scholarly journals Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

scholarly journals Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1910 ◽  
Author(s):  
Miao He ◽  
Yongming Xu ◽  
Ning Li
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Census Data ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Google Earth ◽  
Population Census ◽  
Beijing City ◽  
Night Time ◽  
Sensing Data

Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.

scholarly journals Review of methods and approaches of abandoned lands identification

2020 ◽  
Vol 224 ◽  
pp. 04004
Author(s):  
E Barbotkina ◽  
Ie Dunaieva ◽  
V Popovych ◽  
V Pashtetsky
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Arable Land ◽  
Remote Sensing Data ◽  
The Republic ◽  
Using Data ◽  
Science Service ◽  
Abandoned Lands ◽  
Phenological Phases

The paper describes the causes of abandonment of agricultural land in various regions of the world, as well as in Russia and the Republic of Crimea, and explores the existing methods and approaches for identifying such lands using data from remote sensing of the Earth. To identify abandoned lands in Crimea, the MODIS time series analysis method of remote sensing data was used together with ground data on the location of arable land using Vega-Science service. The territory of Krasnogvardeisky and Belogorsky municipal districts of the Republic of Crimea was chosen; for which a vector mask of agricultural lands was created for them. When identifying unused lands to verify the classification and minimize errors, it is necessary to have data on the phenological phases of vegetation and field data on the structure of crops; use identification methods in a complex (expert, automatic classification with various approaches: random forest, vegetation index, arable land, etc.).

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Generating population census data through aerial remote sensing

1994 ◽  
Vol 22 (3) ◽  
pp. 131-138 ◽  
Author(s):  
R. C. S. Taragi ◽  
K. S. Bisht ◽  
B. S. Sokhi
Keyword(s):  
Remote Sensing ◽  
Census Data ◽  
Population Census ◽  
Aerial Remote Sensing

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

<p>Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.</p><p>Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.</p><p>To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.</p>

scholarly journals Assessing migration patterns in Passerina ciris using the world’s bird collections as an aggregated resource

2015 ◽  
Author(s):  
Ethan Linck ◽  
Eli S Bridge ◽  
Jonah M Duckles ◽  
Adolfo G Navarro-Sigüenza ◽  
Sievert Rohwer
Keyword(s):  
Remote Sensing ◽  
Behavioral Ecology ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Abundance Index ◽  
Population Movements ◽  
Relative Population ◽  
History Museum ◽  
Abundance Indices ◽  
Passerina Ciris

Natural history museum collections (NHCs) represent a rich and largely untapped source of data on demography and population movements. NHC specimen records can be corrected to a crude measure of collecting effort and reflect relative population densities with a method known as abundance indices. We plot abundance index values from georeferenced NHC data in a 12-month series for the new world migratory passerine Passerina ciris across its molting and wintering range in Mexico and Central America. We illustrate a statistically significant change in abundance index values across regions and months that suggests a quasi-circular movement around its non-breeding range, and use enhanced vegetation index (EVI) analysis of remote sensing plots to demonstrate non-random association of specimen record density with areas of high primary productivity. We demonstrate how abundance indices from NHC specimen records can be applied to infer previously unknown migratory behavior, and be integrated with remote sensing data to allow for a deeper understanding of demography and behavioral ecology across space and time.

scholarly journals Inherent Reflectance Variability of Vegetation

Agriculture ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 246 ◽  
Author(s):  
Baabak Mamaghani ◽  
M. Grady Saunders ◽  
Carl Salvaggio
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Time Of Day ◽  
Unmanned Aircraft ◽  
P Value ◽  
Ground Sample ◽  
Significant Difference

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

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