Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS

2004 ◽  
Vol 69 (4) ◽  
pp. 335-354 ◽  
Author(s):  
Xia Li ◽  
Anthony Gar-On Yeh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing And Gis ◽  
Land Use Patterns ◽  
Fast Growing ◽  
Use Patterns ◽  
Spatial Restructuring ◽  
Growing Region

scholarly journals Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

2021 ◽  
Vol 13 (4) ◽  
pp. 631
Author(s):  
Kyle D. Woodward ◽  
Narcisa G. Pricope ◽  
Forrest R. Stevens ◽  
Andrea E. Gaughan ◽  
Nicholas E. Kolarik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Resource Use ◽  
Training Data ◽  
Systems Science ◽  
Participatory Mapping ◽  
Land Use Patterns ◽  
Modeling Framework ◽  
Use Patterns ◽  
Human Habitation

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.

scholarly journals Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1093
Author(s):  
Nguyen Thanh Giao ◽  
Nguyen Van Cong ◽  
Huynh Thi Hong Nhien
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Rice Cultivation ◽  
Land Use Patterns ◽  
Land Use Pattern ◽  
Perennial Crops ◽  
Use Patterns

This study was carried out to understand how land use patterns influence surface water quality in Tien Giang Province using remote sensing and statistical approaches. Surface water quality data were collected at 34 locations with the frequency of four times (March, June, September, and November) in 2019. Water quality parameters were used in the analysis, including pH, temperature, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2−), nitrate (N-NO3−), sulfate (SO42−), orthophosphate (P-PO43−), chloride (Cl−), total nitrogen (TN), total phosphorus (TP), and coliform. The relationship between land use patterns and water quality was analyzed using geographic information techniques (GIS), remote sensing (RS), statistical approaches (cluster analysis (CA), principal component analysis (PCA), and Krustal–Wallis), and weighted entropy. The results showed water quality was impaired by total suspended solids, nutrients (N-NH4+, N-NO2−, P-PO43−), organic matters (BOD, COD), and ions (Cl− and SO42−). Kruskal–Wallis analysis results showed that all water quality parameters in the water bodies in Tien Giang Province were seasonally fluctuated, except for BOD and TN. The highest levels of water pollutants were found mostly in the dry season (March and June). The majority of the land in the study area was used for rice cultivation (40.64%) and residential (27.51%). Water quality in the study area was classified into nine groups corresponding to five combined land use patterns comprising residential–aquaculture, residential–rice cultivation, residential–perennials, residential–rice–perennial, and residential–rice–perennial crops–aquacultural. The concentrations of the water pollutants (TSS, DO, BOD, COD, N-NH4+, N-NO2−, Cl−, and coliform) in the locations with aquaculture land use patterns (Clusters 1 and 2) were significantly larger than those of the remaining land use patterns. PCA analysis presented that most of the current water quality monitoring parameters had a great impact on water quality in the water bodies. The entropy weight showed that TSS, N-NO2−, and coliform are the most important water quality parameters due to residential–aquaculture and residential–rice cultivation; EC, DO, N-NH4+, N-NO2−, Cl−, and coliform were the significant variables for the land use type of residential–perennial crops; N-NO2−, P-PO43−, and coliform for the land use pattern of residential–rice cultivation–perennial crops) and N-NH4+, N-NO2−, Cl−, and coliform for the land use pattern of residential–rice cultivation–perennial crops–aquaculture. The current findings showed that that surface water quality has been influenced by the complex land use patterns in which residential and rice cultivation may have major roles in causing water impairment. The results of the water quality assessment and the variation in water properties of the land use patterns found in this study provide scientific evidence for future water quality management.

scholarly journals Sensoriamento Remoto e Estudos de Clima Urbano

2021 ◽  
Vol 25 (2) ◽  
pp. 195-199
Author(s):  
Mariane Batista de Lima Moraes Brandão Campos ◽  
Victor Hugo de Morais Danelichen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Air Temperature ◽  
Urban Climate ◽  
Remote Sensing Data ◽  
Urban Land Use ◽  
Air Humidity ◽  
Land Use Patterns ◽  
Sparse Vegetation ◽  
Use Patterns

Em regiões onde a rede meteorológica não apresenta uma cobertura satisfatória o uso do sensoriamento remoto se apresenta como uma técnica eficaz para estudo ambiental, possibilitando examinar a métrica dos padrões de uso do solo, bem como análises espaciais e temporais do clima nas cidades. Dessa forma, este trabalho busca analisar a produção científica sobre as técnicas de predição de temperatura e umidade do ar a partir de dados de sensoriamento remoto. Foi realizada uma análise bibliográfica e documental em consultas à base de pesquisa da Scopus (Elseivier) com a seleção final de 15 artigos que possuem relação direta com o conteúdo da pesquisa e enfatizam o estudo de clima urbano pelos padrões de uso do solo urbano, analisando-se dados obtidos por meio de sensores orbitais de satélites como os da série Landsat. O resultado da pesquisa aponta para o uso da vegetação como importante recurso de mitigação dos efeitos negativos do clima na cidade. Modelos matemáticos estão sendo aprimorados para obtenção de temperatura do ar com base em dados de temperatura de superfície, uma vez que ambas variáveis possuem uma forte correlação. Os índices espectrais NDMI e NDWI são úteis na verificação de dados sobre umidade do ar, porém pouco consistentes em locais de vegetação nula ou esparsa. Identificou-se o sensoriamento remoto como ferramenta promissora inclusive na análise de mesoclimas e microclimas urbanos, sendo importante a continuidade de pesquisas e estudos que identifiquem suas potencialidades. Palavras-chave: Umidade do Ar. Sensores Orbitais. Temperatura do Ar. Abstract In regions where the meteorological network does not have a satisfactory coverage or the use of remote sensing, it presents itself as an effective technique for environmental studies, allowing the metrics evaluation of land use patterns as well as spatial and temporal analyzes of the climate in cities. In this way, this research aims to analyze the scientific production on the temperature and humidity techniques prediction from remote sensing data. A bibliographic and documentary analysis was carried out in consultation with the Scopus (Elseivier) research base with a final selection of 15 scientific articles that directly relate to the research content and emphasize the study of urban climate by urban land use patterns, analyzing data obtained using satellite orbital sensors such as the Landsat series. The result of the research points to the use of vegetation as an important resource to mitigate the negative effects of climate in the city. Mathematical models are being improved to obtains air temperature based on surface temperature data, since both variables have a strong correlation. The NDMI and NDWI spectral indices are useful in verifying data on air humidity, but they are not very consistent in areas of null or sparse vegetation. Remote sensing has been identified as a promising tool, including in the analysis of urban mesoclimates and microclimates, and it is important to continue research and studies that identify its potential. Keywords: Air Humidity. Orbital Sensors. Air Temperature.

scholarly journals SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 981-988 ◽  
Author(s):  
Y. Yao ◽  
H. Liang ◽  
X. Li ◽  
J. Zhang ◽  
J. He
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Scale Effect ◽  
Urban Land ◽  
Urban Land Use ◽  
Data Sets ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Multi Scale

With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Changing Traditional Land use Patterns in the Great Himalayas: A Case Study of Lahaul Valley

1996 ◽  
Vol 25 (2) ◽  
pp. 195-211 ◽  
Author(s):  
G. S. Singh ◽  
S. C. Ram ◽  
J. C. Kuniyal
Keyword(s):  
Land Use ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Traditional Land Use
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