Global Analysis of Influencing Forces of Fire Activity: the Threshold Relationships between Vegetation and Fire

Abstract : Manylarge scale firestudies considered the relationships between fire and its influencing factors as smooth.However, the responses of fire activity to influencing factors could be abrupt on the global scale, because the hysteretic responses of vegetation to fire and vegetation types are discrete. This study examined the climatic, vegetation, anthropogenic, lightning, and topographic factorsdriving variations in global fire density, and discussedthe thresholds of vegetation on fire activity. Fire density was developed from 7 years of Moderate Resolution Imaging Spectroradiometer (MODIS) active fire data to represent global fire activity, and nine typical influencing variables were selected. The random forest regression tree method was used to identify the relative importance and relationships between fire and the influencing variables. The patterns of global fire density were captured well by the model (78.33% variance was explained), and the related thresholds were identified. Climatic factors played a primary role in determining global fire density. Agricultural land use and topographic roughness were not identified as the most important factors, probably due to the large scale we considered. Three intervals of tree density were identified to have distinct levels of fire density. Intermediate tree density (9%-53%) was related with the highest fire density, but both low and high percent of tree cover were associated with low fire density (7.0 vs. 1.3/0.9 counts per 100 km 2 per year). This study could provide further insights into understanding of the threshold effects of influencing factors on fire activity, and contribute to advances in fire modelingand vegetation distribution studies

Delimiting Urban Growth Boundary through Combining Land Suitability Evaluation and Cellular Automata

China’s domestic urban planning only worked on researches of urban space control, the scope definition of urban development is not clear enough. The purpose of this study is to present a new urban growth boundary (UGB) delimitation method which combined land suitability evaluation (LSE) and cellular automata (CA). This method gave credence to LSE’s advantage in sustainable land use, and CA’s advantage in objective dynamic simulation. The ecological limitation areas were defined by LSE, which were regarded as the restricted areas of urban growth; meanwhile, it was taken as an important model input to guide intensive land allocation in urban growth model (CA model). The future urban growth scenarios were predicted by CA model and the corresponding UGB lines were delineated by ArcGIS 10.1. The results indicated that this method had good performance in Ningbo’s urban growth simulation. When compared to the planned UGB in urban master planning, the simulated UGBs under port development and regulated scenarios showed more intensive and suitable spatial layout of land. Besides, the simulated UGB under regulated scenario had the most reasonable space structure and the largest ecological protection effect among the UGBs. Hence, the simulated UGBs were superior to the planned UGB. The study recommends that this UGB delimitation method can promote sustainability of land development and ecological environment in Chinese cities

RGB color composite using vegetation fraction maps of 1990 (R), 2002 (G) and 2010 (B).

<p>The typical examples demonstrated are (a) the study area, (b) Xixi National Wetland Park, (c) the urban center, (d) residential communities and (e) the Xiasha suburban college town. Colors for the typical compositions of the vegetation fractions on the three dates are illustrated. H represents high vegetation fraction and L represents low vegetation fraction.</p

Exploring Spatial Network Structure of the Metropolitan Circle Based on Multi-Source Big Data: A Case Study of Hangzhou Metropolitan Circle

The metropolitan circle is the basic unit of regional competition. Enhancing the connection between cities in the metropolitan circle and optimizing the spatial layout of the metropolitan circle is one of the goals of regional high-quality development in the new era. Therefore, it is of great significance to analyze the spatial network structure of the metropolitan circle. Taking Hangzhou metropolitan circle as an example, this study used web crawler technology to obtain data in multiple Internet big data platforms; used centrality analysis, flow data model, and social network analysis to construct the network connection matrix of human flow, goods flow, capital flow, information flow, and traffic flow; and explored the spatial network structure of the metropolitan circle. The results showed that the node intensity of the metropolitan circle presented a distribution pattern of strong in the east and weak in the west. The network connections of each county under the action of different element flows were different, and the skeleton of the integrated flow network connections showed a starfish-shaped feature. Hangzhou, Jiaxing, Huzhou, and Shaoxing cities had strong group effects in goods flow and traffic flow, while Quzhou and Huangshan cities had relatively independent cohesive subgroups in human flow and information flow. This study can provide useful references for regional development and spatial planning implementation

Concentric vegetation coverage analysis in each belt for each year.

<p>(a) Average vegetation fraction (AVF), (b) percentage of area of high coverage pixels, (c) percentage of area of middle-high coverage pixels, (d) percentage of area of middle coverage pixels, and (e) percentage of area of low coverage pixels.</p

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems

Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources

Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications

Investigating the Spatial Heterogeneity and Influencing Factors of Urban Multi-Dimensional Network Using Multi-Source Big Data in Hangzhou Metropolitan Circle, Eastern China

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, and information flow information among sub-cities. Then, spatial visualization analysis, social network analysis, and geographical detector were applied to analyze the differences in spatial structure of multiple urban networks and influencing factors in Hangzhou metropolitan circle, respectively. The results showed that (1) the network connections of population, traffic, goods, and capital flows transcended geographical proximity except that of information flow, and population and traffic flow networks were found to be more flattened in Hangzhou metropolitan circle than in other urban networks; (2) the comprehensive urban network of Hangzhou metropolitan circle was imbalanced across sub-cities, presenting hierarchical and unipolar characteristics; and (3) the influence of traffic distance on the network spatial structure of Hangzhou metropolitan was stronger than the geographical distance, and the interactions between traffic distance and socioeconomic factors would further enhance the regional differentiation of the network spatial structure. This study could provide scientific reference for constructing a coordinated and integrated development pattern in a metropolitan circle