Comparison of Land Cover Maps Using High Resolution Multispectral and Hyperspectral Imagery

2018 ◽  
Author(s):  
J. Marcello ◽  
D. Rodriguez-Esparragon ◽  
D. Moreno
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Hyperspectral Imagery ◽  
Land Cover Maps

scholarly journals STATISTICS FOR PATCH OBSERVATIONS

2016 ◽  
Vol XLI-B6 ◽  
pp. 235-242
Author(s):  
K. L. Hingee
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Tree Canopy ◽  
Closed Set ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Contact Distribution ◽  
Land Cover Maps ◽  
Underlying Processes

In the application of remote sensing it is common to investigate processes that generate patches of material. This is especially true when using categorical land cover or land use maps. Here we view some existing tools, landscape pattern indices (LPI), as non-parametric estimators of random closed sets (RACS). This RACS framework enables LPIs to be studied rigorously. A RACS is any random process that generates a closed set, which encompasses any processes that result in binary (two-class) land cover maps. RACS theory, and methods in the underlying field of stochastic geometry, are particularly well suited to high-resolution remote sensing where objects extend across tens of pixels, and the shapes and orientations of patches are symptomatic of underlying processes. For some LPI this field already contains variance information and border correction techniques. After introducing RACS theory we discuss the core area LPI in detail. It is closely related to the spherical contact distribution leading to conditional variants, a new version of contagion, variance information and multiple border-corrected estimators. We demonstrate some of these findings on high resolution tree canopy data.

scholarly journals REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 175-182
Author(s):  
G. Bratic ◽  
A. Vavassori ◽  
M. A. Brovelli
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Accuracy Assessment ◽  
General Information ◽  
Tree Canopy ◽  
Global Land Cover ◽  
Tree Canopy Cover ◽  
Global Land ◽  
Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

scholarly journals Open educational resources for validation of global high-resolution land cover maps

2019 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Land Cover Maps

Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

scholarly journals Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3523
Author(s):  
Radek Malinowski ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Ewa Gromny ◽  
Małgorzata Jenerowicz ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Data Processing ◽  
Spatial Resolution ◽  
Large Scale ◽  
Observation Data ◽  
European Continent ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps.

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Vol 11 (17) ◽  
pp. 1986 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Operational Systems ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time-series image data, enabling the production of detailed land cover maps globally. When mapping large territories, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixel-wise classification methods. However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In particular, convolutional neural networks learn features which take into account the context of the pixels and, therefore, a better representation of the data can be obtained. In this paper, we assess fully convolutional neural network architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time-series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time-series image data, an adaptation of the U-Net model (a fully convolutional neural network) for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps. We conclude that fully convolutional neural networks can yield improved results with respect to pixel-wise Random Forest classifiers for classes where texture and context are pertinent. However, this new approach shows higher variability in quality across different landscapes and comes with a computational cost which could be to high for operational systems.

scholarly journals A New Approach to High-Resolution Urban Land Use Classification Using Open Access Software and True Color Satellite Images

2019 ◽  
Vol 11 (19) ◽  
pp. 5266 ◽  
Author(s):  
Fernando Chapa ◽  
Srividya Hariharan ◽  
Jochen Hack
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Google Earth ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Wide Range ◽  
True Color ◽  
Land Cover Maps

Urbanization nowadays results in the most dynamic and drastic changes in land use/land cover, with a significant impact on the environment. A detailed analysis and assessment of this process is necessary to take informed actions to reduce its impact on the environment and human well-being. In most parts of the world, detailed information on the composition, structure, extent, and temporal changes of urban areas is lacking. The purpose of this study is to present a methodology to produce high-resolution land use/land cover maps by the use of free software and satellite imagery. These maps can help to understand dynamic urbanizations processes to plan, design, and coordinate sustainable urban development plans, especially in areas with limited resources and advancing environmental degradation. A series of high-resolution true color images provided by Google Earth Pro were used to do initial classifications with the Semi-Automatic Classification Plug-in in QGIS. Afterwards, a new methodology to improve the classification by the elimination of shadows and clouds, and a reduction of misclassifications through superimposition was applied. The classification was carried out for three urban areas in León, Nicaragua, with different degrees of urbanization for the years 2009, 2015, and 2018. Finally, the accuracy of the classification was analyzed using randomly defined validation polygons. The results are three sets of high-resolution land use/land cover maps of the initial and the improved classification, showing the detailed structures and temporal dynamics of urbanization. The average accuracy of classification reaches 74%, but up to 85% for the best classification. The results clearly identify advancing urbanization, the loss of vegetation and riparian zones, and threats to urban ecosystems. In general, the level of detail and simplicity of our methodology is a valuable tool to support sustainable urban management, although its application is not limited to these areas and can also be employed to track changes over time, providing therefore, relevant information to a wide range of decision-makers.

scholarly journals Open educational resources for the validation of global high-resolution land cover maps

2018 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Validation Procedure ◽  
Land Cover Maps

Land cover (LC) maps are crucial to understand and analyze several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, it has been observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on the validation procedure through open source and easy-to-use tools. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

scholarly journals STATISTICS FOR PATCH OBSERVATIONS

2016 ◽  
Vol XLI-B6 ◽  
pp. 235-242
Author(s):  
K. L. Hingee
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Tree Canopy ◽  
Closed Set ◽  
Closed Sets ◽  
Random Closed Sets ◽  
Contact Distribution ◽  
Land Cover Maps ◽  
Underlying Processes

In the application of remote sensing it is common to investigate processes that generate patches of material. This is especially true when using categorical land cover or land use maps. Here we view some existing tools, landscape pattern indices (LPI), as non-parametric estimators of random closed sets (RACS). This RACS framework enables LPIs to be studied rigorously. A RACS is any random process that generates a closed set, which encompasses any processes that result in binary (two-class) land cover maps. RACS theory, and methods in the underlying field of stochastic geometry, are particularly well suited to high-resolution remote sensing where objects extend across tens of pixels, and the shapes and orientations of patches are symptomatic of underlying processes. For some LPI this field already contains variance information and border correction techniques. After introducing RACS theory we discuss the core area LPI in detail. It is closely related to the spherical contact distribution leading to conditional variants, a new version of contagion, variance information and multiple border-corrected estimators. We demonstrate some of these findings on high resolution tree canopy data.

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