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

2019 ◽  
Vol 106 ◽  
pp. 105522 ◽  
Author(s):  
Yuejuan Yang ◽  
Kun Wang
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Northern China ◽  
Land Use Patterns ◽  
Use Patterns

scholarly journals Dynamic Simulation of Land Use Change of the Upper and Middle Streams of the Luan River, Northern China

2019 ◽  
Vol 11 (18) ◽  
pp. 4909 ◽  
Author(s):  
Xia Xu ◽  
Mengxi Guan ◽  
Honglei Jiang ◽  
Lingfei Wang
Keyword(s):  
Land Use ◽  
River Basin ◽  
Land Use Planning ◽  
Land Use Changes ◽  
Northern China ◽  
Transitional Zone ◽  
Sustainable Land Use ◽  
Land Use Patterns ◽  
Reference Case ◽  
Use Patterns

Climatic, socio-economic, geophysical, and human activity factors, among others, influence land use patterns. However, these driving factors also have different relationships with each other. Combining machine learning methods and statistical models is a good way to simulate the dominant land use types. The Luan River basin is located in a farming-pastoral transitional zone and is an important ecological barrier between Beijing and Tianjin. In this study, we predicted future land use and land cover changes from 2010 to 2020 in the Luan River’s upper and middle reaches under three scenarios—the natural scenario, the ecological scenario, and the sustainable scenario. The results indicate that cultivated land will decrease while the forested areas will increase quantitatively in the future. Built-up areas would increase quickly in the natural scenario, and augmented expansion of forest would be the main features of land use changes in both the ecological scenario and the sustainable scenario. Regarding the spatial pattern, different land use patterns will be aggregated and patches will become larger. Our findings for the scenario analysis of land use changes can provide a reference case for sustainable land use planning and management in the upper and middle Luan River basin.

Ecological soil C, N, and P stoichiometry of different land use patterns in the agriculture-pasture ecotone of Northern China

2019 ◽  
Vol 39 (15) ◽  
Author(s):  
高君亮 GAO Junliang ◽  
罗凤敏 LUO Fengmin ◽  
高永 GAO Yong ◽  
党晓宏 DANG Xiaohong ◽  
蒙仲举 MENG Zhongju ◽  
...  
Keyword(s):  
Land Use ◽  
Northern China ◽  
Land Use Patterns ◽  
Soil C ◽  
Use Patterns

Conducting “Siteless Survey”: Results from Coastal New England

1993 ◽  
Vol 14 (1) ◽  
pp. 25-42 ◽  
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.

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.

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