Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Multispectral multi-temporal crop cover classification over Türkiye using random forest algorithm
Download
index.pdf
Date
2022-7-28
Author
Dönmez Altındal, Elif
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
252
views
70
downloads
Cite This
Accurate crop cover maps are beneficial for various aspects like water resources management, crop yield prediction, regulation insurance policies, and investigation of the effects of climate change. In this thesis, agricultural crop mapping is performed over Türkiye. Sentinel-2 Level-2A images with 10-meter spatial resolution acquired between March 15, 2019, and October 15, 2019, are reduced to 15-day median images. In addition to spectral bands, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used as classification features. Twenty years of ERA5-Land 2-meter temperature data is averaged to divide the study area into three temperature zones as Low (LTZ), Medium (MTZ), and High-Temperature Zones (HTZ). Before the classification, feature selection using random forest importance is performed to select the most successful features. After that, a random forest classifier is created for each temperature zone. LTZ reached 89% overall accuracy (OA) with a 0.88 Kappa. MTZ reached 91% OA with 0.92 Kappa, and HTZ reached 94% OA with 0.94 Kappa, giving the best accuracy among the classifiers. Finally, test sets of all temperature zones are combined, and OA of 92% with a Kappa of 0.93 is achieved with this combined test set. To test the advantage of temperature zoning, classification is also performed without the temperature zones, and it is observed that temperature zoning increases the OA and Kappa by 1%. A land cover classification map is then created using temperature zone classifiers with 34 crop classes and six non-agricultural classes.
Subject Keywords
Remote sensing
,
Crop cover mapping
,
Machine learning
,
Supervised classification
URI
https://hdl.handle.net/11511/98586
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
A data-driven approach for predicting solar energy potential of buildings in urban fabric
Duran, Ayça; Gürsel Dino, İpek; Department of Architecture (2022-7)
Energy-efficient buildings that use clean and sustainable energy sources are urgently needed to reduce the environmental impact of buildings and mitigate climate change in cities. Buildings have great potential in harvesting solar energy by their solar exposure capacity. Developments in PV technologies also encourage the integration of PV systems into architectural applications. However, urban contexts can limit solar energy generation capacity of buildings by shading building envelopes and reducing availab...
Evaluating land use/cover change with temporal satellite data and information systems
Erener, Arzu; Duzgun, Sebnem; Yalçıner, Ahmet Cevdet (2012-01-01)
Rapid changes in land use land cover (LULC) adversely affect the environment. In order to provide information for policymakers to support sustainable development, detailed data about these regions is urgently needed. Use of remote sensing (RS) in combination with geographical information System (GIS) is one of the effective information technology tools to generate LULC change information. In this study, it is aimed to explore the temporal and spatial characteristics of urban expansion by using RS integrated...
A BIM-based building circularity framework: assessment and visualization through 5R strategies
Yüksel, Pınar Ece; Atasoy Özcan, Güzide; Department of Civil Engineering (2023-1-26)
Principles of the circular economy are applied to improve resource efficiency and reduce environmental impact. The purpose of this thesis is to create a BIM-based framework for building circularity assessment (BCA) that enables an analysis of R strategies such as Reusing, Recycling and Rethinking with graphically aided data representation and supports decision-making starting from the early stages to end of life. To learn more about building circularity indicators and assessment approaches, a literature rev...
Superensembles of raw and bias-adjusted regional climate models for Mediterranean region, Turkey
Mesta, Buket; Kentel Erdoğan, Elçin (2021-09-01)
For regional-scale studies on climate change and relevant impact assessment, the projections of regional climate models (RCMs) are used due to their advantage of high resolution and better representation of the local climate relative to the global climate models. However, direct use of RCM outputs is prone to uncertainties and biases that may significantly diminish the accuracy of results. EURO-COordinated Regional Downscaling EXperiment (CORDEX) initiative that is a part of the global Coordinated Regional ...
Comparison of BEST and LEED green building rating systems through cost based optimization
Uğurlu, Bengisu; Aksoy, Ayşegül; Department of Environmental Engineering (2020-1-25)
Buildings have significant effects on climate change due to vast resource consumption and pollution generation. Improving the effective use of limited resources and constructing environmentally friendly buildings are important in the realm of mitigations for climate change. Several countries have their green building rating systems tailored towards their regulations, distinctive climatic conditions, unique cultures and traditions, diverse building types and ages, or wide-ranging environmental, economic, and...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Dönmez Altındal, “Multispectral multi-temporal crop cover classification over Türkiye using random forest algorithm,” M.S. - Master of Science, Middle East Technical University, 2022.