Spatio-temporal distribution and classification of utilization of urban bare lots in low-slope hilly regions

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.

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CLASSIFICATION OF MULTITEMPORAL REMOTE-SENSING IMAGES BY A FUZZY FUSION OF SPECTRAL AND SPATIO-TEMPORAL CONTEXTUAL INFORMATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003083 ◽

2004 ◽

Vol 18 (02) ◽

pp. 143-156 ◽

Cited By ~ 12

Author(s):

FARID MELGANI

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Multilayer Perceptron ◽

Contextual Information ◽

Fuzzy Reasoning ◽

Remote Sensing Images ◽

Single Time ◽

Spatio Temporal ◽

Fuzzy Fusion

A fuzzy-logic approach to the classification of multitemporal, multisensor remote-sensing images is proposed. The approach is based on a fuzzy fusion of three basic sources of information: spectral, spatial and temporal contextual information sources. It aims at improving the accuracy over that of single-time noncontextual classification. Single-time class posterior probabilities, which are used to represent spectral information, are estimated by Multilayer Perceptron neural networks trained for each single-time image, thus making the approach applicable to multisensor data. Both the spatial and temporal kinds of contextual information are derived from the single-time classification maps obtained by the neural networks. The expert's knowledge of possible transitions between classes at two different times is exploited to extract temporal contextual information. The three kinds of information are then fuzzified in order to apply a fuzzy reasoning rule for their fusion. Fuzzy reasoning is based on the "MAX" fuzzy operator and on information about class prior probabilities. Finally, the class with the largest fuzzy output value is selected for each pixel in order to provide the final classification map. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are reported. The accuracy of the proposed fuzzy spatio-temporal contextual classifier is compared with those obtained by the Multilayer Perceptron neural networks and a reference classification approach based on Markov Random Fields (MRFs). Results show the benefit of adding spatio-temporal contextual information to the classification scheme, and suggest that the proposed approach represents an interesting alternative to the MRF-based approach, in particular, in terms of simplicity.

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A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images

Pattern Recognition Letters ◽

10.1016/s0167-8655(02)00052-1 ◽

2002 ◽

Vol 23 (9) ◽

pp. 1053-1061 ◽

Cited By ~ 47

Author(s):

Farid Melgani ◽

Sebastiano B. Serpico

Keyword(s):

Remote Sensing ◽

Statistical Approach ◽

Contextual Information ◽

Remote Sensing Images ◽

Spatio Temporal

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Analysis of Spatial and Temporal Distribution Characteristics of Land Desertification Based on GIS and Remote Sensing Images

Scientific Programming ◽

10.1155/2021/7557175 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Li Cui

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Source Region ◽

Temporal Distribution ◽

Spatial And Temporal Distribution ◽

Distribution Characteristics ◽

Remote Sensing Images ◽

Gis And Remote Sensing ◽

Average Value ◽

Land Desertification

Due to the complex geographical situation in China, in this paper, the area of land desertification is 98.5% of the total land desertification area in China. Based on the measured data of GIS and remote sensing images, we will discuss the spatial and temporal distribution characteristics of land desertification in China by calculating standardized precipitation evapotranspiration index (SPEI) and normalized vegetation index and establishing the CA model. The results show the following. (1) The trend of desertification in China has decreased as a whole, and the percentage of nondesertification has increased from 36.91% in 1991 to 44.46% in 2020, an increase of 7.55%. Extremely severe desertification increased from 21.72% to 24.25%, an increase of 2.53%. (2) The drought situation in the study area gradually improved, and the change trend of SPEI decreased by 74%. (3) In recent ten years (2011–2020), the vegetation grew well gradually, and it was in the best state in 2018. The NDVI index value increased by 5.9% compared with the average value in this decade. (4) The model designed by us works very well, and the results of simulating and testing the Three Rivers Source region are very little different from the actual situation, which meets our research requirements.

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