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Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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8099 KiB

Open AccessEditor’s ChoiceArticle

Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas

by Raphaël D’Andrimont, Catherine Marlier and Pierre Defourny

Remote Sens. 2017, 9(3), 211; https://doi.org/10.3390/rs9030211 - 25 Feb 2017

Cited by 4 | Viewed by 5855

Abstract

Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km²). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m², with an edge accuracy below 1 m over large areas. Full article

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11211 KiB

Open AccessEditor’s ChoiceArticle

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

by Olli Nevalainen, Eija Honkavaara, Sakari Tuominen, Niko Viljanen, Teemu Hakala, Xiaowei Yu, Juha Hyyppä, Heikki Saari, Ilkka Pölönen, Nilton N. Imai and Antonio M. G. Tommaselli

Remote Sens. 2017, 9(3), 185; https://doi.org/10.3390/rs9030185 - 23 Feb 2017

Cited by 328 | Viewed by 25382

Abstract

Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future. Full article

(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)

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3711 KiB

Open AccessEditor’s ChoiceArticle

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

by Yinyin Dou, Zhifeng Liu, Chunyang He and Huanbi Yue

Remote Sens. 2017, 9(2), 175; https://doi.org/10.3390/rs9020175 - 20 Feb 2017

Cited by 88 | Viewed by 9439

Abstract

Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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28694 KiB

Open AccessEditor’s ChoiceArticle

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

by Shuzhen Zhang, Shutao Li, Wei Fu and Leiyuan Fang

Remote Sens. 2017, 9(2), 139; https://doi.org/10.3390/rs9020139 - 07 Feb 2017

Cited by 70 | Viewed by 10708

Abstract

Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers. Full article

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6584 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

by Nathan Torbick, Diya Chowdhury, William Salas and Jiaguo Qi

Remote Sens. 2017, 9(2), 119; https://doi.org/10.3390/rs9020119 - 01 Feb 2017

Cited by 185 | Viewed by 21941

Abstract

Assessment and monitoring of rice agriculture over large areas has been limited by cloud cover, optical sensor spatial and temporal resolutions, and lack of systematic or open access radar. Dense time series of open access Sentinel-1 C-band data at moderate spatial resolution offers new opportunities for monitoring agriculture. This is especially pertinent in South and Southeast Asia where rice is critical to food security and mostly grown during the rainy seasons when high cloud cover is present. In this research application, time series Sentinel-1A Interferometric Wide images (632) were utilized to map rice extent, crop calendar, inundation, and cropping intensity across Myanmar. An updated (2015) land use land cover map fusing Sentinel-1, Landsat-8 OLI, and PALSAR-2 were integrated and classified using a randomforest algorithm. Time series phenological analyses of the dense Sentinel-1 data were then executed to assess rice information across all of Myanmar. The broad land use land cover map identified 186,701 km2 of cropland across Myanmar with mean out-of-sample kappa of over 90%. A phenological time series analysis refined the cropland class to create a rice mask by extrapolating unique indicators tied to the rice life cycle (dynamic range, inundation, growth stages) from the dense time series Sentinel-1 to map rice paddy characteristics in an automated approach. Analyses show that the harvested rice area was 6,652,111 ha with general (R2 = 0.78) agreement with government census statistics. The outcomes show strong ability to assess and monitor rice production at moderate scales over a large cloud-prone region. In countries such as Myanmar with large populations and governments dependent upon rice production, more robust and transparent monitoring and assessment tools can help support better decision making. These results indicate that systematic and open access Synthetic Aperture Radar (SAR) can help scale information required by food security initiatives and Monitoring, Reporting, and Verification programs. Full article

(This article belongs to the Special Issue Mapping, Monitoring and Impact Assessment of Land Cover/Land Use Changes in South and South East Asia)

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4880 KiB

Open AccessEditor’s ChoiceArticle

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to Maximize Detection of Forested Inundation Extent in the Delmarva Peninsula, USA

by Melanie K. Vanderhoof, Hayley E. Distler, Di Ana Teresa G. Mendiola and Megan Lang

Remote Sens. 2017, 9(2), 105; https://doi.org/10.3390/rs9020105 - 25 Jan 2017

Cited by 23 | Viewed by 8401

Abstract

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)

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