Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data

2017 ◽  
Vol 10 (6) ◽  
pp. 2674-2684 ◽  
Author(s):  
Weichao Sun ◽  
Xia Zhang ◽  
Nan Wang ◽  
Yi Cen
Keyword(s):  
Population Density ◽  
Land Cover ◽  
Night Time ◽  
Land Cover Data

scholarly journals Spatial simulation of population in Shandong Province based on night-time imagery and land cover data

2021 ◽  
Vol 293 ◽  
pp. 02015
Author(s):  
Keyi Yang ◽  
Yunling Li ◽  
Yang Liu
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Stepwise Regression ◽  
Shandong Province ◽  
Good Precision ◽  
Night Time ◽  
Actual Distribution ◽  
Land Cover Data ◽  
Public Datasets ◽  
Better Than

Population spatial data can more truly express the actual distribution characteristics of the population, and provide data support for the regional environment and population development. Use Shandong Province as the research area, township-level census data, revised DMSP/OLS night-time data, and Globaland30 land cover data as data sources, partitions based on population agglomeration, and uses a stepwise regression method to build a population data spatial model. Use the model to simulate population density with a resolution of 100m. The experimental results show: Stepwise regression model good precision, the average relative error was 23.56%, and Root Mean Square Error, Mean Absolute Error are better than the other two public datasets. The simulation results are better than the two public datasets.

Modeling population density using land cover data

2005 ◽  
Vol 189 (1-2) ◽  
pp. 72-88 ◽  
Author(s):  
Yongzhong Tian ◽  
Tianxiang Yue ◽  
Lifen Zhu ◽  
Nicholas Clinton
Keyword(s):  
Population Density ◽  
Land Cover ◽  
Land Cover Data

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

Changing weather and climate in Northern Ghana: comparison of local perceptions with meteorological and land cover data

2016 ◽  
Vol 17 (3) ◽  
pp. 915-928 ◽  
Author(s):  
Katherine L. Dickinson ◽  
Andrew J. Monaghan ◽  
Isaac J. Rivera ◽  
Leiqiu Hu ◽  
Ernest Kanyomse ◽  
...  
Keyword(s):  
Land Cover ◽  
Northern Ghana ◽  
Local Perceptions ◽  
Land Cover Data ◽  
Weather And Climate
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