Forest Vegetation Cover Research Articles

Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest

Understanding the dynamic relationships between geoenvironmental factors and forest vegetation cover is crucial for effective forest management and planning. This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. The results show that in 2013, altitude and rainfall were the primary factors influencing FVC, with areas of higher altitudes and adequate rainfall exhibiting up to 30% denser forest cover. By 2023, soil moisture and groundwater levels had emerged as the dominant factors, with soil moisture levels accounting for 25% of the variation in FVC. This shift underscores the increasing importance of water management strategies to maintain forest health. The Random Forest model demonstrated high predictive accuracy, achieving an R2 value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. This study’s insights are crucial for guiding sustainable forest management practices and assisting decision-makers in formulating strategies for resource management, environmental preservation, and future planning. This study underscores the necessity of adaptive management strategies that consider evolving climatic and hydrological conditions, emphasizing continuous monitoring and advanced technologies to ensure the resilience of forest ecosystems.

Multi-temporal analysis of forest canopy cover in Ngel Nyaki Forest Reserve using the Sentinel-1 and Sentinel-2 data

ABSTRACT This research used the Sentinel-1 and Sentinel-2 data to map forest canopy cover. The main aim of this study is to assess the capability and value of SAR and Optical image data for mapping, estimating, and monitoring forest canopy cover and its parameters in the Ngel Nyaki Forest Reserve in the Mambilla Plateau, Taraba State, Nigeria. The spatial distribution pattern of forest vegetation cover between 2015 and 2021 was analyzed using a random forest model with Sentinel-1 and maximum likelihood for Sentinel-2 data and the relationship between the biophysical parameter Leaf-Area-Index (LAI) and the spectral indices. The results show changes in canopy cover derived from Sentinel-1 and Sentinel-2 with varying canopy cover frequencies, indicating a decrease from 34% to 27% and 32% to 25% from 2015 to 2021, respectively. The basal area per acre (BA/ac) ranges from 30 to 170 square feet/acre, with a mean of 62.82 square feet/acre and a standard deviation of 37.77 square feet/acre. Moreover, the analysis of biophysical parameters shows that the LAI for the entire forest area was 3.88 in 2015, indicating a relatively dense canopy with substantial leaf cover, while the LAI value of 3.82 in 2021 suggests a slight decrease in leaf area density.

Can beavers canopy alterations affect managed forests more than natural forests?

Beavers, as ecosystem engineers, significantly affect riparian ecosystems, mostly through foraging and dams construction. This study examines how beaver-related tree cutting altered canopy openness and affected microhabitats and forest floor vegetation in the Northern Poland’s Tuchola Forest. Woody plant characteristics, forest floor vegetation abundance, and canopy openness were assessed at three sites varying in forest naturalness, with canopy openness being measured using hemispherical photography. Results show that selective tree removal by beavers from all diameter classes significantly increased canopy openness. This alteration leads to transformative changes in forest habitat properties, notably increasing light availability, and to nutrient status changes as evidenced by shifts in Ellenberg indicator values. Consequently, these changes result in increased forest floor total vegetation cover, diversity and composition. The cascading effects of beaver tree-cutting on canopy alterations have been comprehensively modelled using structural equation models. The study also reveals distinct spatial patterns in canopy alterations, with the most pronounced effects near riverbanks. The intensified impact of beaver activities, could be linked to the naturalness of the forest, and might be especially pronounced in more altered environments, particularly where pioneer aspen trees dominate the tree stand composition. These findings underscore the role of beavers in shaping forest dynamics, particularly in managed or disturbed forests. By creating canopy gaps, beavers initiate processes that enhance habitat heterogeneity and biodiversity, suggesting that their presence may be crucial for the restoration of natural processes in disturbed ecosystems.

Integration of UAV LiDAR and WorldView-2 images for modeling mangrove aboveground biomass with GA-ANN wrapper

BackgroundIntegrating optical and LiDAR data is crucial for accurately predicting aboveground biomass (AGB) due to their complementarily essential characteristics. It can be anticipated that this integration approach needs to deal with an expanded set of variables and scale-related challenges. To achieve satisfactory accuracy in real-world applications, further exploration is needed to optimize AGB models by selecting appropriate scales and variables.MethodsThis study examined the impact of LiDAR point cloud-derived metrics on estimation accuracies at different scales, ranging from 2 to 16 m cell sizes. We integrated WorldView-2 imagery with LiDAR data to construct biomass models and developed a genetic algorithm-based wrapper for variable selection and parameter tuning in artificial neural networks (GA-ANN wrapper).ResultsOur findings indicated that the highest accuracies in estimating AGB were yielded by 4 m and 6 m cell sizes, followed by 8 m and 10 m, associated with the dimensions of vegetation canopies and sampling plots. Models integrating WorldView-2 and LiDAR data outperformed those using each data source individually, reducing RMSEr by 5.80% and 3.89%, respectively. Combining these data sources can capture the canopy spectral responses and vertical vegetation structure. The GA-ANN wrapper model decreased RMSEr by 1.69% over the ANN model and dwindled the number of variables from 38 to 9. The selected variables included vegetation density, height, species, and vegetation indices.ConclusionsThe appropriate cell size for AGB estimation should consider the sizes of vegetation canopies, tree densities, and sampling plots. The GA-ANN wrapper effectively reduced variables and achieved the highest accuracy. Additionally, canopy spectral and vertical structure information are vital for accurate AGB estimation. Our study offered insights into optimizing mangrove AGB models by integrating optical and LiDAR data. The approach, data, model, and indices employed in this research can effectively predict AGB estimates of any other forest types or vegetation cover types in different climate regions.

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Assessment of spatio-temporal dynamics of mangrove forest in Teluk Pangpang, Banyuwangi, East Java, Indonesia

Abstract. Hidayah Z, Rachman HA, Wiyanto DB. 2024. Assessment of spatio-temporal dynamics of mangrove forest in Teluk Pangpang, Banyuwangi, East Java, Indonesia. Biodiversitas 25: 3138-3150. Mangrove forest in Teluk Pangpang, Banyuwangi District, East Java, Indonesia is located in a semi-enclosed bay area on the east coast of Java Island and bordering with Bali Strait. Since 2020, this location has been designated as an Essential Ecosystem Area (EEA) by the Indonesia Ministry of Environment and Forestry. Regardless of its designation as an EEA, information on vegetation stucture and spatio-temporal changes of Teluk Pangpang’s mangrove forest is limited. The availability of multi-temporal medium to high-resolution satellite imagery data enables researchers to monitor the condition of mangrove ecosystem using long-term data and reliable spatial continuity. The objective of this study is to investigate vegetation community structure of mangrove forest in Teluk Pangpang, as well as to determine the changes in extent and condition of mangrove forest measured as Mangrove Health Index (MHI) between 2015 and 2023 using remote sensing and GIS analysis. Spatio-temporal dynamics of MHI was determined using Sentinel-2 imagery data from 2015, 2018 and 2023. Meanwhile, a series of extensive survey in 15 transects with a total of 75 plots were conducted to obtain field data. The results showed that Rhizophora apiculata Blume and R. mucronata Lam. were the most important species with Important Value Index (IVI) of 140.91 and 148.24, respectively. Meanwhile, Sentinel-2 image processing results revealed a major increase in mangrove’s area, from 643.35 ha in 2015 to 737.51 ha in 2023. Furthermore, MHI analysis indicated improving conditions of mangrove ecosystem, as evidenced by the significant increased of mangroves area at the excellent MHI category (MHI>66.8) from 11.00 ha in 2015 to 175.86 ha in 2023. Based on Principal Component Analysis (PCA) several environmental parameters were strongly correlated to MHI including salinity, DO, TSS and mangrove’s diversity index. The findings of this study, primarily the application of MHI approach, may provide an alternative strategy for monitoring the dynamics of vegetation cover of mangrove forest in Teluk Pangpang and other mangrove areas in Indonesia.

Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors

Deep learning approaches for estimating forest vegetation cover and exploring influential ecosystem factors

SPATIAL TEMPORAL MAPPING OF VEGETATION COVER INDICES USING SENTINEL-2 MULTISPECTRAL INSTRUMENT IN UNAAHA CITY

Vegetation cover in urban areas contributes to providing livable ecosystem services for humans. As urbanization continues to expand, vegetation cover in urban areas will change rapidly. This research aims to monitor changes in vegetation cover in the last 5 years in Unaaha City using Google Earth Engine. This study also explores vegetation index algorithms such as NDVI, EVI, SAVI, and MSARVI. The research results show that forest vegetation cover continues to decline, followed by an increase in built-up land with an average change of 1,021 ha or the equivalent of 55% of the total area. This research also found that the NDVI algorithm had the best average accuracy with a value of OA=82.54%, followed by MSARVI=73.23%, SAVI=69.52%, and EVI=63.62%. This makes the NDVI method have good accuracy requirements for identifying vegetation compared to other vegetation index algorithms.

Historical charcoal production as a factor in soil cover heterogeneity in a fluvioglacial landscape − A case study from northern Poland

Although in situ wood thermal processing, as a common practice in European forests in historical times, has been confirmed in many studies, the environmental implications of that process remain poorly explored. In this study, we focused on evaluating the impact of that process on forest vegetation and soil cover heterogeneity in a fluvioglacial landscape dominated by Brunic Arenosols. This study covered three 70–95-year-old stands of Scots pine in northern Poland. Three soil profiles, including from the relict charcoal hearth (RCH), from the surrounding ditch and a control, were examined from each stand, as well as associated vegetation. The studies showed that 19th century charcoal production platforms were overgrown with 20–60% fewer plant species than control stands. The effect on soils varied according to their characteristics. The presence of anthropogenic horizons consisting of a mixture of burnt soil material, ashes, charcoal particles, and other products of wood pyrolysis was the most distinguishing feature of RCH soils classified as Spolic Technosols (Arenic, Dystric, Pyric, Protospodic, Transportic). Typically, these horizons were characterised by lower bulk density, and higher porosity, as well as higher contents of total organic carbon, nitrogen, and exchange cations than the control soils. The differences between the RCH and control soils were highlighted by a comparison between the pools of the components in a soil column (pedon). Clear tendencies were not recorded for pH, the phosphorus content and stocks, the forms of iron and aluminium, the saturation of the soil sorption complex with bases or the trophic index. This may be due to the long-term neutralising effect of forest vegetation, dominated by Scots pine. Moreover, the results of our study may indicate the problem of toxicity of the RCH soils, which should be undertaken, considering the utilitarian importance of the issue.

Economic Analysis of Forest Management for Timber Production and Agricultural Production in Hillside Watershed Areas of Madagascar: A Case Study of the Itasy Region

In hillside watersheds, forests play a crucial role in protecting against soil erosion. Not only does forest vegetation cover serve as a biological measure to prevent soil erosion, it also generates income through sustainable management practices. However, there is a scarcity of research that evaluates and compares the profitability of perennial woody crops with that of other agricultural crops by integrating biological aspects within an economic analytical framework in Central Madagascar. In this study, we address this gap by combining a biological growth model that captures complex forest dynamics with economic management data to utilize the discounted cash flow method. We applied this approach to evaluate and compare the profitability of forest management and agricultural crop production in the hillside watershed areas of Central Madagascar, which have experienced severe human-induced soil erosion. Although our results showed that timber production is a profitable option, other agricultural crops generate much higher profits, indicating that financial subsidies alone may not be enough to shift land use toward perennial woody crops. In addition, growing fruit trees such as oranges and mangoes appears to be a much more financially attractive option for local farmers than annual agricultural crop production.

Profile Development and Soil Properties of Three Forest Reclamations of Different Ages in Sokolov Mining Basin, Czech Republic

Forestry reclamation practices have been very popular in the second half of the last century, and many spoil heaps have been converted into forests since. In our experiment, three forest reclamations of different ages (~90, ~50, and ~30 years) and three soil vegetation covers (I—maple and cherry, II—maple, and III—alder) from Sokolov, Czech Republic, were investigated. In each of the three stands, two soil profiles have been dug, and both disturbed and undisturbed soil samples were taken from all recognized horizons. Samples were tested for bulk and particle density, porosity, water retention capacity, pH (H2O, KCl), cation exchange capacity, oxidizable carbon content, organic matter quality, plant available nutrients, and risk elements. A comparison of these properties throughout the profile, as well as between the stands, was presented. A significant role of stand age in soil profile development and soil quality was observed, as well as the tendency of the anthropogenic mine Technosol to evolve into a forest Cambisol in this climate region and parent material. Influence of forest vegetation cover was observed to ameliorate soil properties by accumulating organic matter, thus reducing compaction and increasing CEC and nutrient availability.

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

ContextWildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.ObjectivesWe demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.MethodsUsing Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.ResultsWe document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.ConclusionWe demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

Forest and Natural Vegetation Cover Loss Over 2000 to 2020 in Sri Lanka; A Canopy Density Base Analysis

As one of the most important biodiversity hotspot, forest and natural vegetation areas play an essential role in the global ecosystem. It provides an uncountable number of ecosystem services while controlling the adverse impacts of climatic changes. With the expansion of civilization and fulfilling the infinite needs of man, forests and natural vegetation areas were destroyed, especially for agricultural purposes, development projects and timber. Due to this, the accelerated growth of deforestation and loss of natural vegetation can be observed in Sri Lanka in the past few decades. This study aimed to identify the forest and natural vegetation area loss from 2000 to 2020 with a particular focus on the Canopy Density (CD) of tree cover (>25%, >50% and >75%) in Sri Lanka. The satellite estimated data (30 mx30 m resolution) was downloaded from the 2000 to 2020 time period from the Global Forest Watch (GFW) online platform conducted by the World Resource Institute were used. Spatial and temporal changing patterns of the forest and natural vegetation areas have been identified using descriptive analysis methods (percentage) using MS Excel 2019 software. Results of the study found that in 2000, 61.38 percent of the country was covered with above 25% tree cover CD, while 53.86 percent and 39.76 percent of areas have covered with the tree cover CD above 50% and 75%, respectively. However, when it has come to 2020, these areas have decreased by 52.78 percent (>25% CD), 44.27 percent (>50% CD) and 28.65 percent (>75% CD). During this period, 8.6 percent (>25% CD), 9.59 percent (>50% CD) and 11.11 percent (>75% CD) of the forest and natural vegetation areas of Sri Lanka have lost the country. Throughout this period, 2016 can be identified as a year at risk for deforestation due to its annual deforestation rate (>8.8 percent). The total area of forest cover lost in 2016 is 17799 ha (>25% CD), 15583 ha (>50% CD) and 9865 ha (>75% CD). This is nearly 13 percent of forest cover loss for all three types of CD tree cover areas. According to the calculations of the forest areas that have >75% of CD, Anuradhapura (12.06%), Kurunegala (9.82%), and Monaragala (8.49%) districts are the areas that contributed to forest cover loss in the country during 2000 to 2020. According to the study, the annual deforestation rate is still very high. Hence should be needed to monitor and take action to stop deforestation and achieve sustainability in natural resource management in Sri Lanka. 
 Keywords: Deforestation, Canopy density, Tree cover, Natural resource management, Sri Lanka

The Application of Remote Sensing Techniques in Ecological Environment Monitoring

As global environmental challenges continue to escalate, remote sensing technology has become an indispensable tool for researching, monitoring, and managing these issues. This article aims to summarize the principles of various remote sensing technologies, their primary application areas, and their crucial role in ecological and environmental conservation for sustainable development. Remote sensing technology offers a diverse array of data from multiple sources and scales, facilitating the monitoring of natural resource changes, studying ecosystem health, and assessing the impact of human activities on the environment. In-depth exploration of the latest advancements and application cases in remote sensing includes: (1) Vegetation: Applications in vegetation monitoring encompass the use of technologies such as unmanned aerial vehicle (UAV) remote sensing and laser radar (LiDAR) for monitoring forest health, vegetation cover, and changes. These applications support forest management and ecological research. (2) Water Bodies: Thermal infrared remote sensing and satellite remote sensing are utilized to monitor water quality, water level fluctuations, and aid in water resource management, addressing water resource challenges. (3) Soil: Remote sensing technology is employed in soil applications, utilizing multispectral and synthetic aperture radar (SAR) techniques for soil property assessment, soil moisture analysis, and soil erosion prediction. These applications contribute to agricultural and land management decisions, and provide suitable remote sensing technologies for the current trends in the ecological field, serving as a reference for monitoring methods.

Forest vegetation cover in Bu Gia Map National Park, Vietnam

Understanding the composition of tree species is essential for establishing conservation priorities. Bu Gia Map National Park is in the tropical rainforest zone of Southeast Vietnam. This paper is to analyse and assess tree species composition and biodiversity conservation in Bu Gia Map National Park. The diversity of plant species in Bu Gia Map National Park was studied to provide baseline information for conservation and sustainable management processes that will prolong the life of National Park. The results obtained from the study conducted on the plant species composition and forest vegetation in this national park revealed a total of 786 species, spanning 430 genera and 132 families, across three divisions of vascular plants: Polypodiophyta, Pinophyta, and Magnoliophyta. Among these are 300 species of timber plants, 270 species of medicinal plants, 62 species of edible plants, 74 species of ornamental plants, 40 species of industrial plants, 28 species of fiber plants, and 12 species of plants with unknown use. Notably, during the investigation period, Goniothalamus vietnamensis Ban and Myxopyrum smilacifolium Blume were newly recorded in the forest vegetation of Bu Gia Map National Park. The study also describes a variety of forest vegetation in the area, identifying two major forest vegetation types within Bu Gia Map National Park.

Wooded biocorridors substantially improve soil properties in low-altitude rural benchlands

This study examines soil properties in 30- and 60-year-old agricultural biocorridors and provides a comparative overview with neighbouring farmland. Both mixed and undisturbed soil samples were collected from six farmland/biocorridor study areas to assess a wide spectrum of physical, hydrophysical, chemical and biological soil properties. Biocorridor soils were characterised by higher water retention capacities, porosity, aeration and soil carbon stock, the latter increasing with depth. On the other hand, biocorridor bulk density under forest vegetation cover was lower, indicating progressive soil restoration. Slightly lower soil reactions in biocorridor soils disproved the hypothesis that nutrient-rich soils under biocorridors would form substrates with a high base cation content, leading to soil acidification. Biological activity, expressed through respiration coefficients, was generally low due to unfavourable physical conditions (clayey or silty-clay substrates), with the lowest levels in biocorridors. Nevertheless, biocorridor soil microbiota displayed more effective utilisation of organic matter as a carbon and nitrogen source, with lighter-textured soils tending to show more effective organic matter utilisation after excluding the influence of land use. Our results confirm biocorridors as an important landscape component, contributing to both soil stability and local revitalisation of soil environments and further emphasising their potential as climate-change mitigation tools in their role as carbon sinks.

Estimation and simulation of carbon sequestration in typical dryland areas of China under future climate change scenarios

Climate anomalies and human disturbances exert complex effects on regional carbon sequestration (CS), causing ecosystem CS to either increase or decrease due to factors such as vegetation greening, climate extremes, land use/land cover (LULC) changes, and farming systems. This study employs the patch-level land use simulation model to forecast changes in various land-use types in the Xinjiang Uyghur Autonomous Region, China, between 2020 and 2060 under different climatic conditions. The Intergovernmental Panel on Climate Change has proposed shared socioeconomic pathways (SSPs) as potential socioeconomic and environmental trajectories that might influence the region’s future land-use patterns. Three salient findings emerged: (1) LULC displayed considerable variation across future climate scenarios, with a notable rise in forest and grassland cover and a marked decrease in cropland areas under the SSP126 scenario. Conversely, the SSP585 scenario witnessed a substantial cropland expansion, paralleled by a decrease in forest and grassland areas; (2) CS exhibited significant disparities across different future climate scenarios. Xinjiang achieved a peak CS of 9.81 Pg between 2020 and 2060 under the SSP126 scenario, marking an increase of 335.22 Tg in 2060 compared to that in 2020. The SSP585 scenario registered the lowest CS at 9.41 Pg, indicating a decline of 50.07 Tg in 2060 relative to 2020; (3) The impacts of longitude and latitude on CS in Xinjiang predominantly arose from shifts in forest area and vegetation cover based on latitude, which positively influenced CS. In contrast, a decrease in vegetation cover and a subsequent decline in CS were evident with increasing longitude, particularly in the Altai, Tianshan, and Kunlun Mountains. These findings hold significant implications for formulating CS management strategies for terrestrial ecosystems and enhancing ecological preservation in Xinjiang.

Impacts of anthropogenic disturbance on forest vegetation cover, health, and diversity within Doma forest reserve, Nigeria.

Forest encroachment is a common practice that has led to the destruction of canopy trees in the Guinea savanna part of Nigeria. This study investigated the influence of human activities on vegetation health and species composition of Doma forest reserve located in Nasarawa State, Nigeria. Landsat satellite data from 1986 to 2021 were utilized to assess forest cover change, land surface temperature (LST), and vegetation indices (VIs). The results show that dense woodland vegetation in the Doma forest reserve depreciated between 1991 and 1999 by 17.82% before increasing by 7.37% between 1999 and 2021. Similarly, vegetation greenness (measured by the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Vegetation Index (GCVI), and leaf area index (LAI)) of the forest mirrored the changes observed in the forest cover. The LST extracted for each year was correlated with all VIs, and an inverse relationship was observed in all relationships analyzed. The decline in greenness between 1999 and 2011 was attributed to increasing lumbering, bush burning, and sand dredging activities. Results also showed the current diversity state (H1 = 0.23), evenness (0.63), and the volume of tree (1.31 m3) species in the heart of the Doma forest reserve. However, a high (25%) native tree species in the Fabaceae family correlated with a dramatic increase in the VIs and an increase in dense woodland cover indicating the importance of Fabaceae in forest ecosystem regeneration.

“It is not Working for Them but with Them”: Adult Educators Roles With Community Groups in Transforming Communities

This paper reflects on Freire’s discourse on liberating education and adult educators’ educational roles with community groups in transforming communities. Mineral-rich communities in Ghana are in danger of losing water bodies, rich arable lands, and forest vegetation cover through “galamsey,” which refers to the gathering of rich mineral resources illegally for sale. Yet the fourth Industrial Revolution Age positions adult educators strategically in making learning the needed skill among community groups to improve communities. This qualitative study adopted a convenience sampling procedure to select and interview eight graduate students in a higher education institution (HEI) in Ghana. Data were analyzed using thematic and narrative approaches. The results indicate that adult educators’ educational roles are empowering, yet they should employ more dialogical, story-telling, and conversational approaches; and must listen to community members’ views on community development programs. The study concludes that adult educators drive community development programs via inclusiveness and networks to make communities sustainable.

FCD-AttResU-Net: An improved forest change detection in Sentinel-2 satellite images using attention residual U-Net

Forest Change Detection (FCD) is a critical component of natural resource monitoring and conservation strategies, enabling informed decision-making. Various methods utilizing the power of artificial intelligence (AI) have been developed for detecting and categorizing changes in forest cover using remote sensing (RS) data. One prominent AI-powered approach is the U-Net, a deep learning (DL) architecture famous for its segmentation proficiency. However, the standard U-Net architecture fails to effectively capture intricate spatial dependencies and long-range contextual information present in remote sensing imagery. To address this research gap, we introduce an attention-residual-based novel DL model which leverages the U-Net architecture and Sentinel-2 satellite images to map alterations in forest vegetation cover in the tropical region. Our novel model enhances the U-Net architecture by seamlessly integrating the strengths of the U-Net, harnessing attention mechanisms strategically to amplify crucial features, and leveraging cutting-edge residual connections to facilitate the smooth flow of information and gradient propagation. These meticulous design choices enabled the precise feature extraction, resulting in improved computational performance of the proposed method compared to the Standard U-Net, Deeplabv3+, Deep Res-U-Net, and Attention U-Net. The classification results demonstrate the enhanced efficiency of our model, achieving a Mean Intersection over Union (MIoU) of 0.9330 on our test dataset. This performance surpasses the Attention U-Net (0.9146), Standard U-Net (0.9029), Deeplabv3+ (0.9247), and Deep Res-U-Net (0.9282). The comparative analysis of ground truth reproductions unveiled the superior detection capabilities of our model in accurately identifying forest and non-forest polygons, surpassing both the standard U-Net, and the U-Net augmented with attention mechanism, along with other state-of-the-art techniques, thereby highlighting its enhanced efficacy. The model’s broad applicability can support forest managers and ecologists in rapidly evaluating the long-term ramifications of infrastructure initiatives, such as roads, on tropical forests, including those in Brunei.