Detailed land use classification model based on vegetation indices and texture features

The United States has witnessed various extreme land use changes over the years. These changes led to alterations in watersheds’ characteristics, impacting their water quality and quantity. To quantify this impact in highly urbanized watersheds such as the Chicago Metropolitan Area, it is crucial to examine the characteristics and imperviousness distribution of urban land uses and available point and non-point sources. In this paper, the effect of urban runoff and nutrient loadings to water bodies in the Chicago River Watershed resulting from level (III) detailed urban land uses is investigated. A watershed scale hydrologic and water quality simulation using BASINS/HSPF model was developed for the highly urbanized watershed. Appropriate considerations were given to the effective impervious area (EIA). The results from the five-year calibrated water quality simulation were reasonably reflected with observed data in the study area and nutrient loadings of both point and non-point sources for 44 different land uses were found. The export coefficients (EC) values obtained are site-specific depending on conditions and variables at the watershed level such as physical characteristics, land use management practices, hydro-meteorological and topographical data, while using a continuous simulation approach and watershed perspective analysis. This is the first attempt to measure and model nutrients’ loadings using detailed land use types in the Chicago River Watershed. The proposed continuous calibrated and validated model can be used in the investigation and analysis of different scenarios and possible future conditions and land utilization.

A detailed land use plan is necessary to conserve farmland in disadvantaged areas. Although many studies have been done on land classification for farmland preservation, little has been done on the economic measure based method as applied to detailed land use planning. This paper presents an economic land classification method for farmland preservation.We chose 83 plots of farmland in the mountainous area of Ohda City, Shimane Prefecture as a case study. Serious farm abandonment and subsequent problems including no insect management and wild boar damage to rice crops have occurred because of the aging and depopulation of farm households and stagnant price of rice products.First, land productivity of each plot was estimated by a logistic regression model with surveyed farm data including farmland yield, area, damage by wild boars, rice lodging and solar radiation. The survey was conducted from August to September in 2002. Second, an estimation function for labor productivity was determined by substituting harvest time, farmland area, length of the short side of a farm field and probability of rice lodging in the model. The estimation successfully fit the harvesting time stated by each farmer. Subsequently, a mathematical programming model was constructed to estimate the expected income under the given conditions. Negative externality by neighboring abandoned farmlands, irrigation facilitymanagement and movement frequency between fields were considered in the model.As a result, optimal land use plans were developed under the given labor input. In the simulated case where scattered abandonment occurred, the effect of negative externality was larger than that for the optimized case. We also simulated the effects of particular farmland conservation on environmental and other concerns.The advantage of this method is that we can determine which land plots should be conserved by economic measures in definite consideration of political effects and special characteristics such as negative externality or movement frequency. For improvement of the model, data reliability of the externality effect needs to be extended. Year round labor input and multiple subjects should also be considered in the model.

A number of general circulation model (GCM) experiments have shown that changes in vegetation in the Sahel can cause substantial reductions in rainfall. In some studies, the climate sensitivity is large enough to trigger drought of the severity observed since the late 1960s. The extent and intensity of vegetation changes are crucial in determining the magnitude of the atmospheric response in the models. However, there is no accurate historical record of regional vegetation changes extending back to before the drought began. One important driver of vegetation change is land use practice. In this paper the hypothesis that recent changes in land use have been large enough to cause the observed drought is tested. Results from a detailed land use model are used to generate realistic maps of vegetation changes linked to land use. The land use model suggests that cropland coverage in the Sahel has risen from 5% to 14% in the 35 yr prior to 1996. It is estimated that this process of agricultural extensification, coupled with deforestation and other land use changes, translates to a conversion of 4% of the land from tree cover to bare soil over this period. The model predicts further changes in the composition of the land surface by 2015 based on changes in human population (rural and urban), livestock population, rainfall, cereals imports, and farming systems. The impact of land use change on Sahelian climate is assessed using a GCM, forced by the estimates of land use in 1961, 1996, and 2015. Relative to 1961 conditions, simulated rainfall decreases by 4.6% (1996) and 8.7% (2015). The decreases are closely linked to a later onset of the wet season core during July. Once the wet season is well developed, however, the sensitivity of total rainfall to the land surface is greatly reduced, and depends on the sensitivity of synoptic disturbances to the land surface. The results suggest that while the climate of the region is rather sensitive to small changes in albedo and leaf area index, recent historical land use changes are not large enough to have been the principal cause of the Sahel drought. However, the climatic impacts of land use change in the region are likely to increase rapidly in the coming years.

Background, aims and scope Most life cycle inventory data for crops do not include the ultimate (marginal) land use induced by crop consumption. The aims of this study were to present, document and discuss a method which can solve this problem and, furthermore, to present concrete examples for wheat consumption in Brazil, China, Denmark and the USA. A global scope is applied and the simulated adaptation to increased wheat demand corresponds to a long-term temporal scope under present market conditions with present technology. Materials and methods The economic general equilibrium model, Global Trade Analysis Project (GTAP) is modified and applied. Agricultural statistics and a number of global land use and land cover datasets are used in the modification and the processing of the model output. Some of the land use datasets are processed by use of a geographic information system tool. Results The net expansion of the global agricultural area per tonne of wheat consumed in Brazil, China, Denmark and the USA is estimated at 2,000, 260, 1,700, and 3,200 m 2 , respectively. For Brazil, the expansion mainly affects tropical evergreen forest. For China and the USA, the expansion mainly affects boreal deciduous forest, savanna, open shrubland and tropical evergreen forest, and for Denmark, it mainly affects savanna, tropical evergreen forest and dense shrubland. The areas affected are quantified in the land use life cycle inventory (LCI) produced for the four countries. Discussion The method applied allows for an even more detailed land use LCI than the one presented in this study. Results are influenced by existing global trade patterns and their inertia. Such aspects should be acknowledged in life cycle assessment (LCA). The method takes its starting point in consumption rather than production. Conclusions The method presented makes it possible to simulate the main mechanisms of the global agricultural system and thereby construct an LCI containing the land use induced by crop consumption in a given region and the nature types (biomes) affected. The results are sensitive to changes in the so-called Armington elasticities representing the inertia of global trade patterns. It is considered reasonable to double the standard elasticities in the GTAP model for the construction of LCI data. Wheat consumption in different countries result in different land use consequences due to differences in trade patterns, which are governed by transport and trade costs, among other factors. Recommendations and perspectives The modelling could be improved by incorporating a mechanism simulating

Floods are among the most destructive natural disasters, resulting in significant loss of life and property for millions of people around the world. Extreme flood events in the foothills of the Himalayan ranges and their forelands are closely linked to heavy monsoonal rainfall, steep slopes, and excessive surface runoff from the uphills. Floods hazards in Nepal have become increasingly devasting due to improper land use planning, unplanned settlement distribution, deforestation, land degradation in the upstream watershed, topography, geological setting and climate change. Nepal was hit by an unprecedented late monsoon rainfall, causing widespread landslides and flooding across the country in September 2024y, resulting in significant loss of life and property. This study investigated the use of Sentinel-1 Synthetic Aperture Radar (SAR) with Ground Range Detected (GRD) scenes  for rapid and robust flood detection during the September 2024 flood events in Kathmandu valley and the surrounding areas. The study area is of utmost interest as it comprises diverse geographical setting on the basis of topography and geological setting and these floods events have a significant impact on settlement, infrastructure and other environmental processes. In the study, a standard workflow was applied for the pre-processing of both the products. Based on the application of pre- and post-SAR imagery, this study estimated the extent of flood inundation, highlighting the major impacted area based on pre- and post-land cover map of the study area using machine learning (ML) algorithms and compare the changes with spectral indices. The change detection and Normalized Difference Flood Index (NDFI) was evaluated using threshhold value of temporal Sentinel-1 GRD data. High resolution Google Earth imagery was used for the accuracy assessment of pre flood environment; post flood site data was evaluated from field visit. Greater level of flood impacts were noted both within the Kathmandu valley (Kathmandu. Bhaktapur, Lalitpur district) and outside the valley Banepa, Dhulikhel, Panauti, Namobuddha, Roshi local area of Kaverepalanchok district; Sunkoshi, Golanjor , Phikkal local areas of Sindhuli district of the study area. The overall accuracy of flood inundation mapping was 95 % and the accuracy of land cover map was evaluated about 88 %. A detailed land use/ cover map of the study area was prepared to present the changes post-flood environment using Sentinel -2 Multi-spectral imagery. Further, Permanent water body (PWB) using Normalized Difference Water Index (NDWI) algorithm and Normalized Difference Vegetation Index (NDVI) were prepared for the evaluation of the post-flood impact area . Overall, the analysis inferred that watershed level flooding vulnerability results from natural factors like heavy rainfall and topography, which are further intensified by human activities such as infrastructure development, urbanization and poor land management.

The authors have been developing Advanced GIS Delineation Data of the upper Kanda river basin by detailed land use recognition. When using distributed water circulation model, grid land use data of 100m or 10m-mesh division is typically used. However there has been no study conducted to verify the permeable area set by the 100m or 10m-mesh land use classification. In this study, we estimated the true permeable area ratio of 10m-mesh land use classification using the advanced GIS delineation. As a result, it was identified that the frequency distribution of permeable area ratio and the characteristic of spatial distribution of permeable area ratio by 10m-mesh.

Urban planning has a considerable impact on the economic performance of cities and on the quality of life of their populations. Efficiency at this level has been hampered by the lack of integrated tools to adequately describe urban space in order to formulate appropriate design solutions. This paper describes an ontology called LBCS-OWL2 specifically developed to overcome this flaw, based on the Land Based Classification Standards (LBCS), a comprehensive and detailed land use standard to describe the different dimensions of urban space. The goal is to provide semantic and computer-readable land use descriptions of geo-referenced spatial data. This will help to make programming strategies available to those involved in the urban development process. There are several advantages to transferring a land use standard to an OWL2 land use ontology: it is modular, it can be shared and reused, it can be extended and data consistency maintained, and it is ready for integration, thereby supporting the interoperability of different urban planning applications. This standard is used as a basic structure for the “City Information Modelling” (CIM) model developed within a larger research project called City Induction, which aims to develop a tool for urban planning and design.

Abstract. Individual travel trajectories denote a series of places people visit along the time. These places (e.g., home, workspace, and park) reflect people’s corresponding activities (e.g., dwelling, work, and entertainment), which are discussed as semantic knowledge and could be implicit under raw data (Yan et al. 2013, Cai et al. 2016). Traditional survey data directly describe people’ activities at certain places, while costing tremendous labors and resources (Huang and Wong 2016). GPS data such as taxi logs record exact origin-destination pairs as well as people’s stay time along the way, from which semantics can be easily inferred combining with geographical context data (Yan et al. 2013). Research has been done to understand the activity sequences indicated by either individual or collective spatiotemporal (ST) travel trajectories using those dense data. Different models are proposed for trajectory mining and activity inference, including location categorization, frequent region detection, and so on (Njoo et al. 2015). A typical method for matching a location or region with a known activity type is to detect stay points and stay intervals of trajectories and to find geographical context of these stay occurrences (Furtado et al. 2013, Njoo et al. 2015, Beber et al. 2016, Beber et al. 2017).However, limited progress has been made to mine semantics of trajectory data collected from social media platforms. Specifically, detection of stay points and their intervals could be inaccurate using online trajectories because of data sparsity. Huang et al. (2014) define the notion of activity zone to detect activity types from digital footprints. In this method, individual travel trajectories first are aggregated using spatial clustering method such as density-based spatial clustering of applications with noise (DBSCAN). Then produced clusters are classified based on a regional land use map and Google Places application programming interface (API). Such land use data are only published at specific places, such as the state cartography office’s website at University of Wisconsin-Madison. Researchers need to search for those data based on their study area. Moreover, while major land use maps can be searched for large areas such as the whole United States, detailed land use data for statewide or citywide areas are made in diverse standards, which adds extra work to classify activity zones consistently. Besides, Google Places API is a service that Google opened for developers and will return information about a place, given the place location (e.g., address or GPS coordinates), in the search request. However, API keys need to be generated before we can use these interfaces and each user can only make a limited number of free-charged requests every day (i.e., 1,000 requests per 24 hours period). In sum, previous methods to detect activity zone types using social media data are not sufficient and can hardly achieve effective data fusion. Comparing to the high cost of using officially published dataset, emerging Volunteered Geographic Information (VGI) data offer an alternative to infer the types of an individual’s activities performed in each zone (i.e., cluster).Using geo-tagged tweets as an example, this research proposes a framework for mining social media data, detecting individual semantic travel trajectories, and individual representative daily travel trajectory paths by fusing with VGI data, specifically OpenStreetMap (OSM) datasets. First, inactive users and abnormal users (e.g., users representing a company with account being shared by many employees) are removed through data pre-processing (Step 1 in Figure 1). Next, a multi-scale spatial clustering method is developed to aggregate online trajectories captured through geo-tagged tweets of a group of users into collective spatial hot-spots (i.e., activity zones; Step 2). By integrating multiple OSM datasets the activity type (e.g., dwelling, service, transportation and work) of each collective zone then can be identified (Step 3). Each geo-tagged tweet of an individual, represented as a ST point, is then attached with a collective activity zone that either includes or overlaps a buffer zone of the ST point. Herein, the buffer zone is generated by using the point as the centroid and a predefined threshold as the radius. Given an individual’s ST points with semantics (i.e., activity type information) derived from the attached collective activity zone, a semantic activity clustering method is then developed to detect daily representative activity clusters of the individual (Step 4). Finally, individual representative daily semantic travel trajectory paths (i.e., semantic travel trajectory, defined as chronological travel activity sequences) are constructed between every two subsequent activity clusters (Step 5). Experiments with the historic geo-tagged tweets collected within Madison, Wisconsin reveal that: 1) The proposed method can detect most significant activity zones with accurate zone types identified (Figure 2); and 2) The semantic activity clustering method based on the derived activity zones can aggregate individual travel trajectories into activity clusters more efficiently comparing to DBSCAN and varying DBSCAN (VDBSCAN).

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

Urban fringes represent very complex landscapes because of their proximity and mutual dependency with cities and rural areas. These landscapes may be considered as transition entities characterized by fuzzy boundaries. An uncontrolled development of urban sprawl and land use changes in these areas may deter- mine negative impacts on all natural, economic and social components. Thus, urban fringes assume a key-role in modern landscape analysis, planning and management. Landscape analysis of these interfaces, as this study shows, can be effectively supported by GIS spatial modelling. The Settlement Density Index (SDI), developed through GIS spatial analysis techniques, expresses punctually the territorial gradients generated by the presence of settlements and allows the identification of the urban fringes in the two periods under invest-tigation. These areas are then characterized and analyzed quantitatively using detailed land use data. The comparison of the diachronic information highlights the transformations of peri-urban landscapes that appear mainly related to the modifications of spatial configuration of urban areas and to the changes of agricultural systems.

Numerical models were employed to simulate the effects of urban infrastructure on the city-scale precipitation distribution during multiple closely occurring convective squall line events over Chicago. Two high-resolution simulations were inter-compared, one using standard land use databases to initialize the WRF-ARW numerical model and the other using a high-resolution urban canopy formulation and detailed land use databases to initialize the WRF-UCM numerical model. Two squall lines organized and propagated over Chicago during an eight-hour period. The (1 km) spatio-temporal evolution of the first squall line was more accurately simulated by the WRF-UCM than that simulated by the WRF-ARW. The WRF-UCM captures more realistic urban heat island-induced buoyancy forcing when validated against multiple airport meteograms and Doppler radar-derived reflectivity and precipitation. The WRF-UCM increases surface heating, substantially strengthening the surface-based convective available potential energy (SBCAPE) and subsequent cold downdrafts. Additionally, the increased surface heating acts to strengthen and bifurcate the upper-level divergence and energize three low-level jets that converge upon the city and shape the convective organization. While the effect of this additional buoyancy on the first squall line was critical, the second line’s dissipation was not substantially different in the two simulations because of diminishing tropospheric forcing.

Quantifying groundwater availability depends upon sound methods and the use of integrated models. To determine availability or sustainable yield, the influence of scientific uncertainty from key sources, such as anthropogenic recharge, must be considered. This study evaluates uncertainty in recharge interpretations on the modeled available water balance for an urban case in Texas, USA. Analyses are completed using the Groundwater Decision Support System, which is a research code-base for an integrated modeling. The case study develops spatially and temporally resolved recharge interpretations based on NEXRAD precipitation and detailed land use data. Results demonstrate the implications of scientific uncertainty as it influences recommendations for policy and urban water management decisions that are based on modeled outputs. Geospatial methods account for spatial and temporal components and can be replicated for other systems. These methods are also useful for resolving uncertainty in relation to the influence of urbanization on recharge through land use change.

Daily travel behaviour in Beijing, China: An analysis of workers' trip chains, and the role of socio-demographics and urban form

This study examined the possible impact of land use changes on shore-spawning kokanee habitat to explain the decline in Oncorhynchus nerka at Okanagan Lake, British Columbia. Four objectives of the study were: 1 .) to characterize spatial and temporal trends in fish abundance using enumeration data provided by the British Columbia Ministry of Environment, Lands and Parks Regional Fisheries Branch for the period from 1973 and 1997; 2.) to characterize spatial and temporal trends in land use from aerial photographs taken in 1963, 1971, 1981 and 1994-6; 3.) to characterize spatial differences in habitat characteristics from field measurements in the fall of 1997 and spring of 1998; and 4.) to examine associations and causative relationships among land use, habitat features and kokanee abundance. Between 1973 and 1997, the shore-spawning kokanee population decreased by 90.5%. The highest fish counts were reported in 1974 while the lowest were reported for 1993. Within each year, spawning activity was most variable in the southeast quadrant of the lake. Over years, spawning activity also decreased the most in the southeast quadrant. Significance tests revealed that fish enumeration reaches with historically high spawning activity experienced the greatest declines. The current and historical distribution of land use differed between 27 shore segments used for detailed analysis and land use in a 500m buffer zone around the entire lake. Among the 27 study units, forested lands were dominant while sage grasslands dominated the entire lake buffer zone. Changes in land use between 1963 and 1996 were also different. Land use data at the 27 study reaches indicated an increase in urban development and forested lands, and a decrease in agriculture and sage grasslands. Lake-wide data revealed an increase in urban development and a decrease in all other land uses. Despite differences in current and historic land use distributions, temporal trends consistently indicated urban areas increased as agricultural areas decreased. Shallow shoreline areas, shoreline slopes and substrate depths differed significantly among lake quadrants but not study reaches. Shoreline areas were larger in the southeast and southwest quadrants of the lake. Shoreline slopes were steeper and substrate depths were greater in the northwest quadrant. Steeper shoreline slopes, greater substrate depths and more angular substrate characterized enumeration reaches with higher current spawning activity. Shoreline areas with gradual slopes, more shallow substrate depths and less angular substrate characterized areas with a higher density of buildings and docks along the shoreline and larger areas of agriculture and sage grasslands within the 500 m buffer zone. These observations suggest that habitat features may facilitate interactions between land use and spawning activity. Interactions between fish abundance and land use including the area of development, agriculture and sage grasslands were demonstrated, however the associations were weak. Dock density was a better predictor of reduced spawning activity than the areal index of land use adjacent to the lakeshore. The current study examines only the shore-spawning stage of the kokanee life history. Other shorerelated stages that may be impacted by habitat changes associated with land use likely include egg incubation, alevin maturation and fry emergence stages. Additional studies are recommended to examine the relative contributions of these stages to overall fish abundance. At the same time, it is recognized that some shoreline issues may already be addressed with conscientious water level management and control of lakeshore development.

The Markov-cellular automata is suitable to study complex spatial-temporal geographic system, especially for regional land use, and it has been an important tool and research focus for regional land use change modeling. Previous researchers focused on a few kind of land use type at the regional scale and the data resolution was cursory because land use maps were usually derived from TM image. Few researchers involved precise scale of land use change within a region. To solve this problem, we took the data of land-use survey as a data source maps that include detailed multiple land use types. The case study area was Changping District, which is a rapidly growing area of Beijing. We select the land use map of 2001 and 2005 which include the multiple land use types as data source to simulate the land use of 2012. The results of simulation show that simulation accuracy of multiple land use types is better than them of cursory scale land use types, although it takes a substantial amount of time to run. The statistical result derived from Moran's I and fractal parameter indicates that simulation shows the high spatial stability. The simulation results showed that the number of cropland is keeping on decrease from 2005 to 2012 without the holistic sustainable development measures and severe land policy. This paper represents a good try to local land use change modeling as shown combined Markov chain analysis and cellular automata models. The simulated future land use changes have significant environmental and socioeconomic implications for sustainable region land detailed planning in the study area.