Visualizing certainty of extrapolations from models of land change

This article presents a method to estimate and to visualize the certainty of land change models as they extrapolate beyond the time interval for which empirical data exist. The method to project the certainty relies on measurements of model performance during a validation run with historic data and on the assumption that the model’s accuracy approaches randomness as it predicts farther into the future. A land change model typically predicts each pixel as exactly one category for each year. This article presents a technique to convert those predictions into conditional probabilities. As an example, we use the model Geomod to extrapolate forest change over a century for the Plum Island Ecosystems, which is a Long Term Ecological Research site of the United States’ National Science Foundation. Geomod uses calibration information between 1971 and 1985 in order to predict the changes from 1985 to 1999, at which point the validation procedure measures the model’s predictive accuracy. Then the model is re-calibrated with information from 1985 to 1999 in order to extrapolate into the future, assuming a business as usual scenario. As time progresses, the expected accuracy approaches 0.5, which is the probability at which the model’s prediction is as accurate as a random prediction, since the application involves two categories. The extrapolated accuracy of the prediction for the entire study area in the year 2097 is 68%. The method is designed to work with any number of categories so it can be used with a variety of land change models.     

Urban permeability for birds: An approach combining mobbing-call experiments and circuit theory

The urban matrix was recently shown to be a mosaic of heterogeneous dispersal habitats. We conducted a playback experiment of mobbing calls to examine the probabilities of forest birds to cross a distance of 50 m over urban matrix with different land-cover types in an urban area. We treated the reciprocal of the crossing probabilities as a movement resistance for forest birds. We drew resistance surfaces based on the land-cover maps of urban Sapporo. We applied a circuit theory to examine the relative role of a detour route consisting of a riparian corridor and urban matrix for dispersing forest bird individuals from continuous forest to an isolated green space in the midst of an urban area. Our results showed that wood cover had the highest crossing probability, while open land (grassland and pavement) had the lowest probabilities. Buildings and water surface displayed an intermediate probability. Resistance surfaces and flow maps at 25- and 50-m resolutions were very similar and suggested that dispersing individuals are likely to use the intervening building areas that dominate the urban matrix rather than detour through riparian corridors. Our results showed the useful combination of experimental approaches and circuit theory, and the importance of the spatial configuration of corridors, as well as the composition and management of dispersal habitats, to landscape connectivity.     

The Impact of Misclassification in Land Use Maps in the Prediction of Landscape Dynamics

Land use maps are widely used in modeling land use change, urban sprawl, and for other landscape related studies. A misclassification confusion matrix for land use maps is usually provided as a measure of their quality and uncertainty. However, this very important information is rarely considered in land use map based studies, especially in modeling landscape dynamics. Ignoring uncertainty of land use maps may cause models to provide unreliable predictions. This study is an attempt to investigate the impact of the accuracy of land use maps used as input for an urban sprawl model. In the study area, the regional confusion matrix has been localized using a topographical map. Based on the regional and local confusion matrices, several error levels have been defined. The results showed that a localized confusion matrix that reflected the characteristics of the study area had error rates that were much different than the regional confusion matrix. The predictions of the probability of urban sprawl based on the land use maps and defined error levels were quite different.     

The Relationship between Environmental Amenities and Changing Human Settlement Patterns between 1980 and 2000 in the Midwestern USA

Natural resource amenities may be an attractor as people decide where they will live and invest in property. In the American Midwest these amenities range from lakes to forests to pastoral landscapes, depending on the ecological province. We used simple linear regression models to test the hypotheses that physiographic, land cover (composition and spatial pattern), forest characteristics, land use on undeveloped land, public ownership, soil productivity and proximity to urban centers predict changes in population, housing, and seasonal housing densities over a 10-year interval (1980–1990). We then generated multiple-regression models to predict population, total and seasonal housing density change in the most recent decade (1990–2000) based on ownership and ecological conditions in 1990 and tested them by comparing the predictions to actual change measured by the US Census Bureau. Our results indicate that the independent variables explained between 25 and 40% of the variability in population density change, 42–67% of the variability of total housing density change, and 13–32% of the variability in seasonal housing density change in the 1980s, depending on the province. The strength of the relationships between independent and dependent variables varied by province, and in some cases the sign varied as well. Topographic relief was significantly related to population growth in all provinces, and land cover composition and the presence of water was significantly related to total housing growth in all provinces. There was a surprisingly limited association of any of the independent variables to seasonal housing growth in the northern province, which is commonly perceived to attract seasonal use because of ecological amenities. Proximity to urban centers is related to population and housing density change, but not seasonal housing density change. Our tests indicated that models for population density change showed some utility, but the models for total and seasonal housing density generally performed poorly. Ecologic variables were consistently poor at predicting seasonal housing density change. Our results show that environmental characteristics appear to have some influence on the spatial distribution of population and housing change in the Midwest, although other factors that were not modeled are clearly dominant.     

Modeling the spatially dynamic distribution of humans in the Oregon (USA) Coast Range

A common approach to land use change analyses in multidisciplinary landscape-level studies is to delineate discrete forest and non-forest or urban and non-urban land use categories to serve as inputs into sets of integrated sub-models describing socioeconomic and ecological processes. Such discrete land use categories, however, may be inappropriate when the socioeconomic and ecological processes under study are sensitive to a range of human habitation. In this paper, we characterize the spatial dynamic distribution of humans throughout the forest landscape of western Oregon (USA). We develop an empirical model describing the spatial distribution and rate of change in historic building densities as a function of a gravity index of development pressure, existing building densities, slope, elevation, and existing land use zoning. We use the empirical model to project changes in building densities that are applied to a 1995 base map of building density to describe future spatial distributions of buildings over time. The projected building density maps serve as inputs into a multidisciplinary landscape-level analysis of socioeconomic and ecological processes in Oregon's Coast Range Mountains. This revised version was published online in July 2006 with corrections to the Cover Date.     

Predicting land cover change and avian community responses in rapidly urbanizing environments

We used an integrated modeling approach to simulate future land cover and predict the effects of future urban development and land cover on avian diversity in the Central Puget Sound region of Washington State, USA. We parameterized and applied a land cover change model (LCCM) that used output from a microsimulation model of urban development, UrbanSim, and biophysical site and landscape characteristics to simulate land cover 28 years into the future. We used 1991, 1995, and 1999 Landsat TM-derived land cover data and three different spatial partitions of our study area to develop six different estimations of the LCCM. We validated model simulations with 2002 land cover. We combined UrbanSim land use outputs and LCCM simulations to predict changes in avian species richness. Results indicate that landscape composition and configuration were important in explaining land cover change as well as avian species response to landscape change. Over the next 28 years, urban land cover was predicted to increase at the expense of agriculture and deciduous and mixed lowland forests. Land cover changes were predicted to reduce the total number of avian species, with losses primarily in native forest specialists and gains in common synanthropic species such as the American Crow (Corvus brachyrhynchos). The integrated modeling framework we present has potential applications in urban and natural resource planning and management and in assessing of the effects of policies on land development, land cover, and avian biodiversity.     

Modeling exurban development near Washington, DC, USA: comparison of a pattern-based model and a spatially-explicit econometric model

The development of private rural lands can significantly fragment landscapes, with potentially negative consequences on ecosystem services. Models of land-use trends beyond the urban fringe are therefore useful for developing policy to manage these environmental effects. However, land-use change models have been primarily applied in urban environments, and it is unclear whether they can adequately predict exurban growth. This study compared the ability of two urban growth models to project exurban development in north-central Virginia and western Maryland over a 24-year period. Pattern-based urban growth models (such as SLEUTH) are widely used, but largely mimic patterns that emerge from historic conditions rather than allowing landowner decision-making to project change. In contrast, spatially-explicit econometric models (such as the complementary log?Clog hazard assessed in this study) model landowner choices as profit-maximizing behavior subject to market and regulatory constraints. We evaluated the two raster-based models by comparing model predictions to observed exurban conversion at pixel and county scales. The SLEUTH model was more successful at matching the total amount of new growth at the county scale than it was at the pixel scale, suggesting its most appropriate use in exurban areas is as a blunt instrument to forewarn potential coarse-scale losses of natural resources. The econometric model performed significantly better than SLEUTH at both scales, although it was not completely successful in fulfilling its promise of projecting changes that were sensitive to policy. The lack of significance of some policy variables may have resulted from insufficient variation in drivers over our study area or time period, but also suggests that drivers of land use change in exurban environments may differ from those identified for urban areas.     

Impact of land use compactness on the habitat services from green infrastructure in Wuhan,China

As an important carrier of biodiversity, green infrastructure (GI) is significantly affected by changes in urban land use in the process of urbanization. In this study, habitat services from GI were used to represent biodiversity support services, and geographic information system (GIS), remote sensing and statistical analysis were combined to analyze the changes in urban land use compactness and GI habitat services in Wuhan in 2005, 2013 and 2018. Seven indices for land use compactness mainly covering three aspects (land use morphology, land use intensity and land function layout) were selected to explore the correlation and regression relationships between urban land use compactness and habitat services on a grid scale. The results show that: i) The urban land use in Wuhan presents a compact development trend except for the degree of integration; and habitat services decrease along with increasing land use compactness. ii) Relative to land use morphology and land function layout, land use intensity is a more prominent factor affecting habitat services, with construction land density and residential land density always being significant indicators for the habitat pattern. iii) Urban areas with different degrees of land use compactness tend to have different indices that affect the habitat services. Therefore, differential urban development strategies should be formulated based on the regional characteristics of land use compactness levels, so as to coordinate urban compact land use and biodiversity conservation.     

Spatial simulation of landscape changes in Georgia: A comparison of 3 transition models

Spatial simulation models were developed to predict temporal changes in land use patterns in a piedmont county in Georgia (USA). Five land use categories were included: urban, cropland, abandoned cropland, pasture, and forest. Land use data were obtained from historical aerial photography and digitized into a matrix based on a 1 ha grid cell format. Three different types of spatial simulation were compared: (1) random simulations based solely on transition probabilities; (2) spatial simulations in which the four nearest neighbors (adjacent cells only) influence transitions; and (3) spatial simulations in which the eight nearest neighbors (adjacent and diagonal cells) influence transitions. Models and data were compared using the mean number and size of patches, fractal dimension of patches, and amount of edge between land uses. The random model simulated a highly fragmented landscape having numerous, small patches with relatively complex shapes. The two versions of the spatial model simulated cropland well, but simulated patches of forest and abandoned cropland were fewer, larger, and more simple than those in the real landscape. Several possible modifications of model structure are proposed. The modeling approach presented here is a potentially general one for simulating human-influenced landscapes.     

Modeling habitat dynamics accounting for possible misclassification

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.     

Forest conservation and land development in Puerto Rico

In the Caribbean island of Puerto Rico, rapid land-use changes over the past century have included recent land-cover conversion to urban/built-up lands. Observations of this land development adjacent to reserves or replacing dense forest call into question how the changes relate to forests or reserved lands. Using existing maps, this study first summarizes island-wide land-cover change between 1977-78 and 1991-92. Then, using binomial logit modeling, it seeks evidence that simple forest cover attributes, reserve locations, or existing land cover influence land development locations. Finally, this study quantifies land development, reserve protection and forest cover by ecological zone. Results indicate that 1) pasture is more likely to undergo land development than shrubland plus forest with low canopy density, 2) forest condition and conservation status appear unimportant in that development locations neither distinguish between classes of forest canopy development nor relate to forest patch size or reserve proximity, and 3) most land development occurs in the least-protected ecological zones. Outside the boundaries of strictly protected forest and other reserves, accessibility, proximity to existing urban areas, and perhaps desirable natural settings, serve to increase land development. Over the coming century, opportunities to address ecological zone gaps in the islands forest reserve system could be lost more rapidly in lowland ecological zones, which are relatively unprotected.This revised version was published online in May 2005 with corrections to the Cover Date.     

Occurrence pattern of Pararge aegeria (Lepidoptera: Nymphalidae) with respect to local habitat suitability, climate and landscape structure

Distribution patterns of wild species are affected by environmental variables, such as climate, anthropogenic land use or habitat quality, which act simultaneously at different scales. To examine the relative importance of particular factors and scales on population response we investigated the speckled wood butterfly Pararge aegeria (L.) as a model organism occupying semi-natural habitats. Its distribution was recorded in 23 study sites (5×5 km) over a 2 year study period. The sites were located in agricultural landscapes within seven Temperate European countries. Environmental predictors were mapped at a local and a regional scale. Logistic regression models were then developed to represent humid (beneficial) and dry (adverse) weather conditions during larval development. The humid year model predicted that P. aegeria is equally but generally not very dependent on local and regional factors, resulting in generally high occurrence probabilities. In contrast, the dry year model predicted severe restrictions of P. aegeria to both high quality patches and landscapes with beneficial structural and climatic preconditions. As both models resulted in entirely different predictions, our study showed that the sensitivity of P. aegeria to local and landscape features might change, and that factors of less importance could easily become limiting factors. The results stress that high quality landscape is important at both the local and regional scale even for species that are considered relatively robust. They also sound a note of caution when predictions about population response for management purposes are based on just a single or a few year(s) of observation.     

Modeling black carbon removal by city trees: Implications for urban forest planning

Urban forests provide multiple ecosystem services, including particulate matter (PM) air pollution removal. While previous studies have assessed relationships between atmospheric PM concentrations and urban land use and land cover, few studies have modeled PM removal by trees in relation to urban form (e.g., topography, land use, land cover, and proximity to emission sources). Particulate matter is a mixture of particles, including black carbon (BC), a byproduct of incomplete fossil fuel and biomass combustion with strong warming potential and linked to adverse health outcomes. We coupled empirical BC deposition data, collected from urban trees in Denton, Texas, with 226 urban form variables to generate land use regression models of annual and seasonal BC removal. Annual and seasonal models revealed emission source proxies, terrain exposure towards emission sources, and topographic exposure as influential to BC removal by trees. Regression equations were applied at one-meter resolution to estimate the BC removal potential of tree planting across the city. The resultant maps, which show regions of probable high and low BC removal by trees, can be used by arborists, urban foresters, landscape architects, and urban planners to inform urban forest design, planning, and decision-making.     

Predicting mammal species richness and distributions: testing the effectiveness of satellite-derived land cover data

Mapping species richness and distributions is an important aspect of conservation and land use planning, but the time and cost of producing maps from field surveys is prohibitive. It is useful, therefore, if mappable environmental variables, from a readily accessible source, can be used as surrogates for species attributes. We evaluated the power of satellite-derived land cover information, from the Land Cover Map of Great Britain, to predict species richness and occurrences of terrestrial mammals in one hundred 10×10 km quadrats, from four regions of Britain. The predictive power of the land cover data was relatively poor – with a few exceptions, land cover explained less than half of the variation in mammal species richness and occurrence in regression models. Predictive power was considerably stronger when regions were analyzed separately than when analyzed together, and best fitting models varied between regions and between mammal taxa. Predictive power was also affected (positively or negatively depending on taxon) when PCA-ordinated land cover variables were used as predictors. The predictive strength of the land cover data was probably limited mostly by the high proportion of British mammal species with geographic distributions changing rapidly and independently of land cover (and hence the non-saturation of preferred habitats), and to a lesser extent by shortcomings in the mammal and land cover data, and the influence of landscape factors other than land cover on mammal distributions. The results suggest that regional stratification is essential when attempting to predict species richness and distributions, even across relatively limited areas such as Great Britain. We conclude that caution is necessary in using results from environmental information systems such as this as a basis for conservation and land use planning decisions.     

A satellite image-based analysis of factors contributing to the green-space cool island intensity on a city scale

Urban green spaces provide cooler microclimates and create localized urban cool islands and, as part of an adaptation strategy to cope with future urban climate change, have been proposed as a means to mitigate the urban heat island effect. Numerous previous research papers have discussed green-space size, type, and vegetation density, as well as many other factors that might influence green-space cooling effects. However, little has been done with regard to exploring and quantifying the characteristics of the green-space cool island (UCI). It is also largely unknown whether or how the patterns of green space and land use, as well as the adjacent urban thermal environment, affect UCIs. In this paper, the land surface temperature (LST) was retrieved from satellite imagery. The UCI was identified, and one of the UCI characteristics, the UCI intensity, was defined. Multiple linear regression models were then used to explore and quantify the combined effects of factors related to the UCI intensity. The results show that the intensity differed between UCIs, and correlated significantly with the extent of and mean temperature reduction associated with an UCI. Multiple linear regression analysis found that UCI intensity was affected by areas of forest vegetation and its spatial arrangements, as well as by the composition of the cool island and its neighboring thermal environment. The study validated the suitability of using intensity as an indicator of the UCI. Identifying the UCI as a result of the green-space cooling effect, will help in the management and planning of the spatial arrangement of green spaces in cities to reduce the effects of the urban thermal environment and help cities adapt to climate change.     

Contribution of time-series data cubes to classify urban vegetation types by remote sensing

Mapping urban vegetation types is important for urban planning and assessing environmental justice. Nowadays, despite data cubes projects are providing Analysis Ready Data to facilitate time-series analysis, we did not found studies employing these data for improving urban vegetation mapping. By relying solely on open data and software, this work proposes and evaluates the integration of time-series data cubes in a hybrid image classification method to map the intra-urban space, differentiating Tree cover and Herb-shrub. The urban area of Goiânia, Goiás, Brazil, is the study area. The hybrid method combined object-based classification of a pan-sharpened CBERS-4A WPM image (spatial resolution of 2 m) with the pixel-based classification of Sentinel-2 MSI time-series data cubes (10 m). Both approaches used the Random Forest algorithm. Objects from the CBERS-4A segmentation composed the spatial unit of analysis and the class assignment depended on the Sentinel-2 time-series urban land cover probabilities. Based on both Maps probabilities, Shannon entropy was calculated to attribute the final urban land cover to the objects. Urban land cover probabilities presented similar spatial distribution patterns for both classification approaches. Regarding the thematic maps, the Herb-shrub cover area was 35% higher in Sentinel-2 time-series classification than in GEOBIA classification, but Tree cover was 21% lower. In general, 75% of the study area was equally classified by the initial approaches. However, for 9% of the remaining area, the hybrid classification improved vegetation classes accuracies by 35%, contributing to the vegetation covers identification. Thus, this study contributes to methodological procedures for urban land cover study and demonstrates that hybrid maps based on open data are effective to reduce classification mistakes, allowing more accurate monitoring, planning, and designing of different urban vegetation types. Future research efforts should focus on scale compatibility between data of different spatial resolutions and expand the use of data cubes to integrate time-series information into the GEOBIA classification.     

GeoAI to implement an individual tree inventory: Framework and application of heat mitigation

Individual Tree Inventory (ITI) is critical for urban planning, including urban heat mitigation. However, an ITI is usually incomplete and costly due to data collection challenges in the dynamic urban landscape. This research developed a methodical GeoAI framework to build a comprehensive ITI and quantify tree species cooling on rising urban heat.The object detection Faster R-CNN model with Inception ResNet V2 was implemented to detect individual trees canopy and seven tree species (Callery pear, Chinese elm, English elm, Mugga ironbark, Plane tree, Spotted gum and White cedar). The land surface temperature (LST) was derived from Landsat 8 surface reflectance imagery. Two models for ITI were further developed for spatial and statistical analysis. Firstly, an ‘Individual tree-based model’ stores the attributes of tree species and its vertical configuration obtained from LiDAR, along with its tree canopy area and surface temperature. Secondly, the ‘LST zone-based model’ stores tree canopy cover and building areas in each zone unit. Pearson correlation, global linear regression, and geographically weighted regression (GWR) were applied to establish the relationship between tree attributes, building areas (explanatory variables) with local temperature (dependent variable). Results showed that English elm has the highest cooling and least by Mugga ironbark in the study area. GWR results demonstrate that 94% of the LST was explained by tree height and tree canopy area. The LST zone-based model showed that 85% of the LST was explained by the percentage of tree species and buildings. Maps of the local R² and coefficients of the independent variables provide spatially explicit information on the cooling of different tree species compared to building areas. The implemented GeoAI approach provides important insights to urban planners and government to monitor urban trees with the enhanced Individual Tree Inventory and strategies mitigation plan to reduce the impact of climate change and global warming.     

Matrix Matters: Effects of Surrounding Land Uses on Forest Birds Near Ottawa,Canada

Matrix quality affects probability of persistence in habitat patches in landscape simulation models while empirical studies show that both urban and agricultural land uses affect forest birds. However, due to the fact that forest bird abundance and species richness can be strongly influenced by local habitat factors, it is difficult to analyze matrix effects without confounding effects from such factors. Given this, our objectives were to (1) relate human-dominated land uses to forest bird abundance and species richness without confounding effects from other factors; (2) determine the scale at which forest birds respond to the matrix; and (3) identify whether certain bird migratory strategies or habitat associations vary in richness or abundance as a function of urban and agriculture land uses. Birds were surveyed at a single point count site 100 m from the edge of 23 deciduous forest patches near Ottawa, Ontario. Land uses surrounding each patch were measured within increasingly large circles from 200 to 5000 m radius around the bird survey site. Regression results suggest that effects of urban and agricultural land uses on forest birds (1) are not uniformly positive or negative, (2) can occur at different spatial scales, and (3) differentially affect certain groups of species. In general, agriculture appeared to affect species at a broad spatial scale (within 5 km), while urban land use had an impact at both a narrower spatial scale (within 1.8 km) and at the broad scale. Neotropical and short distance migrant birds seemed to be the most sensitive to land use intensification within the matrix. Limiting urban land use within approximately 200–1800 m of forest patches would be beneficial for Neotropical migrant birds, which are species of growing conservation concern in temperate North America.     

Developing a complementary framework for urban ecology

Cities are characterized by dynamic interactions between socio-economic and biophysical forces. Currently more than half of the global population reside in cities which influence the global biogeochemical cycles and climate change, substantially exacerbating pressures on urban pollution, water quality and food security, as well as operating costs for infrastructure development. Goods and services such as aesthetic values, water purification, nutrient recycling, and biological diversity, that urban ecosystems generate for the society, are critical to sustain. Urban planners are increasingly facing the considerable challenges of management issues for urban ecosystems. Poor understanding of the complementary roles of urban ecology in urban infrastructure, and the functioning of ecosystems and ecological resilience of a complex human-dominated landscape has impeded effective urban planning over time, resulting in social disharmony. Here a complementary framework for urban ecology is proposed, in which ecosystems interact with land use, architecture and urban design – “E-LAUD” – affecting ecosystem and human health, and building on the concept that land uses in urban green areas, road-strips, wetlands, ‘habitat islands’ and urban architecture could synergistically benefit when clustered together in different combinations of urban landscapes. It is proposed that incorporation of the E-LAUD framework in urban planning forms the context of a new interdisciplinary research programme on ecological resilience for urban ecosystems and helps promote ecosystem services.     

Maintaining the carbon–oxygen balance in residential areas: A method proposal for land use planning

The efforts to mitigate CO₂, one of the greenhouse gases causing global warming and climate change problems, have an important place in the worldwide agenda. One of the factors that cause CO₂ emissions is energy use in residential buildings, hence urban residential use is one of the areas on which mitigation efforts are focused. As land use planning determines the spatial development, density and form of residential areas, it has a direct role in the formation of residential emissions. However, these efforts to mitigate emissions could not yet be integrated with land use planning. It is possible to list the reasons as the mitigation efforts are made either on the building scale (mitigation of residential emissions by technological–architectural solutions) or on a global–regional scale (development of carbon sinks), the focus on transportation sector, and in land use planning, lack of considering the potential of urban green areas to mitigate emissions. By taking into consideration the O₂ production potential of green areas, a method proposal for land use planning is developed in this study to maintain the carbon–oxygen balance in residential areas. Depending on the form and density of the residential areas developed in a plan, the necessary size of green areas in such a plan is estimated by the method proposed. The varying emission amounts in different residential areas with diverse housing types, the number of actively used residential units, and local vegetation differences are included in the estimate. Therefore, an estimation method proposal that could be easily used in land use planning and that considers local differences is presented in the paper.