Summary
In this special issue, we wish to explore the current state in using remote sensing technology to understand land surface properties, patterns, and processes. In particular, studies that employ remotely sensed data to derive quantitative measurements of land surface properties, to characterize and quantify land surface ecological and geographical patterns, and to analyze and model land surface processes are encouraged. The virtues and importance of remote sensing data from various ground, aircraft, and satellite platforms will be assessed. Moreover, we wish to explore how improved sensor and analytical techniques can be employed to better define, characterize, quantify, and model land surface forms, patterns, and processes. The topics may include, but are not limited to, the following:
- Retrieval and analysis of land surface biophysical parameters, temperatures, surface emissivity, surface roughness and anisotropy.
- Analysis of remote sensed data derived parameters such as land surface temperature, emissivity, vegetation cover, soil type, soil moisture, and surface water content for estimation of surface energy fluxes and investigation of land-atmosphere interactions.
- Utilization of remote sensing data of various platforms for landscape characterization (e.g., land cover/land use attributes, impervious surfaces, and habitats).
- Analysis of land surface patterns and their relations to land surface processes and properties.
- Application of GIS, geospatial statistics, visualization, and landscape ecological approaches to remote sensing of land surfaces.
- Study of the improvements in spatial, spectral, radiometric, and temporal resolutions of remote sensing data for analysis of land surface properties, patterns, and processes.
- Investigation of impacts of spatial and temporal scale on analysis of remote sensing data.
Markus Hollaus 1,*, Wolfgang Wagner 1,2, Bernhard Maier 3 and Klemens Schadauer 41 Christian Doppler Laboratory for “Spatial Data from Laser Scanning and Remote Sensing”, at the Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gußhausstraße 27-29, 1040 Vienna, Austria, Tel: ++43(0)1 58801 12239, Fax: ++43(0)1 58801 12299. E-mails: mh@ipf.tuwien.ac.at; ww@ipf.tuwien.ac.at
2 Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gußhausstraße 27-29, 1040 Vienna, Austria. E-mail: ww@ipf.tuwien.ac.at
3 Stand Montafon Forstfonds, Montafonerstraße 21, 6780 Schruns, Austria. E-mail: bernhard.maier@stand-montafon.at
4 Department of Forest Inventory at the Federal Research and Training Center for Forests, Natural Hazards and Landscape, Seckendorff-Gudent-Weg,
1130 Vienna, Austria. E-mail: klemens.schadauer@bfw.gv.at
* Author to whom correspondence should be addressed. Received: 20 July 2007 / Accepted: 14 August 2007 / Published: 17 August 2007Full Paper: Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment
Sensors 2007, 7, 1559-1577 (PDF format, 1770 K)
Christopher D. Elvidge 1,*, Benjamin T. Tuttle 2,3, Paul C. Sutton 3, Kimberly E. Baugh 2, Ara T. Howard 2, Cristina Milesi 4,
Budhendra Bhuduri 5 and Ramakrishna Nemani 6
1 Earth Observation Group, NOAA National Geophysical Data Center, 325 Broadway, Boulder, Colorado 80305, USA. Email: chris.elvidge@noaa.gov
2 Cooperative Institute for Research in the Environmental Sciences University of Colorado, Boulder, Colorado, USA. Email: ben.tuttle@noaa.gov, kim.baugh@noaa.gov, ara.t.howard@noaa.gov
3 Department of Geography, University of Denver, Denver, Colorado, USA. Email: psutton@du.edu
4 Foundation of California State University, Monterey Bay, California.
5 U.S. Department of Energy, Oak Ridge National Laboratory
6 NASA Ames Research Center, Moffett Field, California, USA.
* Author to whom correspondence should be addressed.
Received: 30 August 2007 / Accepted: 11 September 2007 / Published: 21 September 2007
Full Paper: Global Distribution and Density of Constructed Impervious Surfaces
Sensors 2007, 7, 1962-1979 (PDF format, 4690 K)
Amanda Harris 1, Sayma Rahman 2, Faisal Hossain 1,*, Lance Yarborough 3, Amvrossios C. Bagtzoglou 2 and Greg Easson 3
1 Department of Civil and Environmental Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
2 Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
3 Geoinformatics Research Center, Department of Geological Engineering, University of Mississippi. Oxford, MS, USA
* Author to whom correspondence should be addressed. E-mail: fhossain@tntech.edu
Received: 30 November 2007 / Accepted: 18 December 2007 / Published: 20 December 2007
Full Paper: Satellite-based Flood Modeling Using TRMM-based Rainfall Products
Sensors 2007, 7, 3416-3427 (PDF format, 574 K)
Minha Choi 1,* and Jennifer M. Jacobs 2
1 Research Physical Scientist, USDA-ARS Hydrology & Remote Sensing Lab., Beltsville, MD 20705, U.S.A.; E-mail: minha.choi@ars.usda.gov
2 Associate Professor of Civil Engineering, University of New Hampshire, Durham, NH 03824, U.S.A.; E-mail: jennifer.jacobs@unh.edu
* Author to whom correspondence should be addressed.
Received: 3 December 2007 / Accepted: 8 April 2008 / Published: 14 April 2008
Full Research Paper: Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.
Sensors 2008, 8, 2617-2627 (PDF format, 303 K)
Desheng Liu 1,* and Ruiliang Pu 2
1 Department of Geography and Department of Statistics, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210 USA; Tel: 614-247-2775; Fax: 614-292-6213; E-mail: liu.738@osu.edu
2 Department of Geography, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL 33620 USA; Tel.: +1 813 974 1508; Fax: +1 813 974 4808; E-mail: rpu@cas.usf.edu
* Author to whom correspondence should be addressed.
Received: 2 March 2008 / Accepted: 8 April 2008 / Published:16 April 2008
Full Research Paper: Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval
Sensors 2008, 8, 2695-2706 (PDF format, 597 K)
Submitted Papers
Title: Using synthetic data with a statistical concept for retrieving near-surface soil moisture in passive microwave remote sensing
Author: Khil-Ha Lee
Title: Analyzing Land Use/cover Changes using Remote Sensing and GIS in Rize, North-East Turkey
Author: Selçuk Reis
Title: Detection of aspens using high resolution aerial laser scanning data and digital aerial images
Authors: Raita Säynäjoki 1, Petteri Packalén 1, Matti Maltamo 1,*, Mikko Vehmas 1 and Kalle Eerikäinen 2
Title: Changes in Carbon Storage and Oxygen Production in Forest Timber Biomass of Balcı Forest Management Units in Turkey between 1984 and 2006
Authors: Hacı Ahmet Yolasığmaz¹,*, Sedat Keleş²
Title: Data Base Design with GIS in Ecosystem Based Multiple Use Forest Management in Turkey: a case study in Balcı Forest Management Planning Unit
Authors: Hacı Ahmet Yolasığmaz¹,*, Sedat Keleş²
Title: A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery
Authors: Wen-Chun Cheng 1, Jyh-Chian Chang 2, Chien-Ping Chang 1, Yu Su 1 and Te-Ming Tu 1*
Planned Papers
Title: "Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification"
Authors: Martin Rutzinger 1,2,*, Bernhard Höfle 3, Markus Hollaus 4 and Norbert Pfeifer 3
1 alpS - Centre for Natural Hazard Management, Grabenweg 3, A-6020 Innsbruck. E-mail: rutzinger@alps-gmbh.com
2 Institute of Geography, University of Innsbruck, Innrain 52, A-6020 Innsbruck. E-mail: martin.rutzinger@uibk.ac.at
3 Institut of Photogrammetry and Remote Sensing, TU Vienna, Gußhausstraße 27-29, A-1040 Vienna. Email: bh@ipf.tuwien.ac.at. E-mail: np@ipf.tuwien.ac.at
4 Christian Doppler Laboratory “Spatial Data from Laser Scanning and Remote Sensing” at the Institut of Photogrammetry and Remote Sensing, TU Vienna, Gußhausstraße 27-29, A-1040 Vienna. E-mail: mh@ipf.tuwien.ac.at
* Author to whom correspondence should be addressed.
Abstract: Airborne laser scanning (ALS) is a well-suited remote sensing technique for 3D vegetation mapping and structure characterization because the emitted laser pulse is able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (> 20 echoes/m2) and additional classification variables from fullwaveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, allow new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect high vegetation in urban environments. The definition high vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Then segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we processed data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is proofed for two independent validation sites. The results of the OBPA vegetation classification are enhanced by a 3D mode filter, grouping fragmented point groups of small objects to neighboring, larger ones. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness about 90% and correctness about 97% is reached for the validation sites. The comparision of the classification results show good separability of buildings and terrain points respectively, which are occluded by vegetation.
Keywords: Object-based Point Cloud Analysis, Urban vegetation, Segmentation, 3D feature calculation, Classification, Error assessment, Full-waveform, Airborne laser scanning.
Title: "Detection of aspens using high resolution aerial laser scanning data and digital aerial images"
Authors: Säynäjoki, R. 1, Packalén, P. 1, Maltamo, M. 1, Vehmas, M. 1 & Eerikäinen, K. 2
E-Mail:Matti.Maltamo@joensuu.fi
1 University of Joensuu, Faculty of Forest Sciences, 2 Finnish Forest Research Institute, Joensuu Research Unit
Abstract: The aim of the study was to apply a high resolution Aerial Laser Scanning (ALS) data and an aerial images -based individual tree detection technique to the discrimination of aspen (Populus tremula L.) individuals from other deciduous trees. Field data consisted of 14 sample plots with the size of 30×30 m. The study area was located in the Koli National Park in North Carelia, eastern Finland.
Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2. The CHM was low-pass filtered using a Height Based Filtering (HBF) after which it was further binarized to create a mask that is needed for the separation of the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments. The final canopy segments were obtained by extracting the non-canopy elements from the preliminary canopy segments, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was applied in the separation of canopy segments of deciduous trees from the canopy segments of coniferous trees. Finally, the linear discriminant analysis with the correctly classified canopy segments of deciduous trees was used to classify the segments belonging to either aspen or other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data.
The highest percentage attained for the classification accuracy between aspen and other deciduous trees was 79.1 %. Independent variables of the classification function were proportion of vegetation hits, standard deviation of pulse heights, accumulated intensity at the 90th percentile and the ratio between proportions of laser points reflected at the 95th and 40th percentile of height. The accuracy of classification corresponded to the validation results of earlier ALS data -based studies on tree species classification of deciduous tree species.
Keywords: airborne laser scanning, digital aerial images, aspen, individual tree detection, tree species classification
Title: "Estimation of Surface Melt Intensity using MODIS Optical and Thermal Measurements over the Greenland Ice Sheet"
Authors: Derrick J. Lampkin
Assistant Professor, Department of Geography, Graduate Faculty, Department of Geoscience, College of Earth and Mineral Sciences, Pennsylvania State University
Abstract: Satellite observations of the Greenland ice sheet have indicated an increase in the extent and duration of surface melt associated with acceleration in ice sheet velocities. Consequently, there is concern about how changes in ice sheet mass balance will contribute to sea level rise in the future. Surface melt patterns and their duration are an important component of ice sheet mass balance, and have been successfully measured. However, the estimation of ice sheet surface melt rate is still underdetermined from passive microwave approaches. An algorithm for improved assessment of spatio-temporal melt dynamics over the Greenland Ice Sheet has been developed, using coupled optical-thermal satellite signatures, calibrated by melt water content derived from a physical snowmelt model. Meteorological data collected by GC-Net stations from May 25 to July 11, 2001 were used to force SNTHERM89. An empirical snow melt intensity model is derived based on a quantitative relationship between satellite data and liquid water content, and applied to MODIS optical-thermal 8-day composite mosaics over the entire Greenland Ice Sheet at 1km2 to map the melt trend for the 2001 melt season.
Keywords: Greenland ice sheet, snow melt, remote sensing
Title: "An automatic instrument to measure spatial distribution of land surface emissivity"
Authors: Jing Tian*, Ren-hua Zhang, Hong-bo Su, Xiao-min Sun and Jun Xia
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Abstract: The emissivity of land surface is a crucial parameter required in land surface modeling, specifically, in the study of energy balance on land surface. Currently, land surface emissivity in thermal infrared remote sensing is either indirectly inferred, or comes from a look-up table categorized by land/vegetation classification. Direct measurement of emissivity from space has not been demonstrated and the distribution of land surface emissivity can not be observed although the approach about this has been proposed for almost twenty years. In this paper, the design of an automatic instrument to measure spatial distribution of land surface emissivity is presented, which makes the direct field measurement of the spatial distribution of emissivity possible. The significance of this new approach lies in two aspects. One is that it helps to investigate the scaling problems of emissivity and temperature; the other is that, the design of the instrument provides a feasible idea to measure surface emissivity from space and to directly acquire the spatial distribution of land surface emissivity. To improve the accuracy of the measurements, the emissivity measurement and its uncertainty are examined in a series of carefully designed experiments. The impact of the variation of target temperature and the environmental irradiance on the measurement of emissivity is analyzed as well. Instrument calibration was achieved by minimizing the uncertainty of emissivity. In addition, the ideal temperature difference between hot environment and cool environment is obtained based on numerical simulations. Finally, the scaling behavior of surface emissivity and surface temperature caused by the heterogeneity of target is discussed.
Title: "A revised two-source surface flux model for Evapotranspiration and CO2 assimilation and its application in North China using MODIS data"
Authors: Ren-hua Zhang, Jing Tian, Hong-bo Su, Xiao-min Sun, Jun Xia
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Abstract: Recently, more and more attention has been paid in the retrieval of surface flux using quantitative remote sensing. As well known, in arid or semi-arid regions particularly in northern China, it is necessary to adopt two-source flux models due to the highly heterogeneity of the vegetation cover. In these two-source models, the challenge is how to separate the mixed surface temperature and reflectance for soil and vegetation. At present, multi-angel observations and geometrical models make it possible to solve this problem. However, most of widely used satellite sensors, such as MODIS, TM, can not provide multi-angular data, which limits the application of the multi-angle method. Therefore, to remotely estimate land surface fluxes, alternative methods have to be investigated. In this paper, a PCACA (Pixel Component Arranging and Comparing Algorithm) is presented, to separate surface temperature and reflectance respectively for vegetation and soil within a mixed pixel. In combination with the algorithm to partition the available energy (Rn-G) instead of using Beer-law, a two-source flux model was established. To improve the accuracy of the model, four improvements were made comparing with the previous version of model in 2005. The improvements include: a) The method of determining the ideal temperatures of extreme dry and wet soil moisture for fully vegetation fraction cover and bare soil (four cases) was calculated . b) All factors (such as reflectivity, aerodynamic resistance, etc) other than soil moisture, which have an influence on surface temperature, are identified and eliminated, to thoroughly investigate the relationship between soil moisture and the surface temperature; c) the method for partitioning available energy (Rn-G) between soil surface and vegetation is improved, based on the scatter plot of component temperature vs. vegetation fraction; d) The algorithms of determining air temperature, humidity and aerodynamic resistance at each pixel are improved by integrating the observations from ground meteorological stations. The revised model is applied to derive the regional distributions of soil evaporation, vegetation transpiration and vegetation assimilation CO2 flux in North China based on MODIS data. The comparison of the model outputs with the flux observations at Yucheng ecological station shows that the revised model gives more reasonable and accurate estimates of surface fluxes than the previous model.
Title: "Assessment of Cost-effective Aerial Imagery for Crop Monitoring"
Authors: C. Lelong, G. Jubelin, B. Roux, P. Burger, S. Labbé, and F. Baret
UMR TETIS CIRAD/Cemagref/ENGREF, Maison de la Télédétection, 500 Rue Jean-Fançois Breton, 34093 Montpellier Cedex 5, FRANCE
Abstract: This paper outlines how light Unmaned Aerial Vehicules (UAV) can be used in remote sensing for precision farming. It focuses on the combination of market digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown over wheat trial microplots in the South-West of France at six dates staggered over the crop season. For each date, we acquired multiple views in four spectral bands corresponding to mean blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental microplot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, through three different methods. We established therefore a robust and generic relationship between, in one hand, LAI and NDVI and, in the other hand, nitrogen content and GNDVI. A validation protocol shows that we can expect a confidence level of 10% in the biophysical parameters estimation while using this relationship.
Title: "Mapping Annual Vegetation Potential in the Mojave Desert Using MODIS-EVI Data "
Authors: Cynthia S.A. Wallace and Kathryn A. Thomas
U.S. Geological Survey, 520 North Park, Tucson, AZ 85719
Abstract: Annual vegetation cover is an important attribute of desert ecosystems that affects a number of processes or characteristics of the Mojave Desert, including soil-moisture availability, potential evapo-transpiration, stability of biological soil crusts, wind and water erosion potential, wildfire potential, and wildlife habitat. We used Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) to develop a model of annual vegetation potential in the central Mojave Desert, an area of 125,000 km2 including parts of California, Arizona, Utah, and Nevada, USA. The model was developed by applying an unsupervised classification to the MODIS-EVI data to extract the phenological signatures of discrete landscapes in the study area, inspecting these signatures for features related to annual vegetation green-up and senescence, and calculating a suite of measures that capture the observed characteristic phenologies. The phenological measures were then evaluated for use as annual cover proxies by testing how well they predicted field estimates of annual vegetation cover collected during 2003 and 2005 in the Mojave National Preserve. The final model produced R2 = 0.63 and yielded a map of annual vegetation potential in the Mojave Desertat 250 m spatial resolution.
Title: "Ecosystem functional changes in South America and their recognition through different long-term AVHRR NDVI series"
Authors: Baldi G, Nosetto M, Jobbágy E
Grupo de Estudios Ambientales, Insituto de Matemática Aplicada de San Luis, Av. Ejército de los Andes 950 (D5700HHW), San Luis - ARGENTINA.
Tel.: (+54) - 2652- 424740, http://gea.unsl.edu.ar
Abstract: This paper will a) compare three long-term NDVI series (PAL, GIMMS and FASIR) in South America, b) analyze their usefulness to detect ecosystem functional changes and c) present a collaborative web-based utility (http://lechusa.unsl.edu.ar/) oriented to analyze and understand these functional changes.
Submission
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MDPI - Matthias Burkhalter - 16 April 2008