A spatial agent-based modeling to allocate land use for brownfield sites on an urban scale

In order to meet the growing global demand for bioproducts, areas of forests planted for productive purposes tend to increase worldwide. However, there are several controversies about the possible negative impacts of such forests, such as invasive potential, influence on water balance and biodiversity, and competition with other types of land use. As a result, there is a need to optimize land use, in order to achieve improvements in terms of sustainability in the broadest sense. In this study, the environmental and economic performances of pine and eucalyptus forest production systems for multiple purposes are compared aiming an optimized allocation of land use in the Center-West Region of Brazil. Life cycle assessment, life cycle cost and analysis of financial and economic indicators were used to assess potential environmental and economic impacts, covering the agricultural and industrial phases of pine and eucalyptus forest systems managed for the production of cellulose and sawn wood and, for pine, the production of rosin and turpentine from the extraction of gumresin and by applying the kraft process. Subsequently, the TOPSIS multicriteria decision-making method was applied to rank production systems in different combinations of phases and criteria, and multi-objective optimization was used to allocate land use according to different restrictions of areas and efficiency. The adoption of cleaner energy sources and the use of more efficient machines, equipment and vehicles are the main solutions to improve the environmental and economic performance of the forestry sector. The production systems of pine for cellulose and pine for sawn wood, rosin and turpentine were identified as the best solutions to optimize land use. For this reason, they must be considered as alternatives for the expansion and diversification of the Brazilian forest productive chain.

Organizations and individuals are allocated land use or land lease by the state and have their “land use right” recognized by the State. The Land Law regime of Vietnam has, however, plenty of complexities and controversies reflecting policy conflicts. The Land Law, on the other hand, lacks detailed provisions on the rights and obligations of the land users. The 2015 Civil Code has been an attempt to provide further elaborations for the debated aspects of the Land Law. The author was a member of the drafting team of the 2015 Civil Code at the Ministry of Justice of Vietnam and has been involved in the efforts to deal with varieties of the policy conflicts on land. The Civil Code generally provides that the ownership rights and other property rights are restricted by the State in emergency cases or for environmental protection, assurance of social order and safety, etc.

We describe three challenges for biodiversity planning, which arise from a study in Papua New Guinea, but apply equally to biodiversity planning in general. These are 1) the best use of available data for providing biodiversity surrogate information, 2) the integration of representativeness and persistence goals into the area prioritization process, and 3) implications for the implementation of a conservation plan over time. Each of these problems is linked to the effective use of complementarity. Further, we find that a probabilistic framework for calculating persistence-based complementarity values over time can contribute to resolving each challenge. Probabilities allow for the exploration of a range of possible complementarity values over different planning scenarios, and provide a way to evalua!e biodiversity surrogates. The integration of representativeness and persistence goals, via estimated probabilities of persistence, facilitates the crediting of partial protection provided by sympathetic management. For the selection of priority areas and land use allocation, partial protection may be a "given" or implied by an allocated land use. Such an integration also allows the incorporation of vulnerability/threat information at the level of attributes or areas, incorporating persistence values that may depend on reserve design. As an example of the use of persistence probabilities, we derive an alternative proposed priority area set for PNG. This is based on 1) a goal of 0.99 probability of persistence of all biodiversity surrogate attributes used in the study, 2) an assumption of a 0.10 probability of persistence in the absence of any form of formal protection, and 3) a 0.90 probability of persistence for surrogate attributes in proposed priority areas, assuming formal protection is afforded to them. The calculus of persistence also leads to a proposed system of environmental levies based on biodiversity complementarity values. The assigned levy for an area may change to reflect its changing complementarity value in light of changes to protection status of other areas. We also propose a number of complementarity-based options for a carbon credits framework. These address required principles of additionality and collateral benefits from biodiversity protection. A related biodiversity credits scheme, also based on complementarity, encourages investments in those areas that make greatest ongoing contributions to regional biodiversity representation and persistence. All these new methods point to a new "systematic conservation planning" that is not focused only on selecting sets of areas but utilizes complementarity values and changes in probabilities of persistence for a range of decision making processes. The cornerstone of biodiversity planning, complementarity, no longer reflects only relative amounts of biodiversity but also relative probabilities of persistence.

Image scene classification in the remotely sensed (RS) society is an interesting subject that aims to allocate land use / cover semantic information. A big amount of Convolutional neural classification models for RS images have been proposed by the authors due to the massive behaviour of CNNs in features extracted. Despite their impressive results, there are still opportunities for advancement. To begin, local characteristics are just as important as global ones in identifying RS images. The CNNs' hierarchical organizational structure and multidimensional suitable capabilities make them good at trying to capture spatial information [1]. It's not uncommon for the feature maps to be overlooked, moreover. First and foremost, the ranges among RS image pairs must be minimised or maximised in order to achieve satisfying classifier performance. Despite this, the importance of these focuses in classification tasks is undervalued. We propose a new CNN called focus high quality infrastructure (ACNet) depending on the Siamese network in the this paper to improve the robustness discussed previous section. [3]To begin, the input data for ACNet are the picture groups achieved through spatial rotation as a result of the network's the double framework.

As one of the last agricultural frontiers of the humid tropics, Amazonia is the largest area of the world currently undergoing frontier settlement. Although the earliest intrusions of foreign populations into Amazonia date from pre-Hispanic times, the large-scale entrance of peasant colonists into the vast region is a recent phenomenon. Much of this movement represents the spontaneous migration of peoples, but governments in the region have also become increasingly interested in opening up and integrating Amazonia to national and international economies. These actions are frequently seen as potential solutions to a number of national problems, including the need to increase agricultural production, correct spatial imbalances in the distribution of population, exploit frontier lands for reasons of national security, and defuse potentially serious political problems resulting from the existing agrarian structure, landlessness, and unemployment. The upper basin of the Amazon in Ecuador, bordering on the eastern slopes of the Andes, is one such area of frontier settlement. Recent decades have witnessed the rapid conversion of these Amazonian forests to agricultural uses through a series of schemes bearing such labels as land development and colonization. Most forest intervention in the region has come at the hands of colonist farmers attempting to establish land claims along transport routes originally constructed to aid in petroleum exploration and exploitation. These are farmers who formerly have made a living in long-established farmlands and who, for various reasons (population pressures, pervasive poverty, maldistribution of farmland, lack of inputs for intensive cultivation, lack of nonagrarian livelihood opportunities, and generally inadequate rural development) have been increasingly squeezed out of their homelands. A marginal person by virtue of his low socioeconomic and political status, the farmer often perceives

This study analyzes the spatial and temporal characteristics of urban land use structure of more than 18,000 small towns in China by using the National Urban Land Use Survey Data between 2009 and 2013. It finds that the urban land area of small towns expanded rapidly during the research period. The spatial pattern of urban land use structure in small towns exhibits significant regional differences. Small towns in developed coastal regions, particularly those located in global urban areas, have a higher urban land use scale relative to small towns in central and western regions. However, the urban land use scale of small towns located in less developed inland areas has grown faster. The spatial distribution of urban stock land in small towns is mainly influenced by demographic and socio-economic factors. However, the spatial distribution of new urban land is primarily influenced by the land supply policy, which is tilted toward the central and western regions. In terms of structure, the proportion of production and living land in small towns is high, while the proportion of public services and facilities land is low. This trend is being strengthened, with commercial service land and industrial, mining, and storage land becoming the major drivers for the rapid rise of urban land in small towns. The allocation of construction land quotas should be consistent with the direction of population movement and the demands of socioeconomic development.

Land use/land cover change (LUCC) is an important content of geographical research on global change today, and urban land-use change is a complex dynamic and spatio-temporal process because city is not only the most concentrated region of mankind's activity, but also the most impressionable sapce interacts with the humanities factor and natural factor. Urbanization is focus of human and land's relation, which mainly performs expansions of the city space. So how to simulate this process is a key problem. In recent years, much attention is paid to urban land-use change simulations because they can betterly display the mechanism of urban land use change and make out the relevant policies. Spatial simulation on the urban land use pattern is one of the key content of LUCC. While modeling is an important tool for simulating land use pattern due to its ability to integrate measurements of changes in land cover and the associated drivers. Spatial data, like land-use data, have a tendency to be dependent (spatial autocorrelation), which means that when using spatial models, a part of the variance may be explained by neighbouring values. The classic regression model can only analyze the correlation between land use type and driving factors, but cannot depict the spatial autocorrelation. In this paper, Nanjing in Jiangsu Province being located in the Yangtze River Delta was selected as the research area for its fast development of economy, intense human activities and the obvious land use change. According to Landsat TM images of year 2003, the land use information of Nanjing city in 2003 is extracted by the unsupervised classification and manual interpreter methods, and the land use type is redivided into cultivated land, forest land, construction land and virgin land. All driving factors such as distance to town, distance to river, distance to road, population density, DEM, slope and aspect were produced with ArcGIS spatial analysis means. Then the weighting coefficient of every land use type was analyzed with SPSS13.0. The creative idea in this paper is the land use pattern in Nanjing city were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use pattern on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a classic logistic model, this paper sets up a regression model (AutoLogistic model), which considers the spatial autocorrelation factor. And use the model to simulate and analyze the spatial land use pattern in Nanjing city. In addition, the results of two different models are validated by an ROC method. The ROC can compare a map of actual land use distribution to maps of modeled probability for land use types. Through comparison with the classic logistic model without considering the spatial autocorrelation, this model showed better goodness of fitting and higher accuracy of fitting. The area under ROC curves (AUC) of arable-land, wood-land, and building-land from classic logistic regression model were 0.783, 0.824 and 0.751 respectively. The distribution of land use types of arable-land, wood-land and building-land yielded areas under the ROC curves (AUC) were improved to 0.812, 0.877 and 0.806 respectively when using Autologistic model. It is argued that the improved model based on autologistic method is reasonable in some degree, and these types of analysis can provide valuable information for modeling future land use change scenarios that need to consider local and regional conditions of actual land use, and the probability maps of land use types obtained from this study can also support government decisions on land use management for Nanjing city and similar areas. Few attempts have been made to model the pattern of urban land-use based on autologistic model. This paper proposes a new approach to predicting probability models of urban land-use pattern using the application of autologistic regression coupled with GIS, and it is an attempt of incorporating components describing the spatial autocorrelation into a classic logistic model to stimulate the urban land-use pattern, the outcomes can help to understand and explain the causes, locations, consequences and trajectories of urban land-use change, and provide a great support service for land-use planning and policy-making activities.

Land use change can be predicted by spatial modeling. One of spatial method to predict land use change is CLUES (Conversion of Land Use Changes and its Effect at small regional extent). The objective of the research is to determine factors that influence land use change in Kabupaten Majalengka and to predict its land use in the future. Data applied in this research project was biogeophysic, socio economic and existing land use data of Kabupaten Majalengka. Eight land use classes applied were shrub, forest, pond, dry land agriculture, pasture, settlement, and plantation and rice field. Land use classes were dependent variables; and biogeophysical and socio economical data were independent variables. Binary logistic regression was applied to determine influenced factors to land use change. Factors that influence of type of land use’s occurrence were different for each type of land use. The value of the factors was used as an input in land use modeling using CLUES. Scenario that used were scenario with land use change rate same as previous change. The simulation was applied for 12 years with the baseline data in the year of 2009. The simulation result shows that the land use types of shrub, forest, and plantation are decrease. The area that increased are land use types of dry land agriculture, settlement and rice field; however land use types of pond and pasture are remain constant.

<p><span>Nature-Based Solutions (NBS) could be effective measures to respond to land degradation processes and events such as floods. This study demonstrates how to evaluate the benefits of afforestation being a long-term NBS utilizing a combination of an innovative monitoring technology and modelling approaches. The catchment Geroldsbach-Götzens is used as a lead catchment, being typical for numerous Alpine catchments with interacting urban and torrential features. The catchment comprises NBS such as afforestation being installed in the torrent since the early 1950ies.</span></p><p><span>We use an artificial rainfall runoff test site to test different scenarios and analyse runoff behaviour. Besides artificial rainfall simulations, the site is equipped for continuous monitoring of natural occurring rainfall runoff events. In that course, precipitation, snowfall, snow cover, air and soil parameters are assessed. The development and effects of measures over time are modelled utilizing as well the monitoring data. For generalizing and upscaling of the findings, especially with regard to (a) land use in torrents and (b) land use at the urban scale, models are realized as well for other catchments. Beyond realizing historic and current situations exclusively, land use scenarios for assessing the change over time and potential future scenarios are to be modelled.</span></p><p><span>Results can provide a quantification of the benefits and co-benefits of NBS such as: reduction of flood risks, improvement of the recreational qualities, and enhancement of biodiversity. Experience from the field can show the best practices and how to develop innovative ways that can be used for upscaling. Land use and climate scenarios give an indication of changes that can be expected over time and potential future scenarios. Overall findings lead to a better understanding of long-term implementation of NBS and support decision making of stakeholders in other catchments.</span></p>

The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean.

Mapping seismic vulnerability at urban scale: Discussion on relevant cartography representations and smoothing for urban planning purposes on the Oran case study

Urban air quality, which is related to the health of local residents of the Pearl River Delta Region, China, (PRD) has been a hot topic among the masses and academic circles. In addition to economic growth, China’s rapidly increasing urbanization rate has also brought great pressure on urban air quality, in the Region, where due to its huge economic size and population, urban air quality has become the focus of local residents. This study first analyzed the spatio-temporal trends and correlation of land use and PM2.5 average annual concentration, which represents air quality in the PRD from 2000 to 2018, and according to Moran index, PM2.5 concentration in this area has spatial correlation in the study time. Secondly, the spatial error model of the PRD was constructed by using spatial effect, spatial modeling theory and spatial model estimation. The spatial-temporal evolution and influencing factors of PM2.5 concentration were discussed in fixed overall effect. The results showed that the area of cultivated land in the PRD decreased and the area of artificial surface increased year by year, while the PM2.5 concentration increased first and then decreased. In addition, the area of cultivated land and forestland were negatively correlated, while the area of grassland and water body were positively correlated with PM2.5 concentration. The conclusion of this study has a certain theoretical reference for urban land use planning and air quality assurance.

In Africa, rural development and biodiversity conservation, are both important, but sometimes potentially conflicting priorities. Most rural areas adjacent to wildlife protected areas in Southern Africa have high biodiversity potential, but are characterised by high poverty, unemployment, and limited economic activity. The problems in these rural areas are further compounded by problems of crop destruction, and livestock depredation by wildlife. Transfrontier conservation areas (TFCAs), recently introduced in Southern Africa, have potential to address both biodiversity and poverty alleviation through promotion of multiple land uses such as wildlife ranching, tourism, livestock and crop production. It is however, not clear how these land uses can be combined, and what the associated socio-economic costs and benefits of alternative land use options in these areas are. This study proposed a spatial land use model for evaluating alternative land uses and development pathways in these rural areas. The model maximised net revenues from the land, assuming the presence of a social planner. The model proposed, considered a range of socio-economic and biophysical factors, identified jointly with rural communities. The study comprised five empirical chapters in which the following issues are addressed; (i) socioeconomic risks associated with agriculture at the interface, and community attitudes towards wildlife tourism land uses (ii) contribution of existing livelihood strategies to household incomes, (iii) potential for tourism development and (iv) trade-offs in net revenues between different options for land use. The case study areas was Mhinga, one of the rural areas within the Great Limpopo TFCA in South Africa. The study area is situated on the north-western border of Kruger National Park (KNP), next to the Punda Maria park gate. Results showed that the costs by wildlife related damage such as livestock depredation and diseases, were higher than the benefits in employment and subsidies from the park for households. As a consequence attitudes towards wildlife by farmers were generally negative. There was also no mechanism to compensate households incurring wildlife damage. Households living closer to the park had more problems with wildlife damage. When the contribution of different livelihood activities to household incomes were considered, the study found that the main sources of income were the government welfare grants, formal employment and cattle farming. Cattle farmers were not in support of introducing wildlife based land use activities as they considered them to impose costs on other livelihood activities. Some community members were however of the opinion that introducing wildlife tourism could create employment and improve household incomes, especially for those households not engaged in cattle farming. When preferences of tourists, towards supporting forms of ecotourism outside the KNP were analysed, through a choice experiment approach, the study found that tourists were interested in village tours and crafts markets, but generally reluctant to use accommodation facilities outside the park. Analysis of options for land based development at the interface showed that existing land use practices were not optimal. The model results indicate that, by introducing irrigation, tourism and wildlife land uses, net revenues from land could be doubled in the future. It is concluded that, given the socioeconomic and bio-physical constraints characteristic to the area, most income can be obtained by combining all four land uses in the area in optimal proportions. Factors such as property rights, and benefits distribution which could impact the ability of rural communities in the TFCA to support, utilize and benefit from wildlife resources need to be addressed before any land use changes are implemented.

Environmental regulation, land use efficiency and industrial structure upgrading: Test analysis based on spatial durbin model and threshold effect