ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China

Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.

Hierarchical Bayesian Small Area Estimation Using Weakly Informative Priors in Ecologically Homogeneous Areas of the Interior Western Forests

The U.S. Forest Inventory and Analysis Program (FIA) collects inventory data on and computes estimates for many forest attributes to monitor the status and trends of the nation's forests. Increasingly, FIA needs to produce estimates in small geographic and temporal regions. In this application, we implement area level hierarchical Bayesian (HB) small area estimators of several forest attributes for ecosubsections in the Interior West of the US. We use a remotely-sensed auxiliary variable, percent tree canopy cover, to predict response variables derived from ground-collected data such as basal area, biomass, tree count, and volume. We implement four area level HB estimators that borrow strength across ecological provinces and sections and consider prior information on the between-area variation of the response variables. We compare the performance of these HB estimators to the area level empirical best linear unbiased prediction (EBLUP) estimator and to the industry-standard post-stratified (PS) direct estimator. Results suggest that when borrowing strength to areas which are believed to be homogeneous (such as the ecosection level) and a weakly informative prior distribution is placed on the between-area variation parameter, we can reduce variance substantially compared the analogous EBLUP estimator and the PS estimator. Explorations of bias introduced with the HB estimators through comparison with the PS estimator indicates little to no addition of bias. These results illustrate the applicability and benefit of performing small area estimation of forest attributes in a HB framework, as they allow for more precise inference at the ecosubsection level.

Evaluating urban greening scenarios for urban heat mitigation: a spatially explicit approach

Urban green infrastructure, especially trees, are widely regarded as one of the most effective ways to reduce urban temperatures in heatwaves and alleviate the adverse impacts of extreme heat events on human health and well-being. Nevertheless, urban planners and decision-makers are still lacking methods and tools to spatially evaluate the cooling effects of urban green spaces and exploit them to assess greening strategies at the urban agglomeration scale. This article introduces a novel spatially explicit approach to simulate urban greening scenarios by increasing the tree canopy cover in the existing urban fabric and evaluating their heat mitigation potential. The latter is achieved by applying the InVEST urban cooling model to the synthetic land use/land cover maps generated for the greening scenarios. A case study in the urban agglomeration of Lausanne, Switzerland, illustrates the development of tree canopy scenarios following distinct spatial distribution strategies. The spatial pattern of the tree canopy strongly influences the human exposure to the highest temperatures, and small increases in the abundance of tree canopy cover with the appropriate spatial configuration can have major impacts on human health and well-being. The proposed approach supports urban planning and the design of nature-based solutions to enhance climate resilience.

Drivers of understory plant communities in Sierra Nevada mixed conifer forests with pyrodiversity

Background Fire suppression in western North America increased and homogenized overstory cover in conifer forests, which likely affected understory plant communities. We sought to characterize understory plant communities and their drivers using plot-based observations from two contemporary reference sites in the Sierra Nevada, USA. These sites had long-established natural fire programs, which have resulted in restored natural fire regimes. In this study, we investigated how pyrodiversity—the diversity of fire size, severity, season, and frequency—and other environment factors influenced species composition and cover of forest understory plant communities. Results Understory plant communities were influenced by a combination of environmental, plot-scale recent fire history, and plot-neighborhood pyrodiversity within 50 m. Canopy cover was inversely proportional to understory plant cover, Simpson’s diversity, and evenness. Species richness was strongly influenced by the interaction of plot-based fire experience and plot-neighborhood pyrodiversity within 50 m. Conclusions Pyrodiversity appears to contribute both directly and indirectly to diverse understory plant communities in Sierra Nevada mixed conifer forests. The indirect influence is mediated through variability in tree canopy cover, which is partially related to variation in fire severity, while direct influence is an interaction between local and neighborhood fire activity.

Blue-Green infrastructure determines the microclimate mitigation potential targeted for urban cooling

Urban Heat Island (UHI) exacerbated by global warming can increase the thermal load in cities, which leads to more extreme climate events. One of the strategies to mitigate the impact of extreme climates and UHI is through nature-based solutions such as the Blue-Green Infrastructure as it provides environmental and community benefits However, Blue-Green Infrastructure’s role in urban cooling in the tropics still needs to be further investigated. Therefore, this study examined the role of Blue-Green Infrastructure on microclimate modifications in an urban park. Microclimate measurements were made using systematic random sampling with random start (total of 64 sampling points) at a waterbody (Blue Infrastructure) and tree and grass (Green Infrastructure) areas during solar noon time (1200-1400). Blue-Green Infrastructure showed greater microclimate benefits compared to the open space with the reduction of air temperature by up 1.6°C. However, green infrastructure had greater cooling benefits compared to Blue Infrastructure especially trees with significantly lower air temperature and solar radiation interception (0.71°C and 250.3 W/m2, respectively) as well as higher relative humidity (12.17%). Moreover, stand characteristics determine the microclimate mitigation function. This study provides a useful indication of the role of blue and green spaces in urban cooling, where it further emphasizes the importance of Blue-Green Infrastructure utilization in urban landscapes. It further recommends that urban planners, managers and policymakers should consider these strategies for urban cooling purposes : 1) Utilising Blue and Green Infrastructures especially trees 2) Tree canopy cover and DBH should be set as priority traits.

Ecological characteristics of mangroves and coral reefs at Central Weda Sub-District, North Maluku

Increase of human activities on coastal area in Central Weda Sub-District has caused degradation of coastal ecosystems. Even though coastal ecosystems have an essential role in marine ecology, such as coral reefs and mangrove forests, they are sometimes undermined by economic or commercial interests. This study aimed to determine density and cover percentage of mangrove forests and coral reefs in Central Weda Sub-district, North Maluku. Data collection on coral reef ecosystem components was carried out by applying georeferenced photo-transect and line transect quadrat techniques. While for mangroves, data collection included species diversity and tree/canopy cover percentage. Study result showed that based on lifeform percentage of coral reefs, most of the study areas were in a good category with 23.95% coverage, moderate condition 39,5%, and poor condition 36.6%. The coral reefs in good and moderate conditions were found at location having relatively low human activities. Whereas percentage of mangrove cover from all stations ranged from 78,39 to 78,76% with Important Value Index ranging from 106,79% to 158,32%.

Environmental service assessment of trees at Yogyakarta International Airport open space using CITYGreen 5.2

Urban development has many negative impacts in the form of an increase in the concentration of CO2 in the air. One of them is due to the increase in the volume of vehicles. CO2 causes an increase in global temperature (global warming) through the greenhouse effect. Yogyakarta International Airport has a green open space planted with trees to support the existence of the airport itself. Trees have contributed to efforts to reduce CO2 in the air through CO2 absorption. Control of the amount of CO2 emissions in the air is done by adding carbon stocks on land so that the CO2 concentration does not continue to increase, therefore this research is necessary. The purpose of this study is to analyze the ability of trees to absorb pollutants and store carbon and estimate the value of environmental services that will be contributed by the tree green system at Yogyakarta International Airport based on existing planting plans. The research method used is modeling using CITYGreen 5.2 software to determine the value of tree services in the ability to absorb air pollution, carbon storage capacity, and cost savings that can be done by tree canopies. The method used in this study consisted of preparation, data inventory, and data analysis. The results of this study show that the value of environmental services provided by 125,72 acres of tree canopy cover in absorbing air pollution in the airport area is $ 9.045,54 / year or equivalent to Rp 133.356.587,10 per year, the total concentration of pollutants that can be absorbed is 3.813,30 lbs/year or equivalent to 1.729,68 kg/year, and the capacity of carbon storage is 1.326,46 tons with a sequestration rate of around 29,9 tons/year.

Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Land managers require tools that improve understanding of suitable habitat for invasive plants and be incorporated into survey efforts to improve efficiency. Habitat suitability models contain attributes that can meet these requirements, but it is not known how well they perform as they are rarely field tested for accuracy. We developed ensemble habitat suitability models in the state of Wisconsin for 15 species using five algorithms (boosted regression trees, generalized linear models, multivariate regression splines, MaxEnt, and random forests), evaluated performance, determined variables that drive suitability, and tested accuracy. All models had good model performance during the development phase (AUC>0.7 and TSS>0.4). While variable importance and directionality was species specific, the most important predictor variables across all of the species’ models were mean winter minimum temperatures, total summer precipitation and tree canopy cover. Post model development we obtained 5,005 new occurrence records from community science observations for all 15 focal species to test the models’ abilities to accurately predict results. Using a correct classification rate of 80%, just 8 of the 15 species correctly predicted suitable habitat (α≤0.05). Exploratory analyses found the number of reporters of these new data and the total number of new occurrences reported per species contributed to increasing correct classification. Results suggest that while some models perform well on evaluation metrics, relying on these metrics alone is not sufficient and can lead to errors when utilized for surveying. We recommend any model should be tested for accuracy in the field prior to use to avoid this potential issue.