scholarly journals Land Cover Classification and Forest Change Analysis, Using Satellite Imagery-A Case Study in Dehdez Area of Zagros Mountain in Iran

2011 ◽  
Vol 03 (01) ◽  
pp. 1-11 ◽  
Author(s):  
Ali Asghar Torahi ◽  
Suresh Chand Rai
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Land Cover Classification ◽  
Forest Change ◽  
Change Analysis ◽  
Zagros Mountain

Land cover classification from satellite imagery, and its applications in cellular network planning

2005 ◽  
Author(s):  
◽  
Heng Huang
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Cellular Network ◽  
Landsat Imagery ◽  
Land Cover Classification ◽  
Landsat Data ◽  
Change Analysis ◽  
Accuracy Improvement ◽  
Processing Techniques

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] To further increase the classification accuracies, radar image processing techniques were investigated to preprocess the Radarsat data before classification. Eight processing techniques were applied to Radarsat data at various windows from 3 x 3 to 25 x 25 pixels. For a single radar feature, the Entropy processing at window size 13 x 13 provides the best overall land cover classification accuracy improvement when fused with the Landsat imagery. For multiple radar features, a higher accuracy improvement was found when combining the features (i.e., 13 x 13 Entropy, 9 x 9 data range, 19 x 19 mean) with the Landsat data. This study introduces an approach of fusing Landsat data with multiple Radarsat features to the land cover classification practice. Post-classification techniques were studied for land cover classification maps. Several weighted kernels were developed for the majority filtering process. The method evaluates the correlation between neighbor pixels according to the distance and further improves the classification accuracy. For the St. Louis study area, the Gaussian weighted kernel increases the overall land classification accuracy compared to the Landsat images. Post-classification smoothing of the sensor fusion result (Landsat and radar feature combination) further increases the accuracy. A decadal change analysis was also conducted for the St. Louis, Missouri area using Landsat imagery and census population data. This study proposes a methodology to integrate remotely sensed and census data in urban change analysis. The assessment can provide information that can highlight priority urban growth regions. The analysis shows strong correlation between population and land cover changes, which indicates the potential of satellite imagery to generate the physical feature input for tele-traffic forecasting of a cellular network.

scholarly journals Using machine learning to produce a very high-resolution land-cover map for Ireland and beyond

2021 ◽  
Author(s):  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Eoin Walsh
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Weather Forecasting ◽  
Land Cover Classification ◽  
Surface Processes ◽  
Land Cover Map ◽  
Very High

<p>An accurate representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. The Numerical Weather Prediction (NWP) system, to represent these exchanges, requires a land-cover classification map to calculate the surface parameters used in the turbulent, radiative, heat, and moisture fluxes estimations.</p><p>The land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting is ECOCLIMAP. ECOCLIMAP-SG (ECO-SG), the latest version of ECOCLIMAP, was evaluated over Ireland to prepare ECO-SG implementation in HARMONIE-AROME. This evaluation suggested that sparse urban areas are underestimated and instead appear as vegetation areas in ECO-SG [1], with an over-classification of grassland in place of sparse urban areas and other vegetation covers (Met Éireann internal communication). Some limitations in the performance of the current HARMONIE-AROME configuration attributed to surface processes and physiography issues are well-known [2]. This motivated work at Met Éireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.</p><p>In terms of accuracy, resolution, and the future production of time-varying land-cover map, the use of a convolutional neural network (CNN) to create a land-cover map using Sentinel-2 satellite imagery [3] over Estonia [4] presented better potential outcomes than the use of local datasets [5]. Consequently, this method was tested over Ireland and proven to be more accurate than ECO-SG for representing CORINE Primary and Secondary labels and at a higher resolution [5]. This work is a continuity of [5] focusing on 1. increasing the number of labels, 2. optimising the training procedure, 3. expanding the method for application to other HIRLAM countries and 4. implementation of the new land-cover map in HARMONIE-AROME.</p><p> </p><p>[1] Bessardon, G., Gleeson, E., (2019) Using the best available physiography to improve weather forecasts for Ireland. In EMS Annual Meeting.Retrieved fromhttps://presentations.copernicus.org/EMS2019-702_presentation.pdf</p><p>[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . Køltzow, M. Ø. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5),1919–1935.https://doi.org/10.1175/mwr-d-16-0417.1</p><p>[3] Bertini, F., Brand, O., Carlier, S., Del Bello, U., Drusch, M., Duca, R., Fernandez, V., Ferrario, C., Ferreira, M., Isola, C., Kirschner, V.,Laberinti, P., Lambert, M., Mandorlo, G., Marcos, P., Martimort, P., Moon, S., Oldeman,P., Palomba, M., and Pineiro, J.: Sentinel-2ESA’s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012</p><p>[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1–11,http://arxiv.org/abs/2003.02899, 2020</p><p>[5] Walsh, E., Bessardon, G., Gleeson, E., and Ulmas, P. (2021). Using machine learning to produce a very high-resolution land-cover map for Ireland. Advances in Science and Research, (accepted for publication)</p>

Sign in / Sign up

Export Citation Format

Share Document