenology monitoring are also commented. The TT+, collecting additional tree parameters, can be a valuable tool for an integrated monitoring system based on spectral signals from above and below the canopy, at high temporal frequency and high spatial resolution.

The kiwifruit, for which Italy is the second largest producer in the world, is derived from a fruit vine that requires a significant amount of water for proper growth and the production of high-quality fruit. This study aims to investigate the ideal irrigation strategy to ensure an adequate water supply for robust root development, vibrant foliage, and successful fruit production. To achieve this goal, three experimental plots of Soreli kiwifruit vineyards were subjected to different water regimes: full irrigation (100%) and two plots with 80% and 60% irrigation. Each irrigation group was equipped with an Internet of Things (IoT) TT Spectrum, which measured reflected spectrometer data in 12 bands. Additionally, a soil sensor was used to determine moisture content. Fruit quality analysis, including weight loss, soluble solid content, flesh color and firmness, titratable acidity, ascorbic acid, polyphenols and flavonoids, was conducted to correlate productivity with water availability. By analyzing the IoT-TT Spectrum data for each kiwifruit plant, crucial indicators for assessing physiological status, health, and growth were examined. Maintaining a balance between leaf nitrogen content and photosynthetic capacity, with low AI and higher NDVI and CVI values, was used to determine the optimal irrigation level for each of the three plots. Furthermore, qualitative data obtained from fruits revealed that overwatering or underwatering kiwifruit plants affects yield production and fruit quality. Fruits subjected to full irrigation exhibited lower sugar levels compared to those under deficit irrigation. This study demonstrates that excessive or limited irrigation regimes negatively impact plant health and on the fruit quality.

Abstract The Mediterranean diet (MD) is a world-renowned healthy dietary pattern. In the present study we analyse the climate sustainability of the MD and the greenhouse gas emissions (EGHG) associated with current dietary patterns in Mediterranean and non-Mediterranean EU countries, focusing on the major deviations from the MD health and environmental targets in Mediterranean countries. The EGHG associated with dietary patterns were calculated for seven Mediterranean countries (Cyprus, Croatia, Greece, Italy, Portugal, Spain and Malta, referred to as 7MED) and the other 21 countries in the EU 28 (referred to as 21OTHER), using 2017 as the reference year. A new harmonised compilation of 3449 carbon footprint values of food commodities, based on a standardised methodology to extract information and assign optimal footprint values and uncertainties to food items, was used to estimate EGHG of food consumption. Our findings show that the EGHG associated with the ideal MD pattern, 2.3 kg CO2equivalents (CO2eq) capita−1 d−1, are in line with planetary GHG climate targets, though GHG emissions associated with food consumption in Mediterranean countries strongly diverged from the ideal MD. Both MED and 21OTHER countries were found to have comparable dietary associated EGHG (4.46 and 4.03 kg CO2eq capita−1 d−1 respectively), almost double that expected from a sustainable dietary pattern. The primary factor of dietary divergence in 7MED countries was found to be meat overconsumption, which contributed to 60% of the EGHG daily excess (1.8 kg of CO2eq capita−1 d−1).

Spectacled bears (SB) (Tremarctos ornatus) are the only bear species native to South America. This particular bear is the single species of its genus, and it is listed as vulnerable according to the IUCN red list. A critical SB conservation habitat is in the rural territories of the Peruvian Amazon, where anthropogenic land-use changes and landscape fragmentation threaten SB habitats. The following questions arise in this context: How much has land-use changed? How to design the establishment of ecological corridors (ECs) to support the conservation of SB?. We investigated the temporal land use and land cover changes for last 30 years (1990–2020) for a better projection of the ECs and to quantify the temporal landscape metrics. Furthermore, we integrated cloud computing, machine learning models with cost-effective techniques to delineate the ECs for SB within the rural territories. Ensemble Random Forest model associated with Google Earth Engine (GEE) was used to develop four land use and land cover (LULC) maps (for the years 1990, 2000, 2010 and 2020). The least cost path (LCP) model based on Dijkstra’s shortest path algorithm was assembled based on six variables (altitude; slope; distance to roads; distance to population centers; land use map; inventory map of SB). Then, we calculated the ECs based on the multidirectional origin-destination points, we found that forest patches increased by 57% between 1990 and 2020. Results showed statistically significant agreement (R2 = 0.47; p

Measurement(s) carbon footprint of food products and related uncertainties • water footprint of food products and related uncertainties Technology Type(s) digital curation • statistical analysis Factor Type(s) Typologies, sub-tyologies and items of food products Sample Characteristic - Environment agroecosystem • food production system Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14115608

To assess the degree to which it has met its commitments under the Paris Agreement, Morocco is called upon to carry out carbon assessments and transparent evaluations. Within the forestry sector, little is known about the role of Morocco’s forests in contributing to carbon uptake. With this aim, we applied for the first time in the literature the 3-PG model to Cedrus atlantica ((Endl.) Manetti ex Carrière, 1855), which represents about 131,800 ha of Morocco’s forest area (i.e., Azrou forest). Through the Differential Evolution-Markov Chains (DE-MC) we tested and assessed the sensitivity and calibrated the 3-PG model. This process-based model provided significant results regarding the carbon sequestration capacity. The results showed the following: i. Parameters related to stand properties, canopy structure, and processes, as well as biomass partitioning, are the most important or sensitive for the performance of the model; ii. The DE-MC method optimized the values of the 3-PG parameters which was confirmed by the means of the Gelman–Rubin convergence test; iii. According to the predictions of the calibrated 3-PG, the Net Primary Production in the pure Azrou forest varies between 0.35 and 8.82 tC.ha−1.yr−1, it is equal in average to 5.48 tC.ha−1.yr−1, which given the total area corresponds to 7918 tC.ha−1.

Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions.

Recently, Internet of Things (IoT) technologies have grown rapidly and represent now a unique opportunity to improve our environmental monitoring capabilities at extremely low costs. IoT is a new system of thinking in which objects, animals or people are equipped with unique identifiers and transfer data a network without requiring human-to-human or human-to-computer interaction. IoT has evolved from the convergence of wireless technologies, microelectromechanical systems (MEMS) and the Internet. The development of these technologies in environmental monitoring domains allows real-time data transmission and numerous low-cost monitoring points. We have designed a new device, the TreeTalker©, which is capable of measuring water transport in trees, diametrical growth, spectral characteristics of the leaves and microclimatic parameters and transmit data in semi-real time. Here we introduce the device’s features, provide an example of monitored data from a field test site and discuss the application of this new technology to tree monitoring in various contexts, from forest to urban green infrastructures management and ecological research.

An eddy covariance (EC) GHG study was conducted at two comparable agroecosystems in the Central region of European Russia. The study was conducted in 2013 at the RTSAU Experimental Field with Umbric Albeluvisols (Moscow) and a private farm field with Chernozems in the Pristen area (Kursk region). Both studies involved barley crops, but the fields differed in climate, soil and technological conditions. Diurnal values of net ecosystem exchange (NEE) were two times higher in Kursk than in Moscow. The higher gross primary production (GPP) in Kursk was characterized by better climate and soil conditions and, partially, by the low intensity practices of semi-organic farming. GPP dynamics of the two agroecosystems were significantly different only during the first 50 days of vegetation; however, NEE seasonal differences persisted throughout the growing period, with the trends changing until the end of barley ripening. General trends for ecosystem respiration and GPP were determined by the crop phase. NEE seasonal dynamics showed that the Chernozem agroecosystem was characterized by an almost 2-fold increase in the range of CO2 fluxes, largely determined by hydrologic regime features. Since yield in Kursk was 50% lower than that in Moscow, it may be concluded that the use of modern fertilizing and crop protection systems converts a larger portion of GPP into yield mass.

Taking advantage of novel IoT technologies, a new multifunctional device, the “TreeTalker”, was developed to monitor real-time ecophysical and biological parameters of individual trees, as well as climatic variables related to their surrounding environment, principally, air temperature and air relative humidity. Here, IoT applied to plant ecophysiology and hydrology aims to unravel the vulnerability of trees to climatic stress via a single tree assessment at costs that enable massive deployment. We present the performance of the TreeTalker to elucidate the functional relation between the stem water content in trees and respective internal/external (stem hydraulic activity/abiotic) drivers. Continuous stem water content records are provided by an in-house-designed capacitance sensor, hosted in the reference probe of the TreeTalker sap flow measuring system, based on the transient thermal dissipation (TTD) method. In order to demonstrate the capability of the TreeTalker, a three-phase experimental process was performed including (1) sensor sensitivity analysis, (2) sensor calibration, and (3) long-term field data monitoring. A negative linear correlation was demonstrated under temperature sensitivity analysis, and for calibration, multiple linear regression was applied on harvested field samples, explaining the relationship between the sample volumetric water content and the sensor output signal. Furthermore, in a field scenario, TreeTalkers were mounted on adult Fagus sylvatica L. and Quercus petraea L. trees, from June 2020 to October 2021, in a beech-dominated forest near Marburg, Germany, where they continuously monitored sap flux density and stem volumetric water content (stem VWC). The results show that the range of stem VWC registered is highly influenced by the seasonal variability of climatic conditions. Depending on tree characteristics, edaphic and microclimatic conditions, variations in stem VWC and reactions to atmospheric events occurred. Low sapwood water storage occurs in response to drought, which illustrates the high dependency of trees on stem VWC under water stress. Consistent daily variations in stem VWC were also clearly detectable. Stem VWC constitutes a significant portion of daily transpiration (using TreeTalkers, up to 4% for the beech forest in our experimental site). The diurnal–nocturnal pattern of stem VWC and sap flow revealed an inverse relationship. Such a finding, still under investigation, may be explained by the importance of water recharge during the night, likely due to sapwood volume changes and lateral water distribution rather than by a vertical flow rate. Overall, TreeTalker demonstrated the potential of autonomous devices for monitoring sap density and relative stem VWC in the field of plant ecophysiology and hydrology.

The Internet of Things (IoT) development is revolutionizing environmental monitoring and research in macroecology. This technology allows for the deployment of sizeable diffuse sensing networks capable of continuous monitoring. Because of this property, the data collected from IoT networks can provide a testbed for scientific hypotheses across large spatial and temporal scales. Nevertheless, data curation is a necessary step to make large and heterogeneous datasets exploitable for synthesis analyses. This process includes data retrieval, quality assurance, standardized formatting, storage, and documentation. TreeTalkers are an excellent example of IoT applied to ecology. These are smart devices for synchronously measuring trees’ physiological and environmental parameters. A set of devices can be organized in a mesh and permit data collection from a single tree to plot or transect scale. The deployment of such devices over large-scale networks needs a standardized approach for data curation. For this reason, we developed a unified processing workflow according to the user manual. In this paper, we first introduce the concept of a unified TreeTalker data curation process. The idea was formalized into an R-package, and it is freely available as open software. Secondly, we present the different functions available in “ttalkR”, and, lastly, we illustrate the application with a demonstration dataset. With such a unified processing approach, we propose a necessary data curation step to establish a new environmental cyberinfrastructure and allow for synthesis activities across environmental monitoring networks. Our data curation concept is the first step for supporting the TreeTalker data life cycle by improving accessibility and thus creating unprecedented opportunities for TreeTalker-based macroecological analyses.

The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

The occurrence of old-growth forests is quite limited in Mediterranean islands, which have been subject to particularly pronounced human impacts. Little is known about the carbon stocks of such peculiar ecosystems compared with different stages of secondary succession. We investigated the carbon variation in aboveground woody biomass, in litter and soil, and the nitrogen variation in litter and soil, in a 100 years long secondary succession in Mediterranean ecosystems. A vineyard, three stages of plant succession (high maquis, maquis-forest, and forest-maquis), and an old growth forest were compared. Soil samples at two soil depths (0-15 and 15-30 cm), and two litter types, relatively undecomposed and partly decomposed, were collected. Carbon stock in aboveground woody biomass increased from 6 Mg ha-1 in the vineyard to 105 Mg ha-1 in old growth forest. Along the secondary succession, soil carbon considerably increased from about 33 Mg ha-1 in the vineyard to about 69 Mg ha-1 in old growth forest. Soil nitrogen has more than doubled, ranging from 4.1 Mg ha-1 in the vineyard to 8.8 Mg ha-1 in old growth forest. Both soil parameters were found to be affected by successional stage and soil depth but not by their interaction. While the C/N ratio in the soil remained relatively constant during the succession, the C/N ratio of the litter strongly decreased, probably following the progressive increase in the holm oak contribution. While carbon content in litter decreased along the succession, nitrogen content slightly increased. Overall, carbon stock in aboveground woody biomass, litter and soil increased from about 48 Mg ha-1 in the vineyard to about 198 Mg ha-1 in old growth forest. The results of this study indicate that, even in Mediterranean environments, considerable amounts of carbon may be stored through secondary succession processes up to old growth forest.

Research Highlights: Monitoring of soil CH4 fluxes in African tropical forest conducted run for almost two years, contributing to the scant information on greenhouse gas (GHG) fluxes from forests available from this region. Data showed that the forest soil acted as a net yearly source of CH4. Hotspots of CH4 emissions were measured both in upland and lowland areas of the forest, and on an annual basis they overcame the soil CH4 sink during drier periods or in well-drained areas. Background and Objectives: Atmospheric studies indicate that tropics are a strong CH4 source. Regional budgets attribute the majority of this source to wetland ecosystems and flooded lowland forests, whereas un-flooded forests are considered net CH4 sinks, although few studies in tropical forests, in particular in Africa, are available. The present work aims to contribute to this knowledge gap. Materials and Methods: Monitoring campaigns were conducted along the year in the tropical forest of the Ankasa National Park, Ghana, in two contrasting environments, uphill and downhill, using close static chambers coupled with gas chromatography. Results: The uphill area was a net weak CH4 sink with mean daily fluxes ranging from −1.29 to 0.44 mg CH4 m−2 d−1. The downhill area was a significant CH4 source with mean daily fluxes ranging from −0.67 to 188.09 mg CH4 m−2 d−1 and with peaks up to 1312 mg CH4 m−2 d−1 in the wet season. Conclusions: The net annual soil CH4 budget for the Ankasa Park, normalizing the proportion of downhill areas over the whole park surface, was a source of about 3.3 kg CH4 ha−1 yr−1. Overlooking such areas might lead to underestimates of the total CH4 source strength of forested areas.

Background and Objectives: Over the last few decades, the European mountain environment has been characterized by the progressive abandonment of agro-pastoral activities and consequent forest expansion due to secondary succession. While woody encroachment is commonly considered as a climate change mitigation measure, studies suggest a still uncertain role of the soil organic carbon (SOC) pool in contributing to climate change mitigation during this process. Therefore, the objective of the study is to investigate the possible SOC variations occurring as a consequence of the secondary succession process at the provincial level in an Alpine area in Italy. Materials and Methods: A chronosequence approach was applied to identify, in five different study areas of the Belluno province, the land use/land cover change over four different stages of natural succession, from managed grazing land to secondary forest developed on abandoned grazing land. In each chronosequence stage, soil samples were collected down to the bedrock (0–60 cm depth) to determine the changes in the SOC stock due to the woody encroachment process. Results: In all areas, small or no significant (p < 0.05) SOC stock changes were observed during the secondary succession in the upper 30 cm of mineral soil, while significant changes were evident in the 30–60 cm compartment, with the SOC stock significantly decreasing from 30% to 60% in the final stage of the succession. This fact indicates the great importance of considering also the subsoil when dealing with land use/land cover change dynamics. Conclusions: The recorded trend in SOC has been proved to be the opposite in other Italian regions, so our results indicate the importance of local observation and data collection to correctly evaluate the soil contribution to climate change mitigation during woody encroachment.

Urban green infrastructure plays an increasingly significant role in sustainable urban development planning as it provides important regulating and cultural ecosystem services. Monitoring of such dynamic and complex systems requires technological solutions which provide easy data collection, processing, and utilization at affordable costs. To meet these challenges a pilot study was conducted using a network of wireless, low cost, and multiparameter monitoring devices, which operate using Internet of Things (IoT) technology, to provide real-time monitoring of regulatory ecosystem services in the form of meaningful indicators for both human health and environmental policies. The pilot study was set in a green area situated in the center of Moscow, which is exposed to the heat island effect as well as high levels of anthropogenic pressure. Sixteen IoT devices were installed on individual trees to monitor their ecophysiological parameters from 1 July to 31 November 2019 with a time resolution of 1.5 h. These parameters were used as input variables to quantify indicators of ecosystem services related to climate, air quality, and water regulation. Our results showed that the average tree in the study area during the investigated period reduced extreme heat by 2 °C via shading, cooled the surrounding area by transferring 2167 ± 181 KWh of incoming solar energy into latent heat, transpired 137 ± 49 mm of water, sequestered 8.61 ± 1.25 kg of atmospheric carbon, and removed 5.3 ± 0.8 kg of particulate matter (PM10). The values of the monitored processes varied spatially and temporally when considering different tree species (up to five to ten times), local environmental conditions, and seasonal weather. Thus, it is important to use real-time monitoring data to deepen understandings of the processes of urban forests. There is a new opportunity of applying IoT technology not only to measure trees functionality through fluxes of water and carbon, but also to establish a smart urban green infrastructure operational system for management.

The eddy covariance (EC) technique as a powerful statistics-based method of measurement and calculation the vertical turbulent fluxes of greenhouses gases within atmospheric boundary layers provides the continuous, long-term flux information integrated at the ecosystem scale. An attractive way to compare the agricultural practices influences on GHG fluxes is to divide a crop area into subplots managed in different ways. The research has been carried out in the Precision Farming Experimental Field of the Russian Timiryazev State Agricultural University (RTSAU, Moscow) in 2013 under the support of RF Government grant # 11.G34.31.0079, EU grant # 603542 LUС4С (7FP) and RF Ministry of education and science grant # 14-120-14-4266-ScSh. Arable Umbric Albeluvisols have around 1% of SOC, 5.4 pH (KCl) and NPK medium-enhanced contents in sandy loam topsoil. The CO2 flux seasonal monitoring has been done by two eddy covariance stations located at the distance of 108 m. The LI-COR instrumental equipment was the same for the both stations. The stations differ only by current crop version: barley or vetch and oats. At both sites, diurnal patterns of NEE among different months were very similar in shape but varied slightly in amplitude. NEE values were about zero during spring time. CO2 fluxes have been intensified after crop emerging from values of 3 to 7 µmol/s∙m2 for emission, and from 5 to 20 µmol/s∙m2 for sink. Stabilization of the fluxes has come at achieving plants height of 10-12 cm. Average NEE was negative only in June and July. Maximum uptake was observed in June with average values about 8 µmol CO2 m−2 s−1. Although different kind of crops were planted on the fields A and B, GPP dynamics was quite similar for both sites: after reaching the peak values at the mid of June, GPP decreased from 4 to 0.5 g C CO2 m-2 d-1 at the end of July. The difference in crops harvesting time that was equal two weeks did not significantly influence the daily GPP patterns. Cumulative assimilation of CO2 at the end of the growing season was about 150 g C m−2 for both sites. So the difference in NEE was the consequence of essentially higher respiration rates in case of vetch and oats (about 350 g C m−2) comparing to barley (250 g C m−2) that needs additional research. The results have shown high daily and seasonal dynamic of CO2 emission too as a result of different and contrasted conditions: crop type, crop development stage, soil moisture and air temperature. Obtained unique for Russian agriculture data are useful for land-use practices environmental assessment, for soil organic carbon dynamics analysis and agroecological evaluation.

Identifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m), they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning.

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale.

We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZmedian) and fifth percentile of total pulse return intensity (i_tot5th). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_totstdDev, i_totmedian and i_tot5th) and nZmedian, whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZmedian, as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass.

Airborne multispectral and hyperspectral remote sensing is a powerful tool for environmental monitoring applications. In this paper we describe a new system (ASPIS) composed by a 4-CCD spectral sensor, a thermal IR camera and a laser altimeter that is mounted on a flexible Sky-Arrow airplane. A test application of the multispectral sensor to estimate durum wheat quality is also presented.

It is still unclear whether the exponential rise of atmospheric CO2 concentration has produced a fertilization effect on tropical forests, thus incrementing their growth rate, in the last two centuries. As many factors affect tree growth patterns, short -term studies might be influenced by the confounding effect of several interacting environmental variables on plant growth. Long-term analyses of tree growth can elucidate long-term trends of plant growth response to dominant drivers. The study of annual rings, applied to long tree-ring chronologies in tropical forest trees enables such analysis. Long-term tree-ring chronologies of three widespread African species were measured in Central Africa to analyze the growth of trees over the last two centuries. Growth trends were correlated to changes in global atmospheric CO2 concentration and local variations in the main climatic drivers, temperature and rainfall. Our results provided no evidence for a fertilization effect of CO2 on tree growth. On the contrary, an overall growth decline was observed for all three species in the last century, which appears to be significantly correlated to the increase in local temperature. These findings provide additional support to the global observations of a slowing down of C sequestration in the trunks of forest trees in recent decades. Data indicate that the CO2 increase alone has not been sufficient to obtain a tree growth increase in tropical trees. The effect of other changing environmental factors, like temperature, may have overridden the fertilization effect of CO2.