scholarly journals Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data

2021 ◽  
Vol 13 (18) ◽  
pp. 3666
Author(s):  
Jiaqi Shen ◽  
Yanmin Shuai ◽  
Peixian Li ◽  
Yuxi Cao ◽  
Xianwei Ma
Keyword(s):  
Land Use ◽  
Large Scale ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Scientific Basis ◽  
Physical Geography ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Landsat Images ◽  
Intensity Index

It is necessary to understand the relationship between the impervious surface area (ISA) distribution, variation trends and potential driving forces over Dongying, Shandong Province. We extracted ISA information from Landsat images with 3–5 year intervals during 1995 to 2018 using Minimum Noise Fraction (MNF) transform, Pixel Purity Index (PPI), and Linear Spectral Mixture Analysis (LSMA), followed by the analysis on three driving forces of ISA expansion (physical geography, socioeconomic factors, and urban cultural features). Our results show the retrieved ISA thematic map fit the limited requirement of root mean square error (RMSE). The correct classification accuracy of ISA is greater than 83.08%. Further, the cross–comparison exhibits the general consistent with the ISA distribution of the land use classification map published by the National Basic Geographic Information Center. The gradual increasing trend can be captured on the expansion of ISA from 1995 to 2018. Despite of the central region always shown as the high ISA density, it still keeps increasing annually and radiating the surrounding region, especially in the southward which has formed into a new large–scale and high intensity of ISA in 2015–2018. Though the ISA patches scattered in the west region or along the northern and eastern part of the ocean coastline are still small, the expansion trend of ISA can be detected. The expansion intensity index (EII) of ISA measuring the situation of its expansion changes from the lowest value 0.12% between 1995 and 2000 up to the highest 0.73% between 2000 and 2005. Richly endowed by nature, the city’s natural geographical environment provides an elevated chance of further urbanization. The rapid increase of regional economy provides a fundamental driving force for expanding ISAs. The development of urban culture promotes the sustainable development of ISAs. Our results provide a scientific basis for future urban land use management, construction planning, and environmental protection in Dongying.

scholarly journals LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 17-21
Author(s):  
J. R. Bergado ◽  
C. Persello ◽  
A. Stein
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Land ◽  
Urban Land Use ◽  
The Other ◽  
Classification Task ◽  
Land Use Classification ◽  
Convolutional Network

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.

scholarly journals Land use interpretation for cellular automata models with socioeconomic heterogeneity

2011 ◽  
Vol 11 (4) ◽  
pp. 167-183
Author(s):  
Bernardo Alves Furtado
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Space ◽  
Large Scale ◽  
Empirical Test ◽  
Urban Land Use ◽  
City Centre ◽  
Land Use Classification ◽  
Belo Horizonte ◽  
Different Levels

Cellular automata models for simulation of urban development usually lack the social heterogeneity that is typical of urban environments. In order to handle this shortcoming, this paper proposes the use of supervised clustering analysis to provide socioeconomic intra-urban land use classification at different levels to be applied to cellular automata models. An empirical test in a highly diverse context in the Greater Metropolitan Area of Belo Horizonte (RMBH) in Brazil is provided. The results show that a reliable division into different socioeconomic land-use classes at large scale enable detailed urban dynamic analysis. Furthermore, the results also allow the quantification of the proportion of urban space occupation for different levels of income; (2) and their pattern in relation to the city centre.

scholarly journals Land Use Transition and Driving Forces in Chinese Loess Plateau: A Case Study from Pu County, Shanxi Province

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 67
Author(s):  
Han Huang ◽  
Yang Zhou ◽  
Mingjie Qian ◽  
Zhaoqi Zeng
Keyword(s):  
Land Use ◽  
Food Security ◽  
Loess Plateau ◽  
Large Scale ◽  
Driving Forces ◽  
Chinese Loess Plateau ◽  
Shanxi Province ◽  
Forest Land ◽  
Land Use Transition ◽  
Macro Scale

Land use transition is essentially one of the manifestations of land use/cover change (LUCC). Although a large number of studies have focused on land use transitions on the macro scale, there are few studies on the micro scale. Based on the data of two high-resolution land use surveys, this study used a land use transfer matrix and GeoDetector model to explore the spatial-temporal patterns and driving forces of land use transitions at the village level in Pu County over a ten-year period. Results show that Pu County has experienced a drastic process of land use transition. More than 80% of cropland and grassland have been converted to forest land, and over 90% of the expansion of built-up land came from the occupation of forest land, cropland, and grassland. The driving forces of land use transition and its magnitude depended on the type of land use. The implementation of the policy of returning farmland to forest, or grain-for-green (GFG) was the main driving force for the large-scale conversion of cultivated land to forest land in Pu County. In the context of policy of returning farmland to forests, the hilly and gully regions of China’s Loess Plateau must balance between protecting the ecology and ensuring food security. Promoting the comprehensive consolidation of gully land and developing modern agriculture may be an important way to achieve a win-win goal of ecological protection and food security.

scholarly journals Mapping Urban Land Use by Using Landsat Images and Open Social Data

2016 ◽  
Vol 8 (2) ◽  
pp. 151 ◽  
Author(s):  
Tengyun Hu ◽  
Jun Yang ◽  
Xuecao Li ◽  
Peng Gong
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat Images ◽  
Social Data

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals Urban Land-Use Dynamics in the Niger Delta: The Case of Greater Port Harcourt Watershed

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 108 ◽  
Author(s):  
Nimi Dan-Jumbo ◽  
Marc Metzger ◽  
Andrew Clark
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Niger Delta ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Hard Copy ◽  
Land Use Classification ◽  
Depth Analysis

Cities in developing countries are urbanising at a rapid rate, resulting in substantial pressures on environmental systems. Among the main factors that lead to flooding, controlling land-use change offers the greatest scope for the management of risk. However, traditional analysis of a “from–to” change matrix is not adequate to provide information of all the land-use changes that occur in a watershed. In this study, an in-depth analysis of land-use change enabled us to quantify the bulk of the changes accumulating from swap changes in a tropical watershed. This study assessed the historical and future land-use/land-cover (LULC) dynamics in the River State region of the Niger Delta. Land-use classification and change detection analysis was conducted using multi-source (Landsat TM, ETM, polygon map, and hard copy) data of the study area for 1986, 1995, and 2003, and projected conditions in 2060. The key findings indicate that historical urbanisation was rapid; urban expansion could increase by 80% in 2060 due to planned urban development; and 95% of the conversions to urban land occurred chiefly at the expense of agricultural land. Urban land was dominated by net changes rather than swap changes, which in the future could amplify flood risk and have other severe implications for the watershed.

Sign in / Sign up

Export Citation Format

Share Document