Mapping urban tree coverage using object-oriented image analysis method: A case study

In the paper, we adopted an object-oriented image analysis method to detect and classify road vehicles from airborne color digital orthoimagery at a ground pixel resolution of 20cm. Firstly; a vector-generated road mask was used to constrain detection and classification of vehicles to road region. Secondly, image segmentation and edge detection algorithms were performed to separate vehicles from the background in the road region. Then, a fuzzy logic classifier was constructed to classify the extracted object regions into the vehicle and the non-vehicle regions by using the feature information of image objects. Finally, based on the calculated average length and width of vehicles, we classified vehicles into three categories, that is, small, medium and big. And the counts of the three vehicle classes were derived. The automatic counts match manual counts very well.

Declining urban and community tree cover in the United States

The increase of impervious cover and decrease of tree cover within urban areas globally (2012–2017)

A large number of trees have been planted in built-up areas to improve the urban environment, but the effects of tree cover on spontaneous understory herbs are not yet well understood. This study surveyed spontaneous herbs in two kinds of habitats (habitats with and without tree cover) in the built-up area of the small city Junlian in Sichuan Province, China. A total of 222 species of spontaneous herbaceous plants in 180 genera of 71 families were recorded, including a vulnerable species and six species endemic to China. Although the overall species richness values were similar in the two kinds of habitat, the average species richness per quadrat of all plants, perennials, plants with the dwarf growth form, and animal-dispersed plants was significantly higher in the habitats with tree cover than in those without tree cover. The overall species association was significantly positive in the habitats with tree cover (VR = 1.51, p < 0.05) and neural (VR = 0.86) in the habitats without tree cover. Among the top 25 frequently recorded species in each kind of habitat, the species association of plants with the same trait combination type differed greatly in the two kinds of habitats. For the species association between annuals, only 13.33% of species pairs were significantly associated in the habitats with tree cover, while 22.22% of the species pairs were significantly negatively associated in the habitats without tree cover. For the species association between plants with tall growth forms, the proportion of significant positive associations in the habitats with tree cover was approximately twice than in the habitats without tree cover. For the species association between plants with the dwarf growth form, the proportion of negative associations in the habitats without tree cover was approximately twice that in the habitats with tree cover. Species with the same dispersal mode generally had a very low proportion of negative interspecific associations or a high proportion of positive interspecific associations in habitats unfavorable to their establishment. Our findings suggest that tree cover can improve the species richness of the spontaneous herbaceous species beneath them and profoundly influence interspecific coexistence relationships in a built-up area.

Not population density, but city size and per capita income influence the urban forest carbon sequestration: A case of growing cities in Arkansas, USA

Two datasets of percent urban tree canopy cover were compared. The first dataset was based on a 1991 AVHRR forest density map. The second was the US Geological Survey’s National Land Cover Database (NLCD) 2001 sub-pixel tree canopy. A comparison of these two tree canopy layers was conducted in 36 census designated places of western New York State. Reference data generated by photo-interpreting the tree cover on 1994 and 2002 orthoimages were compared on a city-wide basis to estimates of the tree cover derived from the AVHRR and NLCD tree canopy maps. Comparison of the AVHRR-based estimate with the 1994 photo-interpreted reference showed low accuracy and high variability. The comparison between the NLCD-derived estimate and 2002 photo-interpreted values had higher accuracy but shows a consistent under prediction bias for all tree cover values. In comparing the two photo-interpreted reference sets, little actual change was detected for this study region. Assessing change using differences between the imagery-based AVHRR and NLCD urban tree cover datasets is shown to lead to erroneous results.

Habitat corridors that facilitate functional connectivity are a fundamental component of wildlife conservation in fragmented landscapes. However, the landscape matrix separating suitable habitat is not uniformly impermeable to movement and management to increase matrix permeability could be an alternative means to maintain connectivity. Gliding mammals are particularly sensitive to fragmentation because their movements are constrained by glide distance thresholds. Populations of gliders in cities are at risk of being isolated by increasing habitat loss and urban development, yet little is known about how the urban matrix affects glider movement. Here we investigate how the level of urbanization and tree cover in the matrix influence matrix permeability to sugar gliders (Petaurus breviceps) within suburban forest reserves. Twenty-two sugar gliders were radio-tracked over winter and summer at four reserves. Boundary crossing behaviour was measured as the number of times each glider crossed into the matrix, and matrix permeability was determined as the maximum distance travelled by gliders into the matrix. The majority of gliders (81%) were located in the matrix at least once, and rates of boundary crossing were consistent across urbanization and tree cover levels. Matrix permeability was negatively affected by matrix urbanization, but not by matrix tree cover, and no interaction effects were found. Although distances travelled by gliders into the matrix did not exceed 180m, they were comparable with typical movement distances by gliders in reserves. Our results demonstrate that the urban matrix can provide suitable habitat for gliding mammals to move and forage, but that increased urbanization may inhibit glider use of the matrix irrespective of tree cover. This finding has implications for conservation planning and suggests that structurally connected areas may not be used if movement behaviour is inhibited. Conversely, management of matrix permeability could be used to maintain connectivity without needing to construct physical corridors. © 2012 Ecological Society of Australia.

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.

Distribution of urban tree canopies is generally not uniform. Multiple variables have been shown to be associated with tree canopy cover, including violence, health, and general well-being. Herein we examine the relationship of tree cover with intentional deaths. Suicide, homicide, and tree cover data were examined by ZIP code for Louisville, a mid-sized city in Kentucky. Relationship between intentional death (suicide and homicide) and tree cover was examined with Poisson regression analysis. In both univariate and multivariate models, suicides (P < 0.0001 and P = 0.0005), homicides (P < 0.0001 and P = 0.03), and combination (P = 0.0541) were negatively associated with tree cover. In this exploratory study we have found that sparse canopy cover is associated with higher rates of intentional human death (both suicides and homicides). Given that suicides and homicides are relatively rare occurrences, these data suggest that larger samples be examined to confirm the relationship between intentional death and canopy cover.

Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as the feature of the image object at different bands. G statistic was employed to measure the distance between the object histograms at two times. Change vector of the object was built by the histogram distance at different bands. The heterogeneity of the object was measured by the magnitude of the object change vector. The threshold to detect change/no change objects was decided by Otsu method which maximizes the variance between change objects and unchanged objects. The experimental results on QuickBird images show that OCVA can make full use of the spatial information in HR images and get higher accuracy than pixel-based CVA.

Road blockage information extraction from a single-phase postdisaster image is difficult because roads are narrow and easily covered by vegetation. The traditional object-oriented image analysis method is restrictive, and its detection is slow. A deep learning algorithm, i.e., the convolution neural network (CNN), is applied to rapidly extract road blockage information. An algorithm for sample generation is designed to construct a typical sample library for CNN training, and an appropriate CNN structure and a complete detection process are designed to extract road blockage information. Finally, by taking the Jiuzhaigou earthquake on August 8, 2017, as an example, experimental verification is carried out. The kappa coefficient and the F1 score of the results are 77.60% and 87.95%, respectively. The extraction of road blockages can be completed with an efficiency of 14.59 km2 per hour. The requirements for disaster emergency monitoring can be met by the accuracy and efficiency of this method, which are better than those of the traditional object-oriented method.

Statement of purposeGreen space and vegetation may play a protective role for urban violence. We investigated whether being near urban tree cover during outdoor activities related to being assaulted with...

Non-linear effects of meteorological variables on cooling efficiency of African urban trees.

Automated extraction of tree and plot-based parameters in citrus orchards from aerial images

We propose a general structure for a classifier that uses fuzzy inference techniques. This structure leads to a variety of fuzzy logic classifiers that are capable of combining numerical data and linguistic knowledge in a unified framework. Under certain conditions the fuzzy logic classifier reduces to the Bayes minimum error classifier. Our examples show that, when linguistic information is available, the fuzzy classifiers can perform better than probabilistic classifiers that do not use the linguistic information.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>