Forest Research Articles

Soil pore dynamics and infiltration characteristics as affected by cultivation duration for Mollisol in northeast China

Elucidating the effects of long-term cultivation on pore structure and infiltration characteristics is essential for understanding soil degradation mechanism and improving cropland sustainability. In this study, we evaluated a number of indicators regarding soil properties, pore structure, and water infiltration during long-term cultivation and the relationship of basic properties and pore structure with soil infiltration in northeast Mollisol region of China. The studied cropland involved four cultivation durations (20, 40, 60, and 100 years) and one forest (FR) as the control. X-ray computed tomography (CT) and mini disk infiltrometer were applied to assess the effects of long-term cultivation on soil pore structure and infiltration characteristics at soil depths of 0–100 cm. The pore properties including soil porosity (TP), pore number (PN), pore size distribution, mean shape factor (MSF), fractal dimension (FA), anisotropy (DA), and Euler number (EN), and infiltration properties including mean (IR), initial (IIR), stable infiltration rate (SIR), and saturated hydraulic conductivity (Ks) were planned to be measured in this study. The results showed that soil organic carbon (SOC) in cropland was significantly lower than that in FR, while bulk density (BD) and mean weight diameter (MWD) exhibited an opposite trend. Long-term cultivation altered the soil pore size and shape distribution, and decreased the macroporosity (>500 μm) and elongated porosity, which suggested that long-term cultivation results in a simpler pore network. FR took a longer time to reach a stable state in infiltration curve over time, while cropland had lower IR, IIR, SIR and Ks, indicating negative effects of cultivation on water infiltration. > 500 μm porosity and elongated porosity were positively correlated with infiltration characteristics (P<0.01). The infiltration parameters were mainly affected by soil pore properties (MSF, PN, and DA) and basic properties (SOC, MWD, and BD), highlighting the importance of pore morphology in the infiltration process. Our results provide new insights into the evolution of soil structure and properties and explanations for water infiltration dynamics under long-term cultivation from the microscale.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes

Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing the intricate interplay between hydrological factors, their “black box” nature makes it challenging to identify the dynamic drivers of runoff. To overcome this challenge, we employed an interpretable machine learning method to inversely deduce the dynamic determinants within hydrological processes. In this study, we analyzed land use changes in the Ningxia section of the middle Yellow River across four periods, laying the foundation for revealing how these changes affect runoff. The sub-watershed attributes and meteorological characteristics generated by the Soil and Water Assessment Tool (SWAT) model were used as input variables of the Extreme Gradient Boosting (XGBoost) model to simulate substantial sub-watershed rainfall runoff in the region. The XGBoost was interpreted using the SHapley Additive exPlanations (SHAP) to identify the dynamic responses of runoff to the land use changes over different periods. The results revealed increasingly frequent interchanges between the land use types in the study area. The XGBoost effectively captured the characteristics of the hydrological processes in the SWAT-derived sub-watersheds. The SHAP analysis results demonstrated that the promoting effect of agricultural land (AGRL) on runoff gradually weakens, while forests (FRST) continuously strengthen their restraining effect on runoff. Relevant land use policies provide empirical support for these findings. Furthermore, the interaction between meteorological variables and land use impacts the runoff generation mechanism and exhibits a threshold effect, with the thresholds for relative humidity (RH), maximum temperature (MaxT), and minimum temperature (MinT) determined to be 0.8, 25 °C, and 15 °C, respectively. This reverse deduction method can reveal hydrological patterns and the mechanisms of interaction between variables, helping to effectively addressing constantly changing human activities and meteorological conditions.

Prediction of failures in sewer networks using various machine learning classifiers

ABSTRACT In sewer networks, failure prediction plays a significant role in operation and maintenance plans of wastewater utilities. This study aims to determine the effective variables on the failures by using feature selection algorithms (FS) and achieve maximum model accuracy with minimum variables. Also, four scenarios based on the suggested FS algorithms were developed. In these scenarios, the best prediction models were investigated using machine learning classifiers (ML) such as neural network classifier (NNC), gradient boosting machine (GBM), random forest (FR), and hybrid model (HM). The classification performance of ML models was evaluated using accuracy, precision, F1_score, and receiver operating characteristics (ROC) curve. The model accuracies ranging from 0.99 for accuracy to 1 for the ROC curve were achieved through ML algorithms. In conclusion, the ML algorithms suggested in this study may be a decision support tool for wastewater utilities in prioritizing the replacement, maintenance, and inspection of sewer pipes.

Potential of integrated trees-pasture-based systems for GHG emission mitigation and improving soil carbon dynamics in the Atlantic forest biome, Southeastern of Brazil

Brazilian cattle production is mainly carried out in pastures, and mitigating the livestock's greenhouse gas (GHG) emissions has become an essential prerequisite. The importance of planted forests in beef cattle pasture-based integrated systems has been widely recognized in the context of GHG emission mitigation. Adopting the intensification and integration with forest and crops of pasture-based livestock production systems are alternatives to optimizing the soil and tree carbon (C) sequestration; however, research on tropical conditions is still necessary. This long-term trial evaluated the potential of C sequestration (soil + trees) of the forest and the grazing systems with two replicate areas of 3 ha each: 1. continuous stocking rate (CLS) – pasture under continuous stocking; 2. agropastoral or crop-livestock system (AGROP) – pasture under rotational stocking in rotation with corn; 3. agrosilvipastoral or crop-livestock-forestry system (AGROSIL) - similar to AGROP but including eucalyptus; 4. silvipastoral or livestock-forestry system (SIL) - pasture under rotational stocking + eucalyptus and 5. Atlantic Forest (FOR). Soil samples were analysed for texture, C and bulk density in eight soil layers from three to eight trenches for each treatment, then C stock and C accumulation rates were calculated. Data from Eucalyptus urograndis of the SIL and AGROSIL systems with 333 trees. ha−1 during five first years and 166 trees. ha−1 in subsequent years were collected (five- and eight-years old trees) and used to define the wood volume and acquire wood rings. Elemental analyzer CHN performed soil and trees C content. Equations for determining stem volume and tree biomass and, afterward, the C pools of the tree trunks were proposed. The potential of GHG emission mitigation (t CO2 e.. ha−1. year−1) by annual C sequestration (soil or soil + trees) was calculated, including the wood used for the lumber-mill scenario. The participation of the C stock from the 0–30 cm soil layer was between 45 % and 53 % of the total C stock in the 0–100 cm soil layer. Regardless of the elevated C soil accumulation rates, 0.88, 2.54, and 1.74 t C. ha-1. year−1 in the conversion stages CLS → AGROP; AGROP → SIL; AGROP → AGROSIL, respectively, it was not possible to compensate for the negative result of the Forest → CLS conversion (-2.55 t C. ha-1. year−1) because this stage lasted 12 years, while the implementation of AGROSIL and SIL systems had only six years. However, when C tree accumulation rates were considered, the potential of GHG emission mitigation (t CO2 e. ha−1. year−1) by annual C sequestration (soil +trees) was 19.89 to AGROSIL and 18.97 to SIL, including the wood used for the lumber-mill scenario. Based on these results, it can be concluded that C sequestration in trees-pasture integrated systems occurs in layers deeper, with the diverse species contributing in different soil layers; there is a positive effect of improved land-use management, with the use of integrated systems on annual C soil sequestration and the trees consists of an important strategy to improve the GHG mitigation.

Ethnobotanical Significance of the Flora of Neora Valley National Park in the District of Darjeeling, West Bengal (India)

Recent floristic survey of Neora Valley National Park in Kalimpong sub-division of Darjeeling district of West Bengal revealed that the park is a rich repository of the ethnobotanically important plant resources that includes species of food, medicine, ornamental, poisonous and other assorted ethnic values. Based on available literature and direct interaction with forest dwellers living in the periphery of the park, altogether 83 medicinal, 59 edible, 18 ornamental, 21 poisonous (irritants and lethal) and 11 plants having fascinating assorted ethnic uses have been recognized. The forest dwellers have their own dictionary of local names for the identification of these valuable plant resources by which they are maintaining their own ancient ethnoscience. Many of these medicinal plant species were assessed to be the valuable resources for the treatment of a range of human ailments for which there are no complete answers in the modern medical science (?).

Depleting Medicinal and Aromatic Plants of Jharkhand State

The newly created state of Jharkhand is very rich in the plant wealth and other natural resources. The area has maxium concentration of tribal population in North India. The forests have several medicinal and aromatic plants that are being used as a source of medicines in various systems viz., Ayurveda, Siddha Unani and Homoeopathy. Due to continous habitat destruction fragmentation of forests, urbanization coupled with population explosion and agricultural growth, there is unprecedented pressure on plants wealth culminating into loss of biodiversity at an alarming rate. The authors have regularly been surveying the state for the last fifteen years. Based on the field survey literature and screening ca 24 medicinal plants whose populations have been continously depleting are identified and reported in the present communication.

An Ethnobotanical Study of the Kodavas and other Tribes of Kodagu District, Karnataka

The Kodavas are the original inhabitants of Kodagu at present a district of Karnataka State - located in the Western Ghats. Although Kodavas cannot be classified as tribals under the present conditions, they form a minor ethnic group that has apparently been living amidst the forests of this region over the last few thousand years. Their knowledge of the local plants is extensive, and some of it is recorded here, with a listing of 240 species. These include some 105 medicinal plants, 65 food plants, about 60 timber-yielding plants; some 40 species used in different ways in agriculture and animal husbandry; some 80 species having agricultural and domestic uses, including fibre and fuel plants, those used in cleaning and toiletry, etc; there is some overlapping of usages. Although many of the palnts are well-known, some of the usages are not widely known. Some of the plants merit further investigation and development.

Traditional Phytotherapy amongst the Rural Communities of North Western Himalaya

This paper deals with 73 species of medicinal plants used by the traditional communities of Kullu district of north western Himalaya, forest wealth and traditional wisdom. An attempt has been made to record interesting folk-uses and medicinal plant lore used by the people at Chhakinal watershed in Kullu district of north western Himalaya.

A Contribution to the Flora of Shiroy Hills, Ukhrul, Manipur

The paper deals 49 species of flowering plants belonging to 32 families which are reported as new records from Shiroy hills. Forest vegetation at the ranges of altitudes and a short description of the species, locality, date of collection, field number and altitude were described.

Notes on Two Rare Orchids of Meghalaya

Vanda alpina Lindl. Vanda pumila Hk. f. have been collected from the forests of Sonapahar (Khasia hills). Studies show that these two species are rare and recorded here for their extended distribution.

Soil health indicators in oil palm agroforestry systems in the eastern Amazon, Brazil

Agroforestry systems (AFSs) are generally recognized for improving soil health and increasing soil carbon (C) stock. In the Amazon region, oil palm AFSs have been promoted as a productive strategy and may offer an additional benefit of recovering degraded areas. Our objective was to evaluate whether the management practices adopted in oil palm AFSs affect soil health indicators and C stock. We carried out a study in Tomé-Açu, state of Pará, eastern Amazon, through which we evaluated two oil palm AFSs that differed in species diversity: higher diversity (AFShigh) and lower diversity (AFSlow). We used a regenerating forest (FOR) to compare with the oil palm AFSs, as regenerating forests are also a model of environmental recovery in the eastern Amazon. In the 0–10, 10–20 and 20–30 cm soil layers, we evaluated chemical indicators (active acidity (pH), calcium (Ca), magnesium (Mg), potassium (K), phosphorus (P), and aluminum (Al)), (b) physical indicators (density (SD) and stability of aggregates (AGGRE)), and (c) biological indicators (permanganate oxidizable carbon (POXC), particulate organic carbon (POC), total soil carbon (SOC content) and root stock (ROOT)). We also evaluated the total soil carbon stock (SOC stock). The values of the biological indicators in the most superficial layer, especially the C indicators, were higher in the AFSlow (C content: 1.63 ± 0.13%, POXC: 568 ± 17 mg kg−1, POC: 0.63 ± 0.10 g kg−1) and the AFShigh (C content: 1.84 ± 0.04%, POXC: 656 ± 24 mg kg−1, POC: 0.83 ± 0.06 g kg−1) than in FOR (C content: 1.35 ± 0.09%, POXC: 336 ± 10 mg kg−1, POC: 0.44 ± 0.03 g kg−1). In general, the values of chemical indicators were higher in the AFSs than in FOR, except for the Al content, which was higher in FOR than in the AFSs. The physical indicators did not show a specific pattern of variation between the AFSs and FOR; aggregate stability was higher in FOR (range throughout the soil profile: 5.57 ± 0.09 to 5.38 ± 0.24 mm) than in the AFSlow (5.41 ± 0.03 to 3.79 ± 0.07 mm) and the AFShigh (5.07 ± 0.01 to 3.79 ± 0.11 mm) in all the soil layers, and soil density was higher in the AFShigh and FOR than in the AFSlow in most subsurface layers. Therefore, oil palm AFSs improve the chemical and biological health of the soil, especially in the more superficial layers, but it does not improve the physical health of the soil when compared to the forest.

Key thematic groups of vocabulary in the comedy

INTRODUCTION. A comedy in five acts by the Russian dramatist Alexander Nikolaevich Ostrovsky “The Forest” (1823–1886) is one of the most perfect and complex in terms of plot structure of the Russian playwright. The relevance of this study is determined by the need to study the language of one of the most complex plays by A.N. Ostrovsky, the lexical and phraseological features of which have not received detailed coverage in domestic science. The purpose of the study is to identify the key thematic groups of vocabulary in the comedy of A.N. Ostrovsky “The Fo rest”.MATERIALS AND METHODS. During the analysis of linguistic units, structural-semantic and linguostylistic research methods are used.RESULTS AND DISCUSSION. Analysis of the lexical-semantic organization of A.N. Ostrovsky’s comedy “The Forest” showed that the words and phraseological units of the key semantic fields “Theater” and “The Forest” reflect the problems and conflict of the play, evaluate the characters of the work, their actions and deeds.CONCLUSION. The identified words and phraseological units, objectified by theatrical vocabulary, vocabulary of flora and fauna, are text-forming in nature and reflect the solutions to the artistic problems of the author, who uses linguistic units in both direct and figurative meanings, which often expands the semantic scope of linguistic signs.

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of &gt;0.99), and similarly a high level of accuracy (&gt;0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022.

An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings

Indoor thermal conditions are essential in educational buildings. The health and well-being of students can be affected by a poor thermal environment, which also has a clear impact on building energy consumption. In this context, the indoor thermal environment in naturally ventilated university classrooms is explored in this study during a complete academic year. A monitoring campaign and a questionnaire survey were conducted simultaneously in higher education buildings in Spain. A total of 2115 sets of data were collected. Thermal sensation prediction indices (predicted mean vote, extended predicted mean vote and adaptive predicted mean vote) were applied to evaluate student’s thermal perception and their prediction accuracy was assessed. Additionally, two machine-learning models, based on Artificial neural network (ANN) and random forest (RF) algorithms, were formulated to predict occupants’ thermal sensation. The obtained results evidenced that the proposed ANN and RF models outperform traditional indices. Finally, it is also proposed an adaptive thermal comfort model. The results obtained suggest that students have a greater adaptive capacity to changes in environmental conditions than suggested by the ASHRAE-55 adaptive model and that they preferred an environment with lower temperatures than those suggested by the EN-16798 adaptive model.

COMPARISON OF CARBON DIOXIDE EFFLUX IN DIFFERENT LAND USE TYPOLOGIES

Management systems are crucial for soils functioning as a drain or source of greenhouse gas emissions. In this sense, among the alternatives for reducing the precursors of the greenhouse effect are integrated agricultural production systems, particularly Agroforestry Systems. These integrated systems maximize the use of biological cycles, soil, and animal waste, reducing agrochemicals and improving the condition of the rural populations involved. The present study aimed to evaluate the CO2 efflux in different land use types. The study was conducted in four agroecosystems: Forest (FO), Agroforestry Systems (AFS), Mandala (MD), and Degraded area (DA). To quantify the CO2 emanating from the soil, 10 mL of 0.5 N KOH aqueous solution is used and titrated using 0.1 N HCl, phenolphthalein, and 1% methyl orange as an indicator. The CO2 concentration from 5:00 am to 5:00 pm was lower. The lowest CO2 production occurred in FO (82.7 CO2 mg m-2 h-1), statistically differing from the other areas that obtained a CO2 rate higher than 140 mg m-2 h-1. As for kinetics, it was observed in all areas that the greatest release of CO2 during the day occurred at 7:00 am, and this release decreased as the soil temperature increased. The greatest release of CO2 and the highest temperatures occurred in DA, showing that these systems with anthropic modifications can be considered CO2 emitters however, systems closer to the natural ecosystem, such as the AFS, will store carbon in the soil, functioning as a carbon store. Therefore, to reduce CO2 emissions from agricultural activities, it is necessary to use systems that imitate natural ecosystems.

Monotonic trends of soil microbiomes, metagenomic and metabolomic functioning across ecosystems along water gradients in the Altai region, northwestern China

To investigate microbial communities and their contributions to carbon and nutrient cycling along water gradients can enhance our comprehension of climate change impacts on ecosystem services. Thus, we conducted an assessment of microbial communities, metagenomic functions, and metabolomic profiles within four ecosystems, i.e., desert grassland (DG), shrub-steppe (SS), forest (FO), and marsh (MA) in the Altai region of Xinjiang, China. Our results showed that soil total carbon (TC), total nitrogen, NH4+, and NO3− increased, but pH decreased with soil water gradients. Microbial abundances and richness also increased with soil moisture except the abundances of fungi and protists being lowest in MA. A shift in microbial community composition is evident along the soil moisture gradient, with Proteobacteria, Basidiomycota, and Evosea proliferating but a decline in Actinobacteria and Cercozoa. The β-diversity of microbiomes, metagenomic, and metabolomic functioning were correlated with soil moisture gradients and have significant associations with specific soil factors of TC, NH4+, and pH. Metagenomic functions associated with carbohydrate and DNA metabolisms, as well as phages, prophages, TE, plasmids functions diminished with moisture, whereas the genes involved in nitrogen and potassium metabolism, along with certain biological interactions and environmental information processing functions, demonstrated an augmentation. Additionally, MA harbored the most abundant metabolomics dominated by lipids and lipid-like molecules and organic oxygen compounds, except certain metabolites showing decline trends along water gradients, such as N′-Hydroxymethylnorcotinine and 5-Hydroxyenterolactone. Thus, our study suggests that future ecosystem succession facilitated by changes in rainfall patterns will significantly alter soil microbial taxa, functional potential, and metabolite fractions.

A multidisciplinary approach to facies evaluation at regional level using well log analysis, machine learning, and statistical methods

Geological facies evaluation is crucial for the exploration and development of hydrocarbon reservoirs. To achieve accurate predictions of litho-facies in wells, a multidisciplinary approach using well log analysis, machine learning, and statistical methods was proposed for the Lower Indus Basin. The study utilized five supervised machine learning techniques, including Random Forest (FR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP), to analyse gamma ray, resistivity, density, neutron porosity, acoustic, and photoelectric factor logs. The Concentration-Number (C-N) fractal model approach and log–log plots were also used to define geothermal features. In a study on machine learning models for classifying different rock types in the Sawan field of the Southern Indus Basin, it was discovered that sand (fine, medium and coarse) facies were most accurately classified (87–94%), followed by shale (70–85%) and siltstone facies (65–79%). The accuracy of the machine learning models was assessed using various statistical metrics, such as precision, recall, F1 score, and ROC curve. The study found that all five machine learning methods successfully predicted different litho-facies in the Lower Indus Basin. In particular, sand facies were most accurately classified, followed by shale and siltstone facies. The multilayer perceptron method performed the best overall. This multidisciplinary approach has the potential to save time and costs associated with traditional core analysis methods and enhance the efficiency of hydrocarbon exploration and development.

Forest fees paid to permit mining extractive operations on Turkey’s forestlands &amp; the ratio to investments

Forest fees paid to permit mining extractive operations on Turkey’s forestlands &amp; the ratio to investments

Effects of deforestation on microclimate in a Cerrado-Amazonia Transition area

The Cerrado-Amazon Transition region has a high deforested area in Brazil. Given the importance of the forest in maintaining the climate of this region, were evaluated the patterns of micrometeorological variables in forested and deforested areas in the Cerrado-Amazon Transition region in Mato Grosso, Brazil. Precipitation, solar radiation, average, minimum and maximum air temperature, relative air humidity, soil temperature and wind speed were measured into a forest (FOR) and in a deforested area (DEF). Precipitation in the studied region has a hyper-seasonal pattern with 95% of the volume in the wet season (October to April), which influenced the seasonality of the micrometeorological variables. Solar radiation in DEF was 8-folds higher than in FOR, air temperature in DEF was up to 11% higher than in FOR, relative humidity in FOR was up to 14% higher than in DEF, soil temperature in DEF was 18% greater than in FOR and wind speed in DEF was 22-folds greater than in FOR. Deforestation significantly influenced the seasonality and magnitude of the analyzed micrometeorological variables.