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Multitemporal Land Cover and Land Use Mapping

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 32808

Special Issue Editor

Associate Professor, College of Natural Resources and Environment, Department of Geography, Virginia Tech, Blacksburg, VA 24061, USA
Interests: remote sensing; GIS; land cover and land use change; watershed modelling

Special Issue Information

Dear Colleagues,

Multitemporal data analysis has been increasingly used by remote sensing researchers in land cover and land use mapping, land cover change detection, vegetation monitoring, and many ecosystem service related research and applications. Instead of using a conventional single “snapshot” image as input, multitemporal land cover mapping focuses on the use of high-temporal (e.g., 8–16 days or monthly) remote sensing data to improve mapping accuracy and robustness. At global and regional scales, multitemporal MODIS data have been routinely used for general land cover and crop-specific mapping. More recently, researchers have explored the potential of time-series Landsat (and harmonized Landsat and Sentinel-2) data for various land cover mapping tasks. Multitemporal land cover mapping typically involves high dimensional input data with noisy signals. Impacts from cloud cover and weather conditions may not be easily addressed through simple data interpolation or gap-filling. High dimensional and noisy input data, combined with a limited number of training data points, often lead to suboptimal classification results. Machine learning algorithms, such as support vector machine, neural networks, and random forest, can be carefully designed/applied to improve classification performance. Previous published studies suggested the strong potential of multitemporal land cover mapping; however, numerous uncertainties exist in automated data pre-processing, image feature extraction and dimension reduction, comparison and selection of classification algorithms, and accuracy assessment from multitemporal domains. To stimulate more research on multitemporal land cover and land use mapping, this Special Issue calls for papers on a range of topics including the following:

  • Pre-processing of time-series data for multitemporal land cover mapping;
  • New image classification methods and algorithms using multitemporal data;
  • Near real-time land cover monitoring and change detection;
  • Accuracy assessment;
  • Review papers on multitemporal land cover mapping.

Dr. Yang Shao
Guest Editor

Manuscript Submission Information

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Published Papers (7 papers)

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Research

24 pages, 8445 KiB  
Article
Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data
by Shuai Xie, Liangyun Liu and Jiangning Yang
Remote Sens. 2020, 12(18), 3091; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183091 - 21 Sep 2020
Cited by 5 | Viewed by 3276
Abstract
Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off [...] Read more.
Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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28 pages, 6973 KiB  
Article
Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment
by Alessandra Capolupo, Cristina Monterisi and Eufemia Tarantino
Remote Sens. 2020, 12(7), 1201; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071201 - 08 Apr 2020
Cited by 38 | Viewed by 6498
Abstract
Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have [...] Read more.
Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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27 pages, 27915 KiB  
Article
Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series
by Yuzhu Hao, Zhenjie Chen, Qiuhao Huang, Feixue Li, Beibei Wang and Lei Ma
Remote Sens. 2020, 12(3), 478; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030478 - 03 Feb 2020
Cited by 7 | Viewed by 2530
Abstract
High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and [...] Read more.
High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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21 pages, 27625 KiB  
Article
Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity
by Xiaokang Zhang, Wenzhong Shi and Zhiyong Lv
Remote Sens. 2019, 11(21), 2509; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11212509 - 26 Oct 2019
Cited by 9 | Viewed by 2989
Abstract
Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In [...] Read more.
Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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17 pages, 6855 KiB  
Article
Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping
by Mohammad Mardani, Hossein Mardani, Lorenzo De Simone, Samuel Varas, Naoki Kita and Takafumi Saito
Remote Sens. 2019, 11(16), 1907; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161907 - 15 Aug 2019
Cited by 15 | Viewed by 4850
Abstract
In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate [...] Read more.
In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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27 pages, 10142 KiB  
Article
A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine
by Hanzeyu Xu, Yuchun Wei, Chong Liu, Xiao Li and Hong Fang
Remote Sens. 2019, 11(16), 1891; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161891 - 13 Aug 2019
Cited by 28 | Viewed by 4479
Abstract
Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand [...] Read more.
Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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15 pages, 9203 KiB  
Article
Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach
by Sajid Pareeth, Poolad Karimi, Mojtaba Shafiei and Charlotte De Fraiture
Remote Sens. 2019, 11(5), 601; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050601 - 12 Mar 2019
Cited by 28 | Viewed by 7231
Abstract
Increase in irrigated area, driven by demand for more food production, in the semi-arid regions of Asia and Africa is putting pressure on the already strained available water resources. To cope and manage this situation, monitoring spatial and temporal dynamics of the irrigated [...] Read more.
Increase in irrigated area, driven by demand for more food production, in the semi-arid regions of Asia and Africa is putting pressure on the already strained available water resources. To cope and manage this situation, monitoring spatial and temporal dynamics of the irrigated area land use at basin level is needed to ensure proper allocation of water. Publicly available satellite data at high spatial resolution and advances in remote sensing techniques offer a viable opportunity. In this study, we developed a new approach using time series of Landsat 8 (L8) data and Random Forest (RF) machine learning algorithm by introducing a hierarchical post-processing scheme to extract key Land Use Land Cover (LULC) types. We implemented this approach for Mashhad basin in Iran to develop a LULC map at 15 m spatial resolution with nine classes for the crop year 2015/2016. In addition, five irrigated land use types were extracted for three crop years—2013/2014, 2014/2015, and 2015/2016—using the RF models. The total irrigated area was estimated at 1796.16 km2, 1581.7 km2 and 1578.26 km2 for the cropping years 2013/2014, 2014/2015 and 2015/2016, respectively. The overall accuracy of the final LULC map was 87.2% with a kappa coefficient of 0.85. The methodology was implemented using open data and open source libraries. The ability of the RF models to extract key LULC types at basin level shows the usability of such approaches for operational near real time monitoring. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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