Publications

This qualitative study aimed to map what information is used in the forest planning process at large forest-owning companies, how it is used, its level of uncertainty and currently employed strategies to handle forest information uncertainty. An additional aim was to assess the status of the paradigm of the forest planning hierarchy in forestry. We used data from semi-structured interviews with representatives of six large forest-owning companies in Sweden, representing 30 per cent of the productive forest land in the country. Our results show that the forest planning process is a hierarchical system of decisions where the information used in the different planning stages is of varying quality and that the traditional hierarchical planning paradigm still plays a vital role in forestry. The most central source of information in the whole forest-planning process is the forest stand database (forest inventory). This includes uncertain information from various sources, including subjective field measurements and aerial image interpretation. However, the use of remote sensing estimates to feed the databases is increasing, which will probably improve the overall quality. Another important finding is that forest companies tend not to use decision support systems or optimization models to solve planning problems outside the scope of strategic planning; thus, most planning is done manually, e.g. in a geographic information system (GIS) environment. Apart from the hierarchical division of the planning process itself, we identified six main strategies that the companies use to control information uncertainty, namely locking the future by making a decision, utilizing a surplus of available harvests, updating information before a decision is made, replanning when the plan is found to be infeasible, planning by looking back and ignoring the uncertainty, either intentionally or unintentionally. The results from this study increase our understanding of contemporary forest-planning practices and will be helpful in the development of decision support systems and methods for information collection. © 2023 Oxford University Press. All rights reserved.

Forest fires are one of the most important factors for forest ecosystem and cause ecosystem destruction such as decreasing forest area, biodiversity in the Mediterranean region. Türkiye located in Mediterranean region is exposed to hundreds of fires every year, which damage forest. Mapping forest fire risk and danger constitutes an important basis for preventing fire damages. Geographical Information System is used for mapping forest fire risk and making the accurate and fast decision. This study is designed to develop a GIS-based decision support systems (DSS) to produce a forest fire risk and danger map for Türkiye. DSS uses topography, stand structure and anthropogenic factors for mapping forest fire risk and danger. DSS was developed using the C-sharp (C#) programming language with the help of Add-in in the ArcGIS. DSS has been successfully tested on case study sites in Kozan and Milas Forest Enterprises in Türkiye. In conclusion, the DSS has contributed to the forest managers to fight forest fire effectively. This study will make an important contribution to both the General Directorate of Forestry, which is in the position of implementing it, and the scientific community. © 2022 Royal Scottish Geographical Society.

Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems. © 2023 IEEE.

Key components of a digital forestry program (digital tools, databases, and decision-support systems) can be of great importance in the management of forests today. A general lack of knowledge of the needs of forest managers and landowners for components of digital forestry has been hypothesized. A survey regarding digital forestry tools was conducted of registered foresters from five USA states (Alabama, Michigan, Mississippi, New Jersey, and North Carolina). Registered foresters may be private landowners or those working for organizations that meet the requirements for licensure in a state. Of the registered foresters who identified as nonindustrial private landowners, roughly half currently do not use digital technologies for forest management efforts. Of the registered foresters who identified as employees of forestry organizations, about 80% use digital technology in the management of their organization’s forests. Geographic information systems technology was the most important, followed by inventory systems, databases, and field-ready smartphone applications. Those registered foresters who had not used digital technologies in the past suggested that their usefulness for the management of their property and their lack of awareness of potential options were likely reasons for not using digital technologies. Study Implications: From surveying five eastern states in the United States, we found that forest landowners are not current users of technology, whereas those associated with forest organizations are more apt to use technologies when managing their forests. Desirable technologies used by both private landowners and forestry organizations include geographic information systems and smartphone applications, whereas those working for forestry organizations also desired inventory systems and databases. Current technology gaps were also identified. © The Author(s) 2022.

The application of biomass models for quantifying forests’ above-ground biomass is essential for sustainable forest management. However, lack of knowledge in modelig biomass of individual tree growth hinders the sustainable management of Dry Afromontane forests. In this study, models to estimate above-ground biomass were developed for Rhus ruspolii, Ekebergia capensis, and Nuxia congesta. To develop the models, a total of 45 trees from different diameter classes were selected, felled, and divided into different biomass compartments. For the model’s development, diameter at breast height (DBH), total height (TH), diameter at stump height (DSH), and wood density (WD) were used as independent variables. Models’ performances were evaluated using RSE, adjusted coefficient of determination, and AIC. Also, model validations were done by using rRMSE, mean absolute deviation, bias, and coefficient of variation. Models 5 (Adj-R2 = 0.92), 6 (Adj-R2 = 0.97), and 8 (Adj-R22 = 0.82) were the best fitted models for Nuxia congesta, Ekebergia capensis, and Rhus ruspolii, respectively. The average wood densities of Ekebergia capensis, Nuxia congesta, and Rhus ruspolii were 0.59, 0.50, and 0.69, respectively. The variation between observed biomass and estimated biomass using new models was statistically not significant ( p > 0.05 ). Thus, the biomass models developed here can be important tools to accurately estimate above-ground biomass in the Menagesha Suba forest and can be integrated into decision support tools. Copyright © 2023 Tamiru Lemi et al.

As disturbances continue to increase in magnitude and severity under climate change, there is an urgency to develop climate-informed management solutions to increase resilience and help sustain the supply of ecosystem services over the long term. Towards this goal, we used climate analog modeling combined with logic-based conditions assessments to quantify the future resource stability (FRS) under mid-century climate. Analog models were developed for nine climate projections for 1 km cells across California. For each model, resource conditions were assessed at each focal cell in comparison to the top 100 climate analog locations using fuzzy logic. Model outputs provided a measure of support for the proposition that a given resource would be stable under future climate change. Raster outputs for six ecosystem resources exhibited a high degree of spatial variability in FRS that was largely driven by biophysical gradients across the State, and cross-correlation among resources suggested similarities in resource responses to climate change. Overall, about one-third of the State exhibited low stability indicating a lack of resilience and potential for resource losses over time. Areas most vulnerable to climate change occurred at lower elevations and/or in warmer winter and summer environments, whereas high stability occurred at higher elevation, or at mid-elevations with warmer summers and cooler winters. The modeling approach offered a replicable methodology to assess future resource stability across large regions and for multiple, diverse resources. Model outputs can be readily integrated into decision support systems to guide strategic management investments. Copyright © 2024 Povak and Manley.

Key components of a digital forestry program (digital tools, databases, and decision-support systems) can be of great importance in the management of forests today. A general lack of knowledge of the needs of forest managers and landowners for components of digital forestry has been hypothesized. A survey regarding digital forestry tools was conducted of registered foresters from five USA states (Alabama, Michigan, Mississippi, New Jersey, and North Carolina). Registered foresters may be private landowners or those working for organizations that meet the requirements for licensure in a state. Of the registered foresters who identified as nonindustrial private landowners, roughly half currently do not use digital technologies for forest management efforts. Of the registered foresters who identified as employees of forestry organizations, about 80% use digital technology in the management of their organization’s forests. Geographic information systems technology was the most important, followed by inventory systems, databases, and field-ready smartphone applications. Those registered foresters who had not used digital technologies in the past suggested that their usefulness for the management of their property and their lack of awareness of potential options were likely reasons for not using digital technologies. Study Implications: From surveying five eastern states in the United States, we found that forest landowners are not current users of technology, whereas those associated with forest organizations are more apt to use technologies when managing their forests. Desirable technologies used by both private landowners and forestry organizations include geographic information systems and smartphone applications, whereas those working for forestry organizations also desired inventory systems and databases. Current technology gaps were also identified. © The Author(s) 2022.

The application of biomass models for quantifying forests’ above-ground biomass is essential for sustainable forest management. However, lack of knowledge in modelig biomass of individual tree growth hinders the sustainable management of Dry Afromontane forests. In this study, models to estimate above-ground biomass were developed for Rhus ruspolii, Ekebergia capensis, and Nuxia congesta. To develop the models, a total of 45 trees from different diameter classes were selected, felled, and divided into different biomass compartments. For the model’s development, diameter at breast height (DBH), total height (TH), diameter at stump height (DSH), and wood density (WD) were used as independent variables. Models’ performances were evaluated using RSE, adjusted coefficient of determination, and AIC. Also, model validations were done by using rRMSE, mean absolute deviation, bias, and coefficient of variation. Models 5 (Adj-R2 = 0.92), 6 (Adj-R2 = 0.97), and 8 (Adj-R22 = 0.82) were the best fitted models for Nuxia congesta, Ekebergia capensis, and Rhus ruspolii, respectively. The average wood densities of Ekebergia capensis, Nuxia congesta, and Rhus ruspolii were 0.59, 0.50, and 0.69, respectively. The variation between observed biomass and estimated biomass using new models was statistically not significant ( p > 0.05 ). Thus, the biomass models developed here can be important tools to accurately estimate above-ground biomass in the Menagesha Suba forest and can be integrated into decision support tools. Copyright © 2023 Tamiru Lemi et al.

As disturbances continue to increase in magnitude and severity under climate change, there is an urgency to develop climate-informed management solutions to increase resilience and help sustain the supply of ecosystem services over the long term. Towards this goal, we used climate analog modeling combined with logic-based conditions assessments to quantify the future resource stability (FRS) under mid-century climate. Analog models were developed for nine climate projections for 1 km cells across California. For each model, resource conditions were assessed at each focal cell in comparison to the top 100 climate analog locations using fuzzy logic. Model outputs provided a measure of support for the proposition that a given resource would be stable under future climate change. Raster outputs for six ecosystem resources exhibited a high degree of spatial variability in FRS that was largely driven by biophysical gradients across the State, and cross-correlation among resources suggested similarities in resource responses to climate change. Overall, about one-third of the State exhibited low stability indicating a lack of resilience and potential for resource losses over time. Areas most vulnerable to climate change occurred at lower elevations and/or in warmer winter and summer environments, whereas high stability occurred at higher elevation, or at mid-elevations with warmer summers and cooler winters. The modeling approach offered a replicable methodology to assess future resource stability across large regions and for multiple, diverse resources. Model outputs can be readily integrated into decision support systems to guide strategic management investments. Copyright © 2024 Povak and Manley.

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries. © 2022 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.