Next Article in Journal
Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies
Next Article in Special Issue
Examining the Roles of Spectral, Spatial, and Topographic Features in Improving Land-Cover and Forest Classifications in a Subtropical Region
Previous Article in Journal
Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter
Previous Article in Special Issue
A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery
 
 
Article

Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors

1
SoftwareMill, 02-791 Warsaw, Poland
2
Faculty of Earth Sciences and Spatial Management, Nicolaus Copernicus University in Toruń, Lwowska 1, 87-100 Toruń, Poland
3
Faculty of Geographical Sciences, University of Lodz, Kopcińskiego31, 90-142 Łódź, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2628; https://doi.org/10.3390/rs12162628
Received: 30 June 2020 / Revised: 11 August 2020 / Accepted: 12 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Multi-Modality Data Classification: Algorithms and Applications)
This paper investigates the social, demographic, and economic factors determining differences between forest identification based on remote sensing techniques and land registry. The Database of Topographic Objects and Sentinel-2 satellite imagery data from 2018 were used to train a forest detection supervised machine learning model. Results aggregated to communes (NUTS-5 units) were compared to data from land registry delivered in Local Data Bank by Statistics Poland. The differences identified between above mentioned sources were defined as errors of land registry. Then, geographically weighted regression was applied to explain spatially varying impact of investigated errors’ determinants: Urbanization processes, civic society development, education, land ownership, and culture and quality of spatial planning. The research area covers the entirety of Poland. It was confirmed that in less developed areas, local development policy stimulating urbanization processes does not respect land use planning principles, including the accuracy of land registry. A high education level of the society leads to protective measures before the further increase of the investigated forest cover’s overestimation of the land registry in substantially urbanized areas. Finally, higher coverage by valid local spatial development plans stimulate protection against forest classification errors in the land registry. View Full-Text
Keywords: land cover; forest; convolutional neural networks; machine learning; land registry errors land cover; forest; convolutional neural networks; machine learning; land registry errors
Show Figures

Graphical abstract

MDPI and ACS Style

Adamiak, M.; Biczkowski, M.; Leśniewska-Napierała, K.; Nalej, M.; Napierała, T. Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors. Remote Sens. 2020, 12, 2628. https://doi.org/10.3390/rs12162628

AMA Style

Adamiak M, Biczkowski M, Leśniewska-Napierała K, Nalej M, Napierała T. Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors. Remote Sensing. 2020; 12(16):2628. https://doi.org/10.3390/rs12162628

Chicago/Turabian Style

Adamiak, Maciej, Mirosław Biczkowski, Katarzyna Leśniewska-Napierała, Marta Nalej, and Tomasz Napierała. 2020. "Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors" Remote Sensing 12, no. 16: 2628. https://doi.org/10.3390/rs12162628

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop