Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil

Environmental resilience through artificial intelligence: innovations in monitoring and management

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI's potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI's remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI's pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.

Physical and chemical properties of Coarse Woody Debris submitted to the natural process of decomposition in a Secondary Atlantic Forest Fragment in Brazil

Coarse Woody Debris (CWDs) are constantly exposed to the natural decomposition process of wood, which can lead to a change in its physical-chemical properties. However, these changes have not yet been fully elucidated, requiring further studies to help to understand the effect of this process on CWDs degradation. Thus, the objectives of this study were: (i) verify if the decomposition affects the physical-chemical properties of the CWDs; (ii) verify if the structural chemical composition of the CWDs is altered as a function of decomposition, using immediate chemical and thermogravimetric analysis. Wood samples were collected from the CWDs to carry out these analyses, considering pieces with diameters ≥ 5 cm separated into 4 decay classes. The results indicated that the average apparent density decreased as a function of the increase of CWDs decomposition (0.62-0.37 g cm^-3). The averages contents of Carbon and Nitrogen suffered less impact with the increase of CWDs decompositions, ranging from 49.66 to 48.80% and 0.52 to 0.58%, respectively. Immediate chemical and thermogravimetric analysis indicated a loss of holocelluloses and extractives and an increase in the concentration of lignin and ash throughout the decomposition process. The weight loss analyzed by thermogravimetric analysis was greater for less decomposed CWDs and with larger diameters. The use of these analyzes removes the subjectivity of CWDs decay classes, reducing the number of tests to determine CWDs physical-chemical properties and increasing the studies accuracy focused on the carbon cycle of these materials.

Diameters of phloem sieve elements can predict stem growth rates of woody plants

Understanding forest dynamics is crucial to addressing climate change and reforestation challenges. Plant anatomy can help predict growth rates of woody plants, contributing key information on forest dynamics. Although features of the water-transport system (xylem) have long been used to predict plant growth, the potential contribution of carbon-transporting tissue (phloem) remains virtually unexplored. Here, we use data from 347 woody plant species to investigate whether species-specific stem diameter growth rates can be predicted by the diameter of both the xylem and phloem conducting cells when corrected for phylogenetic relatedness. We found positive correlations between growth rate, phloem sieve element diameter and xylem vessel diameter in liana species sampled in the field. Moreover, we obtained similar results for data extracted from the Xylem Database, an online repository of functional, anatomical and image data for woody plant species. Information from this database confirmed the correlation of sieve element diameter and growth rate across woody plants of various growth forms. Furthermore, we used data subsets to explore potential influences of biomes, growth forms and botanical family association. Subsequently, we combined anatomical and geoclimatic data to train an artificial neural network to predict growth rates. Our results demonstrate that sugar transport architecture is associated with growth rate to a similar degree as water-transport architecture. Furthermore, our results illustrate the potential value of artificial neural networks for modeling plant growth under future climatic scenarios.

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives.

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

Improving Yield Projections from Early Ages in Eucalypt Plantations with the Clutter Model and Artificial Neural Networks

A common issue in forest management is related to yield projection for stands at young ages. This study aimed to evaluate the Clutter model and artificial neural networks for projecting eucalypt stands production from early ages, using different data arrangements. In order to do this, the changes in the number of measurement intervals used as input in the Clutter model and artificial neural networks (ANNs) are tested. The Clutter model was fitted considering two sets of data: usual, with inventory measurements (I) paired at intervals each year (I1–I2, I2–I3, …, In–In+1); and modified, with measurements paired at all possible age intervals (I1–I2, I1–I3, …, I2–I3, I2–I4, …, In–In+1). The ANN was trained with the modified dataset plus soil type and geographic coordinates as input variables. The yield projections were made up to the final ages of 6 and 7 years from all possible initial ages (2, 3, 4, 5, or 6 years). The methods are evaluated using the relative error (RE%), bias, correlation coefficient (ryŷ), and relative root mean square error (RMSE%). The ANN was accurate in all cases, with RMSE% from 8.07 to 14.29%, while the Clutter model with the modified dataset had values from 7.95 to 23.61%. Furthermore, with ANN, the errors were evenly distributed over the initial projection ages. This study found that ANN had the best performance for stand volume projection surpassing the Clutter model regardless of the initial or final age of projection.

Allocation of water reservoirs to fight forest fires according to the risk of occurrence

Although forest fires are indispensable for some ecosystems, they can have profound economic, environmental, and social implications, especially when they reach high intensities. There are two crucial factors in fighting forest fires: the availability of water resources and the service network. The objective of this study was to propose an alternative methodology for allocating water reservoirs to fight forest fires. The research was divided into three stages: zoning of fire risk, delimitation of viable areas for the implementation of water reservoirs, and determining strategic locations for reservoir allocation. The variables analyzed were land use and occupation, provision of watercourses, relief orientation, slope, proximity to roads, temperature, and precipitation. Fuzzy logic, Euclidean distance, and network analysis were used as the modeling techniques. Scenarios with all risk classes and only the high- and very high-risk classes were analyzed. A total of 66% of the area was represented by the low- and moderate-risk fire classes and 53.16% had a low potential for reservoir allocation, influenced by the low availability of water resources in the area. The proposed model efficiently allocated the water collection points in the different scenarios, and allowed the determination of the areas most susceptible to the occurrence of forest fires and the optimal locations for the installation of reservoirs, with the allocation of 21 water reservoirs to attend the areas of high- and very high-risk of occurrence of fires at a safe speed (40 km h^-1) and 47 reservoirs to meet all risk classes at the same speed. The proposed methodology is feasible, applicable, and adjustable and can be implemented in other conservation units and areas of economic interest.

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.

An ecological scenario prediction model for newly created wetlands caused by coal mine subsidence in the Yanzhou, China

The ecological model we developed can simulate the state of wetlands and predict ecosystem development by varying both parameter settings and forcing functions. The newly created wetland resulting from large-scale coal mining is a distinct type of wetland, but existing ecological models for this wetland type are limited in number and scope. The Yanzhou coalfield, located in Shandong Province in China, contains a typical newly created wetland that came into being after coal mining subsidence. We developed an ecological model for this wetland that estimates values for four state variables: phytoplankton biomass (A), zooplankton biomass (Z), sediment biomass (D), and hydrophyte biomass (H). Analysis of the results showed that the model was sensitive to changes in nutrient loading. As nutrient loads increased, plankton biomass increased, and the ratio of zooplankton biomass to phytoplankton biomass (Z/A) decreased. We defined three prediction scenarios for the wetland and calculated their eco-exergies to compare the ecological effects for each scenario. The most effective measures to improve the state of the ecosystem are to reduce the subsidence depth and to decrease nutrient loading.

Tackling environmental challenges in pollution controls using artificial intelligence: A review

This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

Necromass Carbon Stock in a Secondary Atlantic Forest Fragment in Brazil

Necromass has a relevant role to play in the carbon stock of forest ecosystems, especially with the increase of tree mortality due to climate change. Despite this importance, its quantification is often neglected in tropical forests. The objective of this study was to quantify the carbon storage in a secondary Atlantic Forest fragment in Viçosa, Minas Gerais, Brazil. Coarse Woody Debris (CWD), standing dead trees (snags), and litter were quantified in twenty 10 m x 50 m plots randomly positioned throughout the forest area (simple random sampling). Data were collected during 2015, from July to December. The CWD and snags volumes were determined by the Smalian method and by allometric equations, respectively. The necromass of these components was estimated by multiplying the volume by the apparent density at each decomposition classes. The litter necromass was estimated by the proportionality method and the average of the extrapolated estimates per hectare. The carbon stock of the three components was quantified by multiplying the necromass and the carbon wood content. The total volume of dead wood, including CWD and snag, was 23.6 ± 0.9 m3 ha−1, being produced mainly by the competition for resources, senescence, and anthropic and climatic disturbances. The total necromass was 16.3 ± 0.4 Mg ha−1. The total carbon stock in necromass was 7.3 ± 0.2 MgC ha−1. The CWD, snag and litter stocked 3.0 ± 0.1, 1.8 ± 0.1, and 2.5 ± 0.1 MgC ha−1, respectively. These results demonstrate that although necromass has a lower carbon stock compared to biomass, neglecting its quantification may lead to underestimation of the carbon balance of forest ecosystems and their potential to mitigate climate change.

Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).