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Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 April 2017) | Viewed by 58638

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Prof. Dr. Massimo Menenti

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1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
Interests: land surface processes; terrestrial water cycle; water management; optical remote sensing
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Prof. Dr. Li Jia

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Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100101, China
Interests: earth observations for terrestrial water cycle study; evapotranspiration; water resource; land surface process; optical-thermal remote sensing; climate change
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Special Issue Information

Dear Colleagues,

Progress towards sustainable development requires ever-increasing knowledge on the amount, the conditions, and the response of terrestrial vegetation to climate variability and its role in the coupled cycles of energy, water, and carbon.

Many vegetation properties are related to features of reflectance spectra in the 400–2500 nm region. Detailed observations of spectral reflectance reveal subtle features related to biochemical components of leaves, such as chlorophyll and water. The architecture of vegetation canopies determines complex changes of observed reflectance spectra with view and illumination angle. Quantitative analysis of reflectance spectra requires, therefore, an accurate characterization of the anisotropy of reflected radiance. Concurrent measurements of vegetation canopies with LIDAR systems can provide a very detailed characterization of the architecture of vegetation canopies. Exchange of energy between the biosphere and the atmosphere is an important mechanism determining the response of vegetation to climate variability. This requires measurements of the component temperatures of foliage and soil. The latter is closely related to the angular variation in thermal infrared emittance.

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of land surface response to climate variability. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes.

Contributions are expected on both innovative observation concepts, particularly combining the techniques mentioned above, and time series analysis of satellite observations to observe and understand the response of terrestrial vegetation to climate forcing.

Prof. Dr. Massimo Menenti
Prof. Dr. Li Jia
Guest Editors

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Keywords

Imaging spectrometry
Thermal infrared radiometry
Multi-angular radiometry
Stereo-grammetry
Laser altimetry
Radiative transfer modeling
Time series analysis

Published Papers (7 papers)

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Research

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4992 KiB

Open AccessArticle

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating

by Xiaoman Lu, Guang Zheng, Colton Miller and Ernesto Alvarado

Sensors 2017, 17(9), 2062; https://doi.org/10.3390/s17092062 - 08 Sep 2017

Cited by 10 | Viewed by 4594

Abstract

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m²) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m²) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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3449 KiB

Open AccessArticle

A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat—GLAS Footprints

by Tianjin Huang, Li Jia, Massimo Menenti, Jing Lu, Jie Zhou and Guangcheng Hu

Sensors 2017, 17(8), 1803; https://doi.org/10.3390/s17081803 - 05 Aug 2017

Cited by 4 | Viewed by 4280

Abstract

We present in this paper a polynomial fitting method applicable to segments of footprints measured by the Geoscience Laser Altimeter System (GLAS) to estimate glacier thickness change. Our modification makes the method applicable to complex topography, such as a large mountain glacier. After a full analysis of the planar fitting method to characterize errors of estimates due to complex topography, we developed an improved fitting method by adjusting a binary polynomial surface to local topography. The improved method and the planar fitting method were tested on the accumulation areas of the Naimona’nyi glacier and Yanong glacier on along-track facets with lengths of 1000 m, 1500 m, 2000 m, and 2500 m, respectively. The results show that the improved method gives more reliable estimates of changes in elevation than planar fitting. The improved method was also tested on Guliya glacier with a large and relatively flat area and the Chasku Muba glacier with very complex topography. The results in these test sites demonstrate that the improved method can give estimates of glacier thickness change on glaciers with a large area and a complex topography. Additionally, the improved method based on GLAS Data and Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) can give estimates of glacier thickness change from 2000 to 2008/2009, since it takes the 2000 SRTM-DEM as a reference, which is a longer period than 2004 to 2008/2009, when using the GLAS data only and the planar fitting method. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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4187 KiB

Open AccessArticle

Temperature Dependence of the Rayleigh Brillouin Spectrum Linewidth in Air and Nitrogen

by Kun Liang, Jiaqi Xu, Peng Zhang, Yuanqing Wang, Qunjie Niu, Li Peng and Bo Zhou

Sensors 2017, 17(7), 1503; https://doi.org/10.3390/s17071503 - 26 Jun 2017

Cited by 13 | Viewed by 4677

Abstract

The relation between spontaneous Rayleigh Brillouin (SRB) spectrum linewidth, gas temperature, and pressure are analyzed at the temperature range from 220 to 340 K and the pressure range from 0.1 to 1 bar, covering the stratosphere and troposphere relevant for the Earth’s atmosphere and for atmospheric Lidar missions. Based on the analysis, a model retrieving gas temperature from directly measured linewidth is established and the accuracy limitations are estimated. Furthermore, some experimental data of air and nitrogen are used to verify the accuracy of the model. As the results show, the retrieved temperature shows good agreement with the reference temperature, and the absolute difference is less than 3 K, which indicates that this method provides a fruitful tool in satellite retrieval to extract the gaseous properties of atmospheres on-line by directly measuring the SRB spectrum linewidth. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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5322 KiB

Open AccessArticle

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

by Jinshui Zhang, Zhoumiqi Yuan, Guanyuan Shuai, Yaozhong Pan and Xiufang Zhu

Sensors 2017, 17(5), 960; https://doi.org/10.3390/s17050960 - 26 Apr 2017

Viewed by 4561

Abstract

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C) and kernel width (s), in mapping homogeneous specific land cover. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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12717 KiB

Open AccessArticle

Multispectral LiDAR Data for Land Cover Classification of Urban Areas

by Salem Morsy, Ahmed Shaker and Ahmed El-Rabbany

Sensors 2017, 17(5), 958; https://doi.org/10.3390/s17050958 - 26 Apr 2017

Cited by 83 | Viewed by 9668

Abstract

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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12002 KiB

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Implementation of an Electronic Ionosonde to Monitor the Earth’s Ionosphere via a Projected Column through USRP

by Jhon Jairo Barona Mendoza, Carlos Fernando Quiroga Ruiz and Carlos Rafael Pinedo Jaramillo

Sensors 2017, 17(5), 946; https://doi.org/10.3390/s17050946 - 25 Apr 2017

Cited by 5 | Viewed by 7936

Abstract

This document illustrates the processes carried out for the construction of an ionospheric sensor or ionosonde, from a universal software radio peripheral (USRP), and its programming using GNU-Radio and MATLAB. The development involved the in-depth study of the characteristics of the ionosphere, to apply the corresponding mathematical models used in the radar-like pulse compression technique and matched filters, among others. The sensor operates by firing electromagnetic waves in a frequency sweep, which are reflected against the ionosphere and are received on its return by the receiver of the instrument, which calculates the reflection height through the signal offset. From this information and a series of calculations, the electron density of the terrestrial ionosphere could be obtained. Improving the SNR of received echoes reduces the transmission power to a maximum of 400 W. The resolution associated with the bandwidth of the signal used is approximately 5 km, but this can be improved, taking advantage of the fact that the daughterboards used in the USRP allow a higher sampling frequency than the one used in the design of this experiment. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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Review

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A Review of Wetland Remote Sensing

by Meng Guo, Jing Li, Chunlei Sheng, Jiawei Xu and Li Wu

Sensors 2017, 17(4), 777; https://doi.org/10.3390/s17040777 - 05 Apr 2017

Cited by 319 | Viewed by 22167

Abstract

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. Full article

(This article belongs to the Special Issue Understanding Land Surface Processes and Ecosystem Changes with Optical and Laser Remote Sensing)

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