THE IMPACTS OF NEIGHBORHOOD LAND USE PATTERNS ON RESIDENT'S SATISFACTION AND PERCEPTION TO SUSTAINABLE URBAN DEVELOPMENT: A COMPARISON OF FOUR NEIGHBORHOODS IN GREATER CAIRO REGION EGYPT

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

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

Journal of Environmental Systems ◽

10.2190/6451-t2jn-0n11-r0j1 ◽

1996 ◽

Vol 25 (2) ◽

pp. 195-211 ◽

Cited By ~ 9

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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Conducting “Siteless Survey”: Results from Coastal New England

North American Archaeologist ◽

10.2190/rxce-py1q-pr1e-93m5 ◽

1993 ◽

Vol 14 (1) ◽

pp. 25-42 ◽

Cited By ~ 1

Author(s):

Jordan E. Kerber

Keyword(s):

Land Use ◽

New England ◽

Rhode Island ◽

Careful Consideration ◽

Archaeological Survey ◽

Land Use Patterns ◽

Use Patterns ◽

Statistical Validity ◽

Survey Results ◽

Survey Strategy

Selecting an effective archaeological survey takes careful consideration given the interaction of several variables, such as the survey's goals, nature of the data base, and budget constraints. This article provides justification for a “siteless survey” using evidence from a project on Potowomut Neck in Rhode Island whose objective was not to locate sites but to examine the distribution and density of prehistoric remains to test an hypothesis related to land use patterns. The survey strategy, random walk, was chosen because it possessed the advantages of probabilistic testing, as well as the ease of locating sample units. The results were within the limits of statistical validity and were found unable to reject the hypothesis. “Siteless survey” may be successfully applied in similar contexts where the distribution and density of materials, as opposed to ambiguously defined sites, are sought as evidence of land use patterns, in particular, and human adaptation, in general.

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The effects of different land use patterns on the microclimate and ecosystem services in the agro-pastoral ecotone of Northern China

Ecological Indicators ◽

10.1016/j.ecolind.2019.105522 ◽

2019 ◽

Vol 106 ◽

pp. 105522 ◽

Cited By ~ 3

Author(s):

Yuejuan Yang ◽

Kun Wang

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Northern China ◽

Land Use Patterns ◽

Use Patterns

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An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI

Remote Sensing ◽

10.3390/rs13040766 ◽

2021 ◽

Vol 13 (4) ◽

pp. 766

Author(s):

Yuanmao Zheng ◽

Qiang Zhou ◽

Yuanrong He ◽

Cuiping Wang ◽

Xiaorong Wang ◽

...

Keyword(s):

Urban Development ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Development Planning ◽

Urban Land ◽

Sustainable Urban Development ◽

Land Use Patterns ◽

Water Index ◽

Nighttime Light

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

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

Remote Sensing ◽

10.3390/rs13040631 ◽

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.

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