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Indoor microclimate conditions and air pollutant concentrations (O3, TVOC, CO, CO2, and particulate matter mass concentrations in six size bins) were measured in the Greek Archaeological Museum of Abdera, which houses priceless works of art from the birthplace of the ancient philosopher Democritus. The monitoring campaign took place during the spring and summer months, when there were the greatest number of visitors. In the exhibition rooms, daily variations in relative humidity ranged from 4% to 10%, and daily variations in air temperature ranged from 0.9 °C to 2.6 °C. These uncontrolled changes may endanger the housed antiquities. The microclimate in the storage rooms varied substantially less than in the exhibition halls due to dehumidifiers and the lack of visitors. Concerning air pollution, indoor O3 concentrations were higher than the recommended limit values for the conservation of artwork. Even more worrisome are particulate matter mass concentrations above the air quality guidelines. Despite the fact that the building is well insulated and that only artificial lighting is used in the exhibition halls, it is difficult to achieve adequate conditions for the protection of the works of art. [ABSTRACT FROM AUTHOR]

Hawaii is known for its diverse micro-climates, making the evaluation of varieties at different locations an important strategy to determine the best varieties for each climate zone. Demand for dry beans in Hawaii has been rising due to the increase in production of value-added goods made from legumes. Initial field trials in 2017 were conducted to determine the best sowing date for dry beans in Hawaii since there were no previous such determinations. Field trials were conducted between 2018 and 2021 to evaluate 24 varieties of chickpea (Cicer arietinum), 21 varieties of common bean (Phaseolus vulgaris), and 10 varieties of cowpea (Vigna unguiculata) for their suitability and yield variability under Hawaii's micro-climates. Preliminary sowing date trials were conducted in 2017, and a variety of trials were conducted between 2018 and 2021; seven field trials were conducted, including two in each of Oahu, Maui, and Hawaii County, and one in Kauai County. The trials were conducted in a randomized complete block design (RCBD) with three replicates. For all the study sites, 20-20-20 NPK fertilizer was applied at 30, 13, and 25 kg/ha N-P-K, respectively. A drip irrigation system was used in all locations as supplemental irrigation. Irrigation was used when needed and turned off 2 weeks prior to harvest. The results showed highly significant (p < 0.01) differences in yield between the varieties of each legume crop. Highly significant (p < 0.01) differences in yield were also found between the study locations. There was a significant (p < 0.05) decline in yield by 28% and 45% in chickpea and by 32% and 43% in common bean when planted 1 and 2 months, respectively, after the optimal mid-February planting on Oahu and Maui County. A decline of 21% and 50% in chickpea and 30 and 48% in common bean was recorded when planted 1 and 2 months, respectively, after the optimal mid-March planting in Hawaii County. The study results lead to developing site-specific recommendations for varieties and planting dates from each of the legume crops for each county. However, more studies are needed to develop site-specific recommendations for the micro-climates within each county. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of forest disturbances in temperate forests has become increasingly crucial as policy demands and climate pressure on these forests rise. The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016–2022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors. [ABSTRACT FROM AUTHOR]

Central Europe is not a typical wildfire region; however, an increasingly warm and dry climate and model-based projections indicate that the number of forest fires are increasing. This study provides new insights into the drivers of forest fire occurrence in the Czech Republic, during the period 2006 to 2015, by focusing on climate, land cover, and human activity factors. The average annual number of forest fires during the study period was 728, with a median burned area of 0.01 ha. Forest fire incidence showed distinct spring (April) and summer (July to August) peaks, with median burned areas of 0.04 ha and 0.005 ha, respectively. Relationships between the predictors (climate data, forest-related data, socioeconomic data, and landscape-context data) and the number of forest fires in individual municipality districts were analyzed using Generalized Additive Models (GAM) on three time scales (annually, monthly, and during the summer season). The constructed GAMs explained 48.7 and 53.8% of forest fire variability when fire occurrence was analyzed on a monthly scale and during the summer season, respectively. On an annual scale, the models explained 71.4% of the observed forest fire variability. The number of forest fires was related to the number of residents and overnight tourists in the area. The effect of climate was manifested on monthly and summer season scales only, with warmer and drier conditions associated with higher forest fire frequency. A higher proportion of conifers and the length of the wildland–urban interface were also positively associated with forest fire occurrence. Forest fire occurrence was influenced by a combination of climatic, forest-related, and social activity factors. The effect of climate was most pronounced on a monthly scale, corresponding with the presence of two distinct seasonal peaks of forest fire occurrence. The significant effect of factors related to human activity suggests that measures to increase public awareness about fire risk and targeted activity regulation are essential in controlling the risk of fire occurrence in Central Europe. An increasing frequency of fire-conducive weather, forest structure transformations due to excessive tree mortality, and changing patterns of human activity on the landscape require permanent monitoring and assessment of possible shifts in forest fire risk. [ABSTRACT FROM AUTHOR]

Dengue is a serious infectious disease worldwide and a climate-sensitive disease. Thus, our goals were to (i) evaluate the relationship between dengue incidence and meteorological variables (rainfall and air temperature); (ii) identify the spatiotemporal pattern of dengue incidence in the municipalities of Mato Grosso from 2001 to 2020; and (iii) verify the spatial dependence of dengue incidence in the dry and wet seasons. We used dengue data from 2001 to 2020, monthly rainfall estimates from GPM, and daily air temperature estimates from ERA-5. The municipalities of the Mato Grosso state are included in 16 healthcare territories. The seasonal rainfall pattern indicates that the peak of the dengue endemic occurred in the wet season. However, drier and/or warmer places had a lower incidence of dengue in the dry season. Furthermore, a lagged effect of meteorological variables on dengue incidence has been identified, ranging from 0 to 7 months. Hotspot areas were identified which might have the potential for an intense spreading of dengue in Mato Grosso. They were mainly concentrated in the healthcare territory of Teles Pires (ID 14) in the dry season, while they were concentrated in the healthcare territories of Garças Araguaia (ID 5), Oeste (ID 11), and Teles Pires (ID 14) in the wet season. In addition, they are located in the Am climate and in the Amazon Forest and Brazilian savanna biomes, which have higher dengue incidence values. These results help to highlight which municipalities decision-makers must intervene in the public health system to prevent and control future epidemics. [ABSTRACT FROM AUTHOR]

Natural disturbances (wildfires, droughts, beetle outbreaks) shaped temperate forests for millennia, including dry forests of the western USA. Could they now best restore and adapt dry forests to climate change while protecting nearby communities? Mechanical fuel-reduction treatments (e.g., thinning) reduce landscape heterogeneity and appear ineffective since <1% of the treated area encounters fire each year and fires are still increasing. We propose and analyze a nature-based solution (NbS), using natural disturbances, to see whether it is feasible, how long it might take, and whether it could more effectively restore and adapt dry forests to climate change. We compared 2010–2019 disturbance rates on ~16 million ha of federal dry forests with historical data. We evaluated how much adaptation is achieved by comparing how trees are selected by treatments and disturbances. We found an NbS, which works with natural disturbances and prioritizes community protection, is feasible in western USA dry forests since disturbances are occurring mostly within historical rates. Natural disturbances, unlike mechanical treatments, select survivors that are more likely to be genetically adapted to survive future disturbances and climate change, while perpetuating ecosystem services. Natural disturbances also could ecologically restore forest heterogeneity, better maintain carbon storage, and reduce management needs. A fully developed disturbance-based NbS could more effectively adapt dry forests to climate change within ~30–40 years if active management is reprioritized to protect the built environment and communities near public forests. [ABSTRACT FROM AUTHOR]

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance. [ABSTRACT FROM AUTHOR]

The community structure of macrofungi is influenced by multiple complex factors, including climate, soil, vegetation, and human activities, making it challenging to discern their individual contributions. To investigate the dynamic changes in macrofungal diversity in an Ancient Tree Park located in Northeast China and explore the factors influencing this change, we collected 1007 macrofungi specimens from different habitats within the park and identified 210 distinct fungal species using morphological characteristics and ITS sequencing. The species were classified into 2 phyla, 6 classes, 18 orders, 55 families, and 94 genera. We found macrofungal compositions among different forest types, with the mixed forest displaying the highest richness and diversity. Climatic factors, particularly rainfall and temperature, positively influenced macrofungal species richness and abundance. Additionally, by analyzing the soil fungal community structure and comparing aboveground macrofungi with soil fungi in this small-scale survey, we found that the soil fungal bank is not the main factor leading to changes in the macrofungal community structure, as compared to the influence of climate factors and forest types. Our findings provide valuable insights into the dynamic nature of macrofungal diversity in the Ancient Tree Park, highlighting the influence of climate and forest type. [ABSTRACT FROM AUTHOR]

Measurements of net ecosystem exchange (NEE), solar global radiation, photosynthetically active radiation (PAR) and meteorological parameters were carried out on a subtropical conifer plantation in China from 2013 to 2016. These observations were used to develop and evaluate an empirical model of gross primary production (GPP) (EMGPP) with 3-factor and 2-factor models. Using a 3-factor model, the simulated hourly GPP values were consistent with observations with a relative bias of 9.96% and normalized mean square error values of 0.07 mg CO2 m−2 s−1 for the scattering factor S/Q (S and Q are diffuse and global solar radiation) < 0.5 and 15.52% and 0.15 mg CO2 m−2 s−1 for S/Q ≥ 0.5. Validations of the EMGPP for hourly, daily, monthly, and annual GPP values were carried out and showed that both 3-factor and 2-factor EMGPP models can accurately capture diurnal, seasonal and interannual variations in GPP, but most simulated GPP overestimated the observed value. When the scattering factor is not available, the 2-factor EMGPP can be used. The EMGPP using 3-factor and 2-factor models was applied to simulate GPP under all sky conditions from 2013–2016, and the estimated GPP were in reasonable agreement with the measured values and showed systematic overestimations of 31% and 29% for mean hourly GPP and 41% and 29% for annual amounts, respectively. The sensitivity test demonstrated that GPP values were more sensitive to changes in PAR than to changes in water vapor and scattering factor at low S/Q, but were more sensitive to changes in water vapor than to PAR and S/Q at high S/Q. The sensitivity test revealed some mechanisms of GPP and its related processes, including the relationships between GPP and scattering of PAR, GPP and water vapor, which were in good agreement with other observations and model studies. An empirical model based on PAR energy balance can better describe the multiple interactions between GPP and its driving factors (PAR, water vapor, S/Q). The ratio of the emissions of biogenic volatile organic compounds (BVOCs) to net ecosystem exchange clearly varied between forests in different climate zones. [ABSTRACT FROM AUTHOR]

Priority conservation areas are the key areas of biodiversity maintenance and ecosystem conservation. Based on a Maxent model, this study predicted the potential distribution of Pseudotsuga sinensis under the current climate and future climate change scenarios in Guizhou province, and then, assessed three kinds of priority conservation area under climate change. The results were as follows: (1) The AUC (Area Under the Curve) values showed excellent prediction accuracy of the model. (2) The areas of the potential habitats of P. sinensis forests under the current climate and future climate change scenarios were 22,062.85 km2 and 18,395.92 km2, respectively. As for their spatial distribution, the potential habitats of P. sinensis forests were distributed in the Bijie, Zunyi, Tongren, Liupanshui and Xingyi regions under the current climate, and in the Kaili region, in addition to the above-mentioned cities, under future climate change scenarios. (3) The total area of priority conservation areas under climate change was 25,350.26 km2. The area of the predicted sustainable potential habitats was 15,075.96 km2, of the vulnerable potential habitats was 7256.59 km2 and of the derivative potential habitats was 3017.71 km2. [ABSTRACT FROM AUTHOR]

Understanding the ecological role of fire in forests is essential for proper management and conservation programs. The objectives of this study were: (1) to reconstruct the history of fires in a temperate forest in Sierra Madre Occidental; and (2) to interpret the impacts of fire and climate on forest structure. Sixty tree cross-sections with fire scars were analyzed, and descriptive statistics of fire history were generated. Additionally, growth cores were analyzed, and the ages of trees of different diameter categories were calculated. The synchrony between fire history and tree establishment was determined, and precipitation and Palmer Drought Severity Index (PDSI) values were correlated with the number of trees established per year. The presence of 137 fire scars was determined, which allowed the reconstruction of 41 fire events over the period 1855–2019; however, only the period 1940–2015 was used to compare tree recruitment, as tree establishment was detected in this period. The mean fire interval (MFI) was 2.28 years in general, and 12.17 years for extensive fires. As regards vegetation, a continuous recruitment pattern was observed, typical of a frequent low-intensity fire regime, although peak regeneration occurred after extensive fires. The correlation analysis showed that the number of trees established per year was influenced by the wet conditions that occurred in December of the previous year and the dry conditions in September and October of the previous year. This finding demonstrates the historical influence of fire and climate on the structure of the current stand in the study area. Therefore, the present study highlights the importance of including fire in forest management programs, considering the natural fire regime to which the species in this ecosystem are already adapted. [ABSTRACT FROM AUTHOR]

Frequent forest fires cause air pollution, threaten biodiversity and spoil forest ecosystems. Forest fire vulnerability assessment is a potential way to improve the ability of forests to resist climate disasters and help formulate appropriate forest management countermeasures. Here, we developed an automated hybrid machine learning algorithm by selecting the optimal model from 24 models to map potential forest fire vulnerability over China during the period 2001–2020. The results showed forest aboveground biomass (AGB) had a vulnerability of 26%, indicating that approximately 2.32 Gt C/year of forest AGB could be affected by fire disturbances. The spatiotemporal patterns of forest fire vulnerability were dominated by both forest characteristics and climate conditions. Hotspot regions for vulnerability were mainly located in arid areas in western China, mountainous areas in southwestern China, and edges of vegetation zones. The overall forest fire vulnerability across China was insignificant. The forest fire vulnerability of boreal and temperate coniferous forests and mixed forests showed obviously decreasing trends, and cultivated forests showed an increasing trend. The results of this study are expected to provide important support for the forest ecosystem management in China. [ABSTRACT FROM AUTHOR]

Protected cropping produces more food per land area than field-grown crops. Protected cropping includes low-tech polytunnels utilizing protective coverings, medium-tech facilities with some environmental control, and high-tech facilities such as fully automated glasshouses and indoor vertical farms. High crop productivity and quality are maintained by using environmental control systems and advanced precision phenotyping sensor technologies that were first developed for broadacre agricultural and can now be utilized for protected-cropping applications. This paper reviews the state of the global protected-cropping industry and current precision phenotyping methodology and technology that is used or can be used to advance crop productivity and quality in a protected growth environment. This review assesses various sensor technologies that can monitor and maintain microclimate parameters, as well as be used to assess plant productivity and produce quality. The adoption of precision phenotyping technologies is required for sustaining future food security and enhancing nutritional quality. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R² = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4° to 55.1° SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined. [ABSTRACT FROM AUTHOR]

(1) Background: The management of forest and land fires has become of concern to the Indonesian government, as demonstrated by the issuing of the Presidential Instruction of the Republic of Indonesia Number 11 of 2015, then replaced by Number 3 of 2020, concerning Forest and Land Fire Management. In 2002, the Ministry of Environment and Forestry (MoEF) established Manggala Agni, a forest fire management organization, at the central government level. This study aims to explain the development of the Indonesian Forest and Land Fire Prevention Patrol System. The system development was conducted by the Computer Science Department of IPB University, in collaboration with the Agency for Climate Change and Forest and Land Fire Management, as well as the Directorate of Forest and Land Fire Management, MoEF. (2) Methods: The system development adopted the steps in the prototyping method, namely communication, quick planning, rapid design modeling, and prototype construction, as well as dissemination, submission, and feedback. (3) Results: The web-based system for the real-time monitoring and analysis of the forest and land fire prevention patrol is integrated into a mobile application for recording field observations during patrol activities. The system testing was successfully conducted by involving the users. The test results show that all features in the system are working correctly and meet the user's requirements. The mobile application has saved 36.02% of the time for recording the patrol data and 40.32% for creating the patrol reports. In addition, using the web-based application has saved time in preparing the patrol reports by 56.48%. [ABSTRACT FROM AUTHOR]

The Mediterranean climate has dry and hot summers, which is harsh for plants, especially seedlings. During the 1950s and 1960s, most reforestations carried out in Central Spain, a Mediterranean climate area, were successful, but in recent decades an increasing difficulty in forest regeneration has been observed, often attributed to increased summer drought. This study analyses changes in climatic parameters related to forest regeneration through statistical treatment of meteorological data series from the mid-twentieth century to the present. Simple and multiple regressions and ANOVAs were performed for five parameters, considering annual, summer and extended summer values. Rainfall reduction and prolongation of the summer drought period were not statistically significant. The change that better explains regeneration problems is the increase in temperature, especially in July and August, which was mostly significant between 2002 and 2021. Raising temperatures increase the vapor pressure deficit, exacerbating drought effects and plant mortality. Climate change scenarios point to an increase in temperatures until 2100; thus, the tipping point for natural regeneration of some species could be passed. The most affected species are those at their ecological limit. It is necessary to facilitate the adaptation of these forests to climate change, since their future will depend on the actions carried out today. [ABSTRACT FROM AUTHOR]

In this study, the effect of capitalization premium in forest estate markets on forest management and climate change mitigation economics is investigated. It is shown that proportional goodwill in capitalization induces linear scaling of the financial return, without any contribution to sound management practices. However, there is a financial discontinuity, as harvesting deteriorates goodwill. Such deterioration might be partially avoided by entering the real estate market. Conversely, a capitalization premium set on bare land as a tangible asset would increase timber storage and carbon sequestration. Observations indicate that the proportional goodwill is closer to reality within the Nordic Region, resulting in continuity problems. [ABSTRACT FROM AUTHOR]

Global climate change is becoming increasingly prominent and has already begun to influence natural biological systems. Assessing the potential impact of climate change on ecosystems is an important research topic of the International Geosphere-Biosphere Programme (IGBP). Based on current distribution data, climate data, climate change scenarios (RCP8.5 scenario, 2070–2099), and application of the MaxEnt model, this study assessed suitability and sensitivity of the potential distribution of Cyclobalanopsis glauca forests under climate change conditions in Guizhou Province. The results were as follows: (1) Area under the curve values of training data and texting data indicated excellent performance of the model; (2) Compared to the current climate, areas of probability <0.4 were decreased, and the other areas presented an increasing trend under the RCP8.5 scenario; (3) Positive sensitivity areas were much larger than negative sensitivity areas under climate change. In either case, slight sensitivity areas accounted for the largest proportion; (4) The mean altitude of slight sensitivity areas measured the lowest, and highly negative sensitivity areas were the highest. [ABSTRACT FROM AUTHOR]

Forest loss and degradation are central problems in the context of climate change and biodiversity conservation. The identification of areas of loss relies on accurate base maps. Central datasets in this context are the products of the Global Land Analysis & Discovery (GLAD) project. Although the GLAD forest cover products are primarily intended to serve as a near real-time flag for areas of forest loss, its historic datasets are increasingly also being used in ways that go beyond this initial focus. To date, very little information is available on the performance of GLAD data in temperate regions. This study aims to address this research gap by comparing the GLAD baseline forest cover maps for the years 2000 and 2010 with UK national forest datasets. The results showed substantial commission errors, which highlight potential problems when deviating from the GLAD datasets' intended use. GLAD data appear to be less useful in regions with a high proportion of medium to low-density canopy cover. In such cases, its application in forest models should only be used in conjunction and cross-calibration with good quality reference data. [ABSTRACT FROM AUTHOR]

The characterization of the microclimatic conditions is fundamental for the preventive conservation of archaeological sites. In this context, the identification of the factors that influence the thermo-hygrometric equilibrium is key to determine the causes of cultural heritage deterioration. In this work, a characterization of the thermo-hygrometric conditions of Casa di Diana (Ostia Antica, Italy) is carried out analyzing the data of temperature and relative humidity recorded by a system of sensors with high monitoring frequency. Sensors are installed in parallel, calibrated and synchronized with a microcontroller. A data set of 793,620 data, arranged in a matrix with 66,135 rows and 12 columns, was used. Furthermore, the influence of human impact (visitors) is evaluated through a multiple linear regression model and a logistic regression model. The visitors do not affect the environmental humidity as it is very high and constant all the year. The results show a significant influence of the visitors in the upset of the thermal balance. When a tourist guide takes place, the probability that the hourly temperature variation reaches values higher than its monthly average is 10.64 times higher than it remains equal or less to its monthly average. The analysis of the regression residuals shows the influence of outdoor climatic variables in the thermal balance, such as solar radiation or ventilation. [ABSTRACT FROM AUTHOR]

Urban ventilation plays an important role in regulating city climate and air quality. A numerical study was conducted to explore the ventilation effectiveness on the microclimate and pollutant removal in the urban street canyon based on the rebuilt Southern New Town region in Nanjing, China. The RNG k - ε turbulence model in the computational fluid dynamics (CFD) was employed to study the street canyon under parallel and perpendicular wind directions, respectively. Velocity inside of the street canyon and temperature on the building envelopes were obtained. A novel pressure coefficient was defined, and three methods were applied to evaluate the urban ventilation effectiveness. Results revealed that there was little comfort difference for the human body under two ventilation patterns in the street canyon. Air stagnation occurred easily in dense building clusters, especially under the perpendicular wind direction. In addition, large pressure coefficients (CP > 1) appeared at the windward region, contributing to promising ventilation. The air age was introduced to evaluate the "freshness" of the air in the street canyon and illustrated the ventilation effectiveness on the pollutant removal. It was found that the young air distributed where the corresponding ventilation was favorable and the wind speed was large. The results from this study can be useful in further city renovation for the street canyon construction and municipal planning. [ABSTRACT FROM AUTHOR]

In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production. This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system’s capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process. A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable. [ABSTRACT FROM AUTHOR]

Ground-based proximal sensing of vineyard features is gaining interest due to its ability to serve in even quite small plots with the advantage of being conducted concurrently with normal vineyard practices (i.e., spraying, pruning or soil tilling) with no dependence upon weather conditions, external services or law-imposed limitations. The purpose of the present work was to test performance of the new terrestrial multi-sensor MECS-VINE® in terms of reliability and degree of correlation with several canopy growth and yield parameters in the grapevine. MECS-VINE®, once conveniently positioned in front of the tractor, can provide simultaneous assessment of growth features and microclimate of specific canopy sections of the two adjacent row sides. MECS-VINE® integrates a series of microclimate sensors (air relative humidity, air and surface temperature) with two (left and right) matrix-based optical RGB imaging sensors and a related algorithm, termed Canoyct). MECS-VINE® was run five times along the season in a mature cv. Barbera vineyard and a Canopy Index (CI, pure number varying from 0 to 1000), calculated through its built-in algorithm, validated vs. canopy structure parameters (i.e., leaf layer number, fractions of canopy gaps and interior leaves) derived from point quadrat analysis. Results showed that CI was highly correlated vs. any canopy parameter at any date, although the closest relationships were found for CI vs. fraction of canopy gaps (R² = 0.97) and leaf layer number (R² = 0.97) for data pooled over 24 test vines. While correlations against canopy light interception and total lateral leaf area were still unsatisfactory, a good correlation was found vs. cluster and berry weight (R² = 0.76 and 0.71, respectively) suggesting a good potential also for yield estimates. Besides the quite satisfactory calibration provided, main improvements of MECS-VINE® usage versus other current equipment are: (i) MECS-VINE® delivers a segmented evaluation of the canopy up to 15 different sectors, therefore allowing to differentiate canopy structure and density at specific and crucial canopy segments (i.e., basal part where clusters are located) and (ii) the sensor is optimized to work at any time of the day with any weather condition without the need of any supplemental lighting system. [ABSTRACT FROM AUTHOR]

This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Réunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Réunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Réunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts. [ABSTRACT FROM AUTHOR]

Economic growths lead to population increases in large cities. This has brought about the growing necessity for apartment housing which has resulted in higher density populations living in high-rise apartment complexes. Therefore, the urban microclimate is aggravated due to the increasing ratio of artificial coverage and substandard daylight availability. To achieve a comfortable living environment and improve urban microclimates, a process considering the daylight availability and the outdoor thermal environment is required when designing apartment housing complexes. This study selected a total of 27 valid cases using an orthogonal array, L27(313) design of experiments (DOE). As a result of significance probability obtained in DOE analysis, the design factors that have an effect on the outdoor thermal environment and daylight availability were found to be building coverage ratio, distance between buildings, and azimuth. The rankings of the effect of design factors were shown to be in the order of azimuth > building coverage ratio > distance between buildings > floor area ratio > width/depth ratio. The surface temperature of the whole building decreased by 0.3 °C and Mean Radiant Temperature (MRT) decreased by 1.1 °C as a result of applying the greenery coverage ratio to apartment complexes. Heat Island Potential (HIP) also showed a decrease of 5.4 °C (at noon). [ABSTRACT FROM AUTHOR]

A recent re-evaluation of urban heat island (UHI) studies has suggested that the urban effect may be expressed more meaningfully as a difference between Local Climate Zones (LCZ), defined as areas with characteristic dimensions of between one and several kilometers that have distinct effects on climate at both micro-and local-scales (city streets to neighborhoods), rather than adopting the traditional method of comparing urban and rural air temperatures. This paper reports on a UHI study in Dublin (Ireland) which maps the urban area into LCZ and uses these as a basis for carrying out a UHI study. The LCZ map for Dublin is derived using a widely available land use/cover map as a basis. A small network of in-situ stations is deployed into different LCZ across Dublin and additional mobile temperature traverses carried out to examine the thermal characteristics of LCZ following mixed weather during a 1 week period in August 2010. The results show LCZ with high impervious/building coverage were on average >4 °C warmer at night than LCZ with high pervious/vegetated coverage during conditions conducive to strong UHI development. The distinction in mean LCZ nocturnal temperature allows for the generation of a heat map across the entire urban area. [ABSTRACT FROM AUTHOR]

Forest is the largest vegetation carbon pool in the global terrestrial ecosystem. The spatial distribution and change of forest biomass are of importance to reveal the surface spatial variation and driving factors, to analyze and evaluate forest productivity, and to evaluate ecological function of forest. In this study, broad-leaved forests located in a typical state nature reserve in northern subtropics were selected as the study area. Based on ground survey data and high-resolution remote sensing images, three machine learning models were used to identify the best remote sensing quantitative inversion model of forest biomass. The biomass of broad-leaved forest with 30-m resolution in the study area from 1998 to 2016 was estimated by using the best model about every two years. With the estimated biomass, multiple leading factors to cause biomass temporal change were then identified from dozens of remote sensing factors by investigating their nonlinear correlations. Our results showed that the artificial neural network (ANN) model was the best (R2 = 0.8742) among the three, and its accuracy was also much higher than that of the traditional linear or nonlinear models. The mean biomass of the broad-leaved forest in the study area from 1998 to 2016 ranged from 90 to 145 Mg ha−1, showing an obvious temporal variation. Instead of biomass, biomass change (BC) was studied further in this research. Significant correlations were found between BC in broad-leaved forest and three climate factors, including average daily maximum surface temperature, maximum precipitation, and maximum mean temperature. It was also found that BC has a strong correlation with the biomass at the previous time (i.e., two years ago). Those quantitative correlations were used to construct a linear model of BC with high accuracy (R2 = 0.8873), providing a new way to estimate the biomass change of two years later based on the observations of current biomass and the three climate factors. [ABSTRACT FROM AUTHOR]

Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests' capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. [ABSTRACT FROM AUTHOR]

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them. [ABSTRACT FROM AUTHOR]

In the modern world, the fine balance and delicate relationship between human society and the environment in which we exist has been affected by the phenomena of urbanisation and urban development. Today, various environmental factors give rise to horizontal dispersion, spread and growth of cities. One of the most important results of this is climatic change which is directly affected by the urban sprawl of every metropolis. The aim of this study is to identify the relationship between the various horizontally distributed components of Tehran city and changes in essential microclimate clusters, by means of the humidex index. Results showed that, when the humidex was calculated for each of the obtained clusters, it was evident that it had increased with time, in parallel with Shannon's entropy, as a consequence of the average temperature and relative humidity of each cluster. At the same time, results have shown that both temperature and relative humidity of the study area are related with urban sprawl, urbanisation and development, as defined by Shannon's entropy and, in consequence, with humidex. In consequence, this new concept must be considered in future research works to predict and control urban sprawl and microclimate conditions in cities. [ABSTRACT FROM AUTHOR]

Micro-relief heterogeneity can lead to substantial variability in microclimate and hence niche opportunities on a small scale. We explored the relationship between plant species richness and small-scale heterogeneity of micro-relief on the subtropical island of La Palma, Canary Islands. Overall, we sampled 40 plots in laurel and pine forests at four altitudinal bands. Species richness was recorded separately for various growth forms (i.e., mosses, herbaceous and woody plants). Site conditions such as altitude, slope, aspect, and tree density were measured. Micro-relief heterogeneity was characterized by surface structure and a subsequently derived surface heterogeneity index. The effect of micro-relief heterogeneity on species richness was analysed by means of linear mixed effect models and variance partitioning. Effects of micro-relief heterogeneity on species richness varied considerably between growth forms. While moss richness was affected significantly by micro-relief heterogeneity, herbaceous and woody plants richness responded mainly to larger-scale site conditions such as aspect and tree density. Our results stress the importance of small-scale relief heterogeneity for the explanation of spatial patterns of species richness. This poses new challenges as small-scale heterogeneity is largely underrepresented, e.g. with regard to its application in species distribution models. [ABSTRACT FROM AUTHOR]

Forests account for 60% of lands in Taiwan. Climate change impacts forests in many aspects and is increasingly likely to undermine the ability of forests to provide basic ecosystem services. To help reduce the impact of climate change on Taiwan's forests, people must be made aware of the relationship between climate change and forests. Based on questionnaires collected from 17 cities in Taiwan, this study applied spatial analysis to assess the respondents' understanding of climate change and adaptation strategies for forest management. A total of 650 questionnaires were distributed and 488 valid ones were collected. The results show that (1) Most respondents believe that climate change is true and more than half of the respondents have experienced extreme weather events, especially extreme rainfall; (2) Most respondents believe that climate change will affect Taiwan's forests with the majority recognizing the increasing impact of extreme events being the primary cause, followed by changes in the composition of tree species and the deterioration of forest adaptability due to climate change; (3) Most respondents expressed that forest management should be adjusted for climate change and called for measures being taken to establish mixed forests as well as monitoring forest damage; (4) In order to address the difficulties faced by forest owners on the impact of climate change, the majority of respondents felt that the government should raise forest owners' understanding on climate change and adaptation policies, while the subsidy incentives must also be adjusted. The results of this study show that the respondents do realize the importance of climate change and forest management so much so their awareness in this matter led to their support for forest adaptation measures and policies. [ABSTRACT FROM AUTHOR]

Forests produce goods and services, but the forest economy is based on goods, with market price, more than on services. Under Mediterranean climate conditions forests have low timber production, being frequently financially loss-making, despite the environmental services provided, such as carbon sequestration. Timber production and carbon sequestration are compatible, and a proper valuation of both can allow for a more balanced management. The aim of this paper is to assess financially a scenario based on maximizing carbon sequestration versus another based on maximizing timber harvesting in a Mediterranean forest. To do that, timber stock, growth and harvesting, and carbon sequestration have been calculated. Applying market prices for timber and CO2 both scenarios have been assessed, carrying out a sensitivity analysis. Maximising carbon sequestration was more profitable in the vast majority of combinations; timber harvesting was only more profitable if CO2 prices fell below 30% and timber price increases more than 20%; timber price rise is possible, but a collapse in CO2 price is not probable. The real barrier is that while timber is as a commodity with market price, carbon sequestration is not. The challenge for the future is to pay for carbon sequestration, mobilising resources from polluting sectors to forests. [ABSTRACT FROM AUTHOR]

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya's forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha−1 yr−1) than wooded grasslands (0.6 t C ha−1 yr−1), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks. [ABSTRACT FROM AUTHOR]

The magnitude of net carbon dioxide emissions resulting from global forest carbon change, and hence the contribution of forests to global climate change, is highly uncertain, owing to the lack of direct measurement by Earth observation and ground data collection. This paper uses a new method to evaluate this uncertainty with greater precision than before. Sources of uncertainty are divided into conceptualization and measurement categories and distributed between the spatial, vertical and temporal dimensions of Earth observation. The method is applied to Forest Reference Emission Level (FREL) reports and National Greenhouse Gas Inventories (NGGIs) submitted to the UN Framework Convention on Climate Change (UNFCCC) by 12 countries containing half of tropical forest area. The two sets of estimates are typical of those to be submitted to the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism of the UNFCCC and the 2023 Global Stocktake of its Paris Agreement, respectively. Assembling the Uncertainty Fingerprint of each estimate shows that Uncertainty Scores are between 10 and 14 for the NGGIs and 5 and 10 for the FREL reports, and so both exceed the threshold of 2 when it is advisable to evaluate uncertainty by standard statistical methods. Conceptualization uncertainties account for 60% of all uncertainties in the NGGIs and 47% in the FREL reports, e.g., there is incomplete coverage of forest carbon fluxes, and limited disaggregation of fluxes between different ecosystem types and forest carbon pools. Of the measurement uncertainties, all FREL reports base forest area estimates on at least medium resolution satellite data, compared with only 3 NGGIs; after REDD+ Readiness schemes, mean area mapping frequency has fallen to 2.3 years in Latin America and 3.0 years in Asia, but only 8.3 years in Africa; and carbon density estimates are based on national forest inventory data in all FREL reports but only 4 NGGIs. The effectiveness of the Global Stocktake and REDD+ monitoring will therefore be constrained by considerable uncertainties, and to reduce these requires a new phase of REDD+ Readiness to ensure more frequent national forest inventories and forest carbon mapping. [ABSTRACT FROM AUTHOR]

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth's surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming. [ABSTRACT FROM AUTHOR]

The manuscript reviews the current literature on scattering applications of RADAR (Radio Detecting And Ranging) in remote sensing and diagnostics. This paper gives prime features for a variety of RADAR applications ranging from forest and climate monitoring to weather forecast, sea status, planetary information, and mapping of natural disasters such as the ones caused by earthquakes. Both the fundamental parameters involved in scattering mechanisms of RADAR applications and the factors affecting RADAR performances are also discussed. [ABSTRACT FROM AUTHOR]

Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due to fire also offer opportunities for mapping burned areas using three-dimensional (3D) datasets such as Light detection and ranging (LiDAR). This study focuses on the feasibility of using photon counting LiDAR data from National Aeronautics and Space Administration's (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat−2) mission to differentiate vegetation structure in burned and unburned areas and ultimately classify burned areas along mapped ground tracks. The ICESat−2 mission (launched in September 2018) provides datasets such as geolocated photon data (ATL03), which comprises precise latitude, longitude and elevation of each point where a photon interacts with land surface, and derivative products such as the Land Water Vegetation Elevation product (ATL08), which comprises estimated terrain and canopy height information. For analysis, 24 metrics such as the average, median and standard deviation of canopy height were derived from ATL08 data over forests burned by recent fires in 2018 in northern California and western New Mexico. A reference burn map was derived from Sentinel−2 images based on the differenced Normalized Burn Ratio (dNBR) index. A landcover map based on Sentinel−2 images was employed to remove non-forest classes. Landsat 8 based dNBR image and landcover map were also used for comparison. Next, ICESat−2 data of forest samples were classified into burned and unburned ATL08 100-m segments by both Random Forest classification and logistic regression. Both Sentinel−2 derived and Landsat 8 derived ATL08 samples got high classification accuracy, 83% versus 76%. Moreover, the resulting classification accuracy by Random Forest and logistic regression reached 83% and 74%, respectively. Among the 24 ICESat−2 metrics, apparent surface reflectance and the number of canopy photons were the most important. Furthermore, burn severity of each ATL08 segment was also estimated with Random Forest regression. R2 of predicted burn severity to observed dNBR is 0.61 with significant linear relationship and moderate correlation (r = 0.78). Overall, the reasonably high accuracies achieved in this study demonstrate the feasibility of employing ICESat−2 data in burned forest classification, opening avenues for improved estimation of burned biomass and carbon emissions from a 3D perspective. [ABSTRACT FROM AUTHOR]

Most human energy budget models consider a person to be approximately cylindrical in shape when estimating or measuring the amount of radiation that they receive in a given environment. Yet, the most commonly used instrument for measuring the amount of radiation received by a person is the globe thermometer. The spherical shape of this instrument was designed to be used indoors where radiation is received approximately equally from all directions. But in outdoor environments, radiation can be strongly directional, making the sphere an inappropriate shape. The international standard for measuring radiation received by a person, the Integral Radiation Measurement (IRM) method, yields a measure of the Mean Radiant Temperature (Tmrt). This method uses radiometers oriented in the four cardinal directions, plus up and down. However, this setup essentially estimates the amount of energy received by a square peg, not a cylinder. This paper identifies the errors introduced by both the sphere and the peg, and introduces a set of two new instrument that can be used to directly measure the amount of radiation received by a vertical cylinder in outdoor environments. The Cylindrical Pyranometer measures the amount of solar radiation received by a vertical cylinder, and the Cylindrical Pyrgeometer measures the amount of terrestrial radiation received. While the globe thermometer is still valid for use in indoor environments, these two new instruments should become the standard for measuring radiation received by people in outdoor environments. [ABSTRACT FROM AUTHOR]

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions. [ABSTRACT FROM AUTHOR]

Strong El Niño events alter tropical climates and may lead to a negative carbon balance in tropical forests and consequently a disruption to the global carbon cycle. The complexity of tropical forests and the lack of data from these regions hamper the assessment of the spatial distribution of El Niño impacts on these ecosystems. Typically, maps of climate anomaly are used to detect areas of greater risk, ignoring baseline climate conditions and forest cover. Here, we integrated climate anomalies from the 1982–1983, 1997–1998, and 2015–2016 El Niño events with baseline climate and forest edge extent, using a risk assessment approach to hypothetically assess the spatial and temporal distributions of El Niño risk over tropical forests under several risk scenarios. The drivers of risk varied temporally and spatially. Overall, the relative risk of El Niño has been increasing driven mainly by intensified forest fragmentation that has led to a greater chance of fire ignition and increased mean annual air temperatures. We identified areas of repeated high risk, where conservation efforts and fire control measures should be focused to avoid future forest degradation and negative impacts on the carbon cycle. [ABSTRACT FROM AUTHOR]

This article presents unmanned aerial system (UAS)-based photogrammetry as an efficient method for the estimation of snow-field parameters, including snow depth, volume, and snow-covered area. Unlike similar studies employing UASs, this method benefits from the rapid development of compact, high-accuracy global navigation satellite system (GNSS) receivers. Our custom-built, multi-sensor system for UAS photogrammetry facilitates attaining centimeter- to decimeter-level object accuracy without deploying ground control points; this technique is generally known as direct georeferencing. The method was demonstrated at Mapa Republiky, a snow field located in the Krkonose, a mountain range in the Czech Republic. The location has attracted the interest of scientists due to its specific characteristics; multiple approaches to snow-field parameter estimation have thus been employed in that area to date. According to the results achieved within this study, the proposed method can be considered the optimum solution since it not only attains superior density and spatial object accuracy (approximately one decimeter) but also significantly reduces the data collection time and, above all, eliminates field work to markedly reduce the health risks associated with avalanches. [ABSTRACT FROM AUTHOR]

Micro-scale cold-air flow along a gentle slope was analyzed using thermal infrared imaging (TIR), focusing exclusively on the lowermost 2 m above ground. Cold-air pulses were analyzed with regard to their vertical temperature stratification as well as flow characteristics, such as flow speed. Analyses on the transition zone between the near-surface very stable inversion layer and the less stable, warmer air above highlight turbulent situations and detrainment effects at the cold-air inversion top. Using thermal imaging in a high spatiotemporal resolution with up to 90 vertical data points and TIR pixels for 1.5 m cold-air depth, a high-precision cold-air flow analysis was realized. [ABSTRACT FROM AUTHOR]

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 5.82%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data. [ABSTRACT FROM AUTHOR]

Urban trees can significantly improve the outdoor thermal environment, especially in subtropical zones. However, due to the lack of fundamental evaluations of numerical simulation models, design and modification strategies for optimizing the thermal environment in subtropical hot-humid climate zones cannot be proposed accurately. To resolve this issue, this study investigated the physiological parameters (leaf surface temperature and vapor flux) and thermal effects (solar radiation, air temperature, and humidity) of four common tree species (Michelia alba, Mangifera indica, Ficus microcarpa, and Bauhinia blakeana) in both spring and summer in Guangzhou, China. A comprehensive comparison of the observed and modeled data from ENVI-met (v4.2 Science, a three-dimensional microclimate model) was performed. The results show that the most fundamental weakness of ENVI-met is the limitation of input solar radiation, which cannot be input hourly in the current version and may impact the thermal environment in simulation. For the tree model, the discrepancy between modeled and observed microclimate parameters was acceptable. However, for the physiological parameters, ENVI-met tended to overestimate the leaf surface temperature and underestimate the vapor flux, especially at midday in summer. The simplified calculation of the tree model may be one of the main reasons. Furthermore, the thermal effect of trees, meaning the differences between nearby treeless sites and shaded areas, were all underestimated in ENVI-met for each microclimate variable. This study shows that the tree model is suitable in subtropical hot-humid climates, but also needs some improvement. [ABSTRACT FROM AUTHOR]

The Tokyo 2020 Olympic Games will be held in July and August. As these are the hottest months in Tokyo, the risk of heat stress to athletes and spectators in outdoor sporting events is a serious concern. This study focuses on the marathon races, which are held outside for a prolonged time, and evaluates the potential heat stress of marathon runners using the COMFA (COMfort FormulA) Human Heat Balance (HBB) Model. The study applies a four-step procedure: (a) measure the thermal environment along the marathon course; (b) estimate heat stress on runners by applying COMFA; (c) identify locations where runners may be exposed to extreme heat stress; and (d) discuss measures to mitigate the heat stress on runners. On clear sunny days, the entire course is rated as ‘dangerous’ or ‘extremely dangerous’, and within the latter half of the course, there is a 10-km portion where values continuously exceed the extremely dangerous level. Findings illustrate which stretches have the highest need for mitigation measures, such as starting the race one hour earlier, allowing runners to run in the shade of buildings or making use of urban greenery including expanding the tree canopy. [ABSTRACT FROM AUTHOR]

This paper presents research carried out in the city of Chillán, a medium size city located on the southern limit of the Chilean Mediterranean domain, at 36°36′s south latitude. Chillán provides a good representative example of warm summers in central and southern Chilean cities. Five public spaces were selected, representing different typologies and relating to different urban background conditions. Users in these public spaces were observed, counted and photographed five times a day (12, 14, 16, 18 and 20 h, local time) during a heat wave event in the summer of 2016, while meteorological parameters were measured at different points within the public space. The variables evaluated were impervious surfaces, sky view factor, H/W, azimuth, shadow, and radiation. Local public environmental management should pay attention to the complex relations between urban climate, public spaces and thermal comfort since they affect the quality of life of the most vulnerable sectors of the population. This is particularly important given the increasing episodes of elevated temperatures and intense heat waves which have occurred in the city of Chillán in recent summers, which are related to urban heat islands and climate change. [ABSTRACT FROM AUTHOR]