Quantifying Uncertainty in Land-Use/Land-Cover Classification Accuracy: A Stochastic Simulation Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Ke-Sheng Cheng ◽  
Jia-Yi Ling ◽  
Teng-Wei Lin ◽  
Yin-Ting Liu ◽  
You-Chen Shen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Stochastic Simulation ◽  
Classification Accuracy ◽  
Stochastic Simulations ◽  
Training Data ◽  
Land Use Land Cover ◽  
Simulation Approach ◽  
Data Set ◽  
Sample Data

In numerous applications of land-use/land-cover (LULC) classification, the classification rules are determined using a set of training data; thus, the results are inherently affected by uncertainty in the selection of those data. Few studies have assessed the accuracy of LULC classification with this consideration. In this article, we provide a general expression of various measures of classification accuracy with regard to the sample data set for classifier training and the sample data set for the evaluation of the classification results. We conducted stochastic simulations for LULC classification of a two-feature two-class case and a three-feature four-class case to show the uncertainties in the training sample and reference sample confusion matrices. A bootstrap simulation approach for establishing the 95% confidence interval of the classifier global accuracy was proposed and validated through rigorous stochastic simulation. Moreover, theoretical relationships among the producer accuracy, user accuracy, and overall accuracy were derived. The results demonstrate that the sample size of class-specific training data and the a priori probabilities of individual LULC classes must be jointly considered to ensure the correct determination of LULC classification accuracy.

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

scholarly journals Land use Land Cover Mapping using Modified Ant Colony Optimization Technique

2020 ◽  
Vol 9 (4) ◽  
pp. 100-106
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Environmental Changes ◽  
Optimization Technique ◽  
Unsupervised Classification ◽  
Ant Colony ◽  
Landsat 8 ◽  
Land Use Land Cover

Land use Land cover classification is an important aspect for managing natural resources and monitoring environmental changes. Urban expansion becomes one of the major challenges for the administrator. The LANDSAT 8 images are processed using the open source GRASS (Geographic Resource Analysis Support System). Unsupervised classification technique based on Ant Colony Optimization (ACO) algorithm has been modified and proposed as Modified Ant Colony Optimization (MACO) for LULC classification. In order to improve the classification accuracy of the proposed algorithm, we have combined spatial, spectral and texture features to extract more information of homogeneous land surface. The classification accuracy of the proposed algorithm has been compared with other unsupervised classification methods such as k-means, ISODATA and ACO algorithms. The overall classification accuracy of proposed unsupervised MACO algorithm has been increased by 11.24 %, 8.24% for open space and water bodies class, respectively as compared to ACO algorithm.

scholarly journals ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 669-674 ◽  
Author(s):  
C. C. Fonte ◽  
L. See ◽  
J. C. Laso-Bayas ◽  
M. Lesiv ◽  
S. Fritz
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Reference Database ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Data Set ◽  
Mixed Pixels ◽  
Land Cover Map

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

scholarly journals Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

2020 ◽  
Vol 12 (20) ◽  
pp. 3428
Author(s):  
Cidália C. Fonte ◽  
Joaquim Patriarca ◽  
Ismael Jesus ◽  
Diogo Duarte
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Software Package ◽  
Random Forest Classifier ◽  
Training Data ◽  
Automatic Extraction ◽  
Land Use Land Cover ◽  
Mixed Pixels ◽  
Different Characteristics ◽  
Sentinel 2

This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image classification. Firstly, OSM data was converted into LULC maps using the OSM2LULC_4T software package. The Random Forest classifier was then trained to classify a time-series of Sentinel-2 imagery into 8 LULC classes with samples extracted from: (1) The LULC maps produced by OSM2LULC_4T (TD0); (2) the TD1 dataset, obtained after removing mixed pixels from TD0; (3) the TD2 dataset, obtained by filtering TD1 using radiometric indices. The classification results were generalized using a majority filter and hybrid maps were created by merging the classification results with the OSM2LULC outputs. The accuracy of all generated maps was assessed using the 2018 official “Carta de Ocupação do Solo” (COS). The methodology was applied to two study areas with different characteristics. The results show that in some cases the filtering procedures improve the training data and the classification results. This automated methodology allowed the production of maps with overall accuracy between 55% and 78% greater than that of COS, even though the used nomenclature includes classes that can be easily confused by the classifiers.

scholarly journals The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification

2019 ◽  
Vol 11 (19) ◽  
pp. 2249 ◽  
Author(s):  
Patrick Leinenkugel ◽  
Ramona Deck ◽  
Juliane Huth ◽  
Marco Ottinger ◽  
Benjamin Mack
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Natura 2000 ◽  
High Potential ◽  
Training Data ◽  
Data Generation ◽  
Data Set ◽  
Similar Accuracy ◽  
Landsat Satellite

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.

scholarly journals Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Tomáš Řezník ◽  
Jan Chytrý ◽  
Kateřina Trojanová
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Training Data ◽  
External Information ◽  
Land Use Land Cover ◽  
Proof Of Concept ◽  
Area Of Interest ◽  
Open Land ◽  
Sentinel 2

Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

scholarly journals Detecting Land Use Modification And Its Influence on Resident Communities

2020 ◽  
Author(s):  
Tesfahun Admas Endalew
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Thematic Mapper ◽  
Land Use Land Cover ◽  
Tree Plantation ◽  
Data Set ◽  
Entire Study ◽  
Socioeconomic Analysis ◽  
The Impact

Abstract Background The study intended to detecting the land use land cover changes, trends and their magnitude between 1986 and 2019 years by using GIS and remote sensing in Fagita Lekoma District, Amhara region, Ethiopia. Three satellite data set of Landsat Thematic Mapper for 1986, Enhanced Thematic Mapper Plus for 2002 and Operational Land Imager for 2019 were used generate land use and land cover maps of the study area. Post classification comparison changed detection method was employed to identify gains and losses between Land Use Land Cover classes. Socioeconomic survey, key informant interview and field observation were also used conclude the encouragement of land use /land cover change in the study area. Results The result shows that cultivated land and wetland similarly decline in the entire study periods. In the 33 years, forest lands expanded by upon 200% of the original forest cover what was existed on the base year. Whereas, a result of the socioeconomic analysis the expansion of Acacia decurrens tree plantations and agricultural land are main causes of land use land cover change in the study area. The impact of this land use land cover change is more significant on the livelihood condition and status of the study area. Conclusion The land use system of the study area highly converted cultivation land into forest/tree plantation. Mainly, the expansion of Acacia decurrens tree plantation on farmland is increasing the income of local residence when compare with the previous living condition in the study area.

scholarly journals BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine

2018 ◽  
Vol 10 (9) ◽  
pp. 1455 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey Cardille ◽  
Michael Coe
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Google Earth ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Data Set ◽  
Mato Grosso ◽  
Order Of Magnitude ◽  
Advanced Analysis ◽  
Google Earth Engine

Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales.

scholarly journals Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images

2016 ◽  
Vol XV (1) ◽  
pp. 45-53 ◽  
Author(s):  
Varun Narayan Mishra ◽  
Praveen Kumar Rai ◽  
Pradeep Kumar ◽  
Rajendra Prasad
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Land Use Land Cover ◽  
Remote Sensing Images

scholarly journals Detecting Land Use Modification And Its Influence on Resident Communities

2020 ◽  
Author(s):  
Tesfahun Admas Endalew
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Thematic Mapper ◽  
Land Use Land Cover ◽  
Tree Plantation ◽  
Data Set ◽  
Entire Study ◽  
Socioeconomic Analysis ◽  
The Impact

Abstract Background The study intended to detecting the land use land cover changes, trends and their magnitude between 1986 and 2019 years by using GIS and remote sensing in Fagita Lekoma District, Amhara region, Ethiopia. Three satellite data set of Landsat Thematic Mapper for 1986, Enhanced Thematic Mapper Plus for 2002 and Operational Land Imager for 2019 were used generate land use and land cover maps of the study area. Post classification comparison changed detection method was employed to identify gains and losses between Land Use Land Cover classes. Socioeconomic survey, key informant interview and field observation were also used conclude the encouragement of land use /land cover change in the study area. Results The result shows that cultivated land and wetland similarly decline in the entire study periods. In the 33 years, forest lands expanded by upon 200% of the original forest cover what was existed on the base year. Whereas, a result of the socioeconomic analysis the expansion of Acacia decurrens tree plantations and agricultural land are main causes of land use land cover change in the study area. The impact of this land use land cover change is more significant on the livelihood condition and status of the study area. Conclusion The land use system of the study area highly converted cultivation land into forest/tree plantation. Mainly, the expansion of Acacia decurrens tree plantation on farmland is increasing the income of local residence when compare with the previous living condition in the study area.

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