Remote Sensing

41 pages, 45289 KiB

Open AccessArticle

Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales

by Xiaocui Wu^1,2, Weimin Ju^1,2,*, Yanlian Zhou³, Mingzhu He⁴, Beverly E. Law⁵

, T. Andrew Black⁶, Hank A. Margolis⁷, Alessandro Cescatti⁸, Lianhong Gu⁹, Leonardo Montagnani^10,11, Asko Noormets¹², Timothy J. Griffis¹³, Kim Pilegaard¹⁴, Andrej Varlagin¹⁵, Riccardo Valentini¹⁶, Peter D. Blanken¹⁷, Shaoqiang Wang¹⁸, Huimin Wang¹⁸, Shijie Han¹⁹, Junhua Yan²⁰, Yingnian Li²¹, Bingbing Zhou³ and Yibo Liu²² Show full author list Hide full author list

¹ International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

² Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, China

³ School of Geographic and Oceanographic Science, Nanjing University, Nanjing 210023, China

⁴ Numerical Terradynamic Simulation Group, the University of Montana, Missoula, MT 59812, USA

⁵ College of Forestry, Oregon State University, Corvallis, OR 97331, USA

⁶ Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

⁷ Center ďÉtude de la Forêt, Laval University, Quebec City, QC G1V 0A6, Canada

⁸ Institute for Environment and Sustainability, Joint Research Center, European Commission, 20127 Ispra, Italy

⁹ Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

¹⁰ Forest Services, Autonomous Province of Bolzano, Via Brennero 6, 39100 Bolzano, Italy

¹¹ Faculty of Science and Technology, Free University of Bolzano, Piazza Università 5, 39100 Bolzano, Italy

¹² Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA

¹³ Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USA

¹⁴ Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark

¹⁵ Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Lenisky pr.33, Moscow 119071, Russia

¹⁶ Department for Innovation in Biological, Aro-food and Forest Systems, University of Tuscia, 01100 Viterbo, Italy

¹⁷ Department of Geography, University of Colorado, CO 80309, USA

¹⁸ Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China

¹⁹ State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China

²⁰ South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China

²¹ Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China

²² Jiangsu Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China

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Remote Sens. 2015, 7(3), 2238-2278; https://doi.org/10.3390/rs70302238 - 25 Feb 2015

Cited by 27 | Viewed by 10169

Abstract

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), [...] Read more.

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale. Full article

(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)

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4 pages, 602 KiB

Open AccessEditorial

Landsat-8 Sensor Characterization and Calibration

by Brian Markham^1,*,†, James Storey^2,*,† and Ron Morfitt^3,*,†

¹ NASA/GSFC, Code 618, Greenbelt, MD 20771, USA

² Stinger Ghaffarian Technologies (SGT), Technical Support Services Contractor to USGS EROS, NASA/GSFC Mail Code 618, Greenbelt, MD 20771, USA

³ USGS Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, USA

^† These authors contributed equally to this work.

Remote Sens. 2015, 7(3), 2279-2282; https://doi.org/10.3390/rs70302279 - 25 Feb 2015

Cited by 25 | Viewed by 14988

Abstract

Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational [...] Read more.

Landsat-8 was launched on 11 February 2013 with two new Earth Imaging sensors to provide a continued data record with the previous Landsats. For Landsat-8, pushbroom technology was adopted, and the reflective bands and thermal bands were split into two instruments. The Operational Land Imager (OLI) is the reflective band sensor and the Thermal Infrared Sensor (TIRS), the thermal. In addition to these fundamental changes, bands were added, spectral bandpasses were refined, dynamic range and data quantization were improved, and numerous other enhancements were implemented. As in previous Landsat missions, the National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) cooperated in the development, launch and operation of the Landsat-8 mission. One key aspect of this cooperation was in the characterization and calibration of the instruments and their data. This Special Issue documents the efforts of the joint USGS and NASA calibration team and affiliates to characterize the new sensors and their data for the benefit of the scientific and application users of the Landsat archive. A key scientific use of Landsat data is to assess changes in the land-use and land cover of the Earth’s surface over the now 43-year record. [...] Full article

(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)

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19 pages, 35218 KiB

Open AccessArticle

Remote Sensing of Shrubland Drying in the South-East Mediterranean, 1995–2010: Water-Use-Efficiency-Based Mapping of Biomass Change

by Maxim Shoshany^1,* and Lev Karnibad²

¹ Mapping and Geoinformation Engineering, Faculty of Civil & Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel

² Agricultural Engineering, Faculty of Civil & Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel

Remote Sens. 2015, 7(3), 2283-2301; https://doi.org/10.3390/rs70302283 - 26 Feb 2015

Cited by 21 | Viewed by 7001

Abstract

Recent climate studies of the South-Eastern Mediterranean indicate an increase in drought frequencies and decreasing water resources since the turn of the century. A four-phase methodology was developed for assessing above-ground biomass changes in shrublands caused by these recent trends. Firstly, we generalized [...] Read more.

Recent climate studies of the South-Eastern Mediterranean indicate an increase in drought frequencies and decreasing water resources since the turn of the century. A four-phase methodology was developed for assessing above-ground biomass changes in shrublands caused by these recent trends. Firstly, we generalized the function SB = 0.008MAP^1.54 describing the shrublands above-ground biomass (SB) dependence on mean annual precipitation (MAP) for areas of full shrub cover. Secondly, relationships between MAP and NDVI were formalized, allowing an estimation of precipitation levels from observed NDVI values (MAP_NDVI). Thirdly, relative water-use efficiency (RWUE) was defined as the ratio between MAP_NDVI and MAP. Finally, the function SB_RWUE = 0.008MAP^0.54^{+ RWUE} was formalized, utilizing RWUE in estimating shrublands biomass. This methodology was implemented using Landsat TM images (1994 to 2011) for an area between the Judean Mountains and the deserts bordering them to the east and south. More than 50% of the study area revealed low biomass change (±0.2 kg/m²), compared with 30% of the woodlands of the Jerusalem Mountains, where biomass increased between 0.2 and 1.4 kg/m² and with 50% of the semi-arid shrublands, where it decreased between 0.2 and 1.4 kg/m². These results suggest that aridity lines in southern Israel are migrating northwards. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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32 pages, 45522 KiB

Open AccessArticle

A Robust Photogrammetric Processing Method of Low-Altitude UAV Images

by Mingyao Ai^1,2, Qingwu Hu^1,*

, Jiayuan Li¹

, Ming Wang¹, Hui Yuan¹ and Shaohua Wang³

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

² State Key Laboratory of Information Engineering, Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

³ International School of Software, Wuhan University, Wuhan 430079, China

Remote Sens. 2015, 7(3), 2302-2333; https://doi.org/10.3390/rs70302302 - 26 Feb 2015

Cited by 58 | Viewed by 10920

Abstract

Low-altitude Unmanned Aerial Vehicles (UAV) images which include distortion, illumination variance, and large rotation angles are facing multiple challenges of image orientation and image processing. In this paper, a robust and convenient photogrammetric approach is proposed for processing low-altitude UAV images, involving a [...] Read more.

Low-altitude Unmanned Aerial Vehicles (UAV) images which include distortion, illumination variance, and large rotation angles are facing multiple challenges of image orientation and image processing. In this paper, a robust and convenient photogrammetric approach is proposed for processing low-altitude UAV images, involving a strip management method to automatically build a standardized regional aerial triangle (AT) network, a parallel inner orientation algorithm, a ground control points (GCPs) predicting method, and an improved Scale Invariant Feature Transform (SIFT) method to produce large number of evenly distributed reliable tie points for bundle adjustment (BA). A multi-view matching approach is improved to produce Digital Surface Models (DSM) and Digital Orthophoto Maps (DOM) for 3D visualization. Experimental results show that the proposed approach is robust and feasible for photogrammetric processing of low-altitude UAV images and 3D visualization of products. Full article

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18 pages, 935 KiB

Open AccessArticle

Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

by Ling Yu¹, Sheryl B. Ball², Christine E. Blinn³, Klaus Moeltner^1,*, Seth Peery⁴, Valerie A. Thomas³

and Randolph H. Wynne³

¹ Department of Agricultural and Applied Economics, Virginia Tech, 208 Hutcheson Hall (0401), 250 Drillfield Drive, Blacksburg, VA 24060, USA

² Department of Economics, Virginia Tech, 3016 Pamplin Hall (0316), Blacksburg, VA 24061, USA

³ Department of Forest Resources and Environmental Conservation, Virginia Tech, 310 West Campus Dr., Blacksburg, VA 24061, USA

⁴ Virginia Tech Information Technology, 1700 Pratt Drive (0214), Blacksburg, VA 24061, USA

Remote Sens. 2015, 7(3), 2334-2351; https://doi.org/10.3390/rs70302334 - 26 Feb 2015

Cited by 6 | Viewed by 7400

Abstract

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is [...] Read more.

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms. Full article

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21 pages, 51209 KiB

Open AccessArticle

Index of Soil Moisture Using Raw Landsat Image Digital Count Data in Texas High Plains

by Sanaz Shafian^1,* and Stephan J. Maas²

¹ Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA

² Department of Plant and Soil Science, Texas Tech University and Research Center, Texas A&M, Lubbock, TX 79409, USA

Remote Sens. 2015, 7(3), 2352-2372; https://doi.org/10.3390/rs70302352 - 26 Feb 2015

Cited by 59 | Viewed by 22033

Abstract

The growth and yield of crops in the arid and semi-arid regions of the world is driven by the amount of soil moisture available to the crop through rainfall and irrigation. Various methods have been developed for quantifying the soil moisture status of [...] Read more.

The growth and yield of crops in the arid and semi-arid regions of the world is driven by the amount of soil moisture available to the crop through rainfall and irrigation. Various methods have been developed for quantifying the soil moisture status of agricultural crops. Recent technological advances in remote sensing have shown that soil moisture can be measured with a variety of remote sensing techniques, each with its own strengths and weaknesses. In this study, building on of the strengths of multispectral satellite imagery, a new approach is suggested for estimating soil moisture content. A soil moisture index, the Perpendicular Soil Moisture Index (PSMI), is proposed; it is evaluated using raw image digital count (DC) data in the red, near-infrared, and thermal infrared spectral bands. To test this approach, soil moisture was measured in 18 agricultural fields in the semi-arid Texas High Plains over two years and compared to corresponding PSMI values determined from Landsat image data. These results showed that PSMI was strongly correlated (R² = 0.79) with observed soil moisture. It was further demonstrated that maps of PSMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region. While further study is needed to determine the exact relationship between PSMI and soil moisture in larger areas with different climates, this study suggests that PSMI is a good indicator of soil moisture and has potential for operationally monitoring soil moisture conditions at the field to regional scales. Full article

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28 pages, 17216 KiB

Open AccessArticle

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

by Isabel Pôças^1,2,*, Teresa A. Paço¹

, Paula Paredes¹, Mário Cunha^2,3 and Luís S. Pereira¹

¹ LEAF—Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal

² Centro de Investigação em Ciências Geo-Espaciais (CICGE), Rua do Campo Alegre, 4169-007 Porto, Portugal

³ Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal

Remote Sens. 2015, 7(3), 2373-2400; https://doi.org/10.3390/rs70302373 - 27 Feb 2015

Cited by 81 | Viewed by 9794

Abstract

A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (K_{cb VI}) considering a density coefficient (K_d) and a crop coefficient for bare soil. K_d is computed using the fraction of ground cover by [...] Read more.

A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (K_{cb VI}) considering a density coefficient (K_d) and a crop coefficient for bare soil. K_d is computed using the fraction of ground cover by vegetation (f_{c VI}), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (K_{c VI}) is also proposed by integrating the K_{cb VI} with the soil evaporation coefficient (K_e) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both f_{c VI} and K_{cb VI} compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual K_{cb VI} and K_{c VI} with actual K_cb and K_c obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. Full article

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30 pages, 33645 KiB

Open AccessArticle

Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

by Parth S. Roy^1,*,†

, Arijit Roy^2,†, Pawan K. Joshi^3,†, Manish P. Kale^4,†, Vijay K. Srivastava^5,†, Sushil K. Srivastava^2,†, Ravi S. Dwevidi^5,†, Chitiz Joshi^2,†, Mukunda D. Behera^6,†, Prasanth Meiyappan⁷, Yeshu Sharma¹, Atul K. Jain^7,*, Jamuna S. Singh⁸, Yajnaseni Palchowdhuri⁹, Reshma. M. Ramachandran¹, Bhavani Pinjarla¹, V. Chakravarthi¹, Nani Babu¹⁰, Mahalakshmi S. Gowsalya¹¹, Praveen Thiruvengadam¹¹, Mrinalni Kotteeswaran¹¹, Vishnu Priya¹², Krishna Murthy V. N. Yelishetty², Sandeep Maithani², Gautam Talukdar¹³, Indranil Mondal¹³, Krishnan S. Rajan¹⁴, Prasad S. Narendra¹⁵, Sushmita Biswal³, Anusheema Chakraborty³, Hitendra Padalia², Manoj Chavan⁴

, Satish N. Pardeshi⁴, Swapnil A. Chaudhari⁴, Arur Anand¹⁶, Anjana Vyas⁹, Mruthyunjaya K. Reddy¹⁷, M. Ramalingam¹⁸, R. Manonmani¹⁸, Pritiranjan Behera⁶, Pulakesh Das⁶, Poonam Tripathi⁶, Shafique Matin⁶, Mohammed L. Khan¹⁹, Om P. Tripathi²⁰, Jyotihman Deka²⁰, Prasanna Kumar²¹ and Deepak Kushwaha¹ Show full author list Hide full author list

¹ University Center for Earth and Space Science, University of Hyderabad, Prof. C R Rao Road, P.O. Central University, Hyderabad 500046, India

² Indian Institute of Remote Sensing, ISRO, 4 Kalidas Road, Dehradun 248001, India

³ Department of Natural Resources, TERI University, Plot No. 10 Institutional Area, Vasant Kunj, New Delhi 110070, India

⁴ CDAC 3rd Floor, RMZ Westend Center 3, Westend IT Park, Nagras Road, Aundh, Pune 411007, India

⁵ National Remote Sensing Center, Balanagar, Hyderabad 500037, India

⁶ Indian Institute of Technology, Kharagpur 721302, India

⁷ Departments of Atmospheric Sciences, University of Illinois, Urbana-Champaign, 105 S South Gregory Street, Urbana, IL 61801, USA

⁸ Department of Botany, BHU, Varanasi 221005, India

⁹ CEPT University, Kasturbhai Lalbhai Campus, University Rd, Navrangpura, Ahmadabad, Gujarat 380009, India

¹⁰ SACI WATERS, B-87, 3rd Avenue, Sainikpuri, Secunderabad-500-094, Telangana, India

¹¹ Anna University, Tirunelveli 627005, India

¹² Anna University, Kotturpuram, Chennai 600025, India

¹³ Wildlife Institute of India, Chandrabani, Dehradun 248171, India

¹⁴ IIIT, Gachibouwli, Hyderabad 500032, India

¹⁵ SACON, Coimbatore 641108, India

¹⁶ RRSC-C, NRSC (ISRO), Amravati Road, Nagpur 440033, India

¹⁷ APSRAC, Chinthal Basthi, Hyderabad 500038, India

¹⁸ IRS Anna University, Chennai 600025, India

¹⁹ Dr. Harisingh Gour Central University, Sagar 470003, India

²⁰ NERIST, National Highway 52A, Nirjuli, Arunachal Pradesh 791109, India

²¹ ORSAC, Plot No. 45/48, Jayadev Vihar, Bhubaneshwar 751023, India

^† These authors contributed equally to this work.

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Remote Sens. 2015, 7(3), 2401-2430; https://doi.org/10.3390/rs70302401 - 27 Feb 2015

Cited by 242 | Viewed by 32609

Abstract

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources [...] Read more.

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study. Full article

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18 pages, 5445 KiB

Open AccessArticle

Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

by Ehsan H. Chowdhury and Quazi K. Hassan^*

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada

Remote Sens. 2015, 7(3), 2431-2448; https://doi.org/10.3390/rs70302431 - 2 Mar 2015

Cited by 39 | Viewed by 7664

Abstract

Forest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing [...] Read more.

Forest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009–2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (T_S), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The T_S, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under “extremely high” to “moderate” danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape. Full article

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22 pages, 14189 KiB

Open AccessArticle

Climate Contributions to Vegetation Variations in Central Asian Drylands: Pre- and Post-USSR Collapse

by Yu Zhou^1,2, Li Zhang^1,*, Rasmus Fensholt³

, Kun Wang¹, Irina Vitkovskaya⁴ and Feng Tian^3,5

¹ Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

² University of Chinese Academy of Sciences (UCAS), No.19A Yuquan Road, Beijing 100049, China

³ Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark

⁴ National Centre of Space Research and Technology of the Republic of Kazakhstan, Almaty 050010, Kazakhstan

⁵ School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Remote Sens. 2015, 7(3), 2449-2470; https://doi.org/10.3390/rs70302449 - 2 Mar 2015

Cited by 125 | Viewed by 16235

Abstract

Central Asia comprises a large fraction of the world’s drylands, known to be vulnerable to climate change. We analyzed the inter-annual trends and the impact of climate variability in the vegetation greenness for Central Asia from 1982 to 2011 using GIMMS3g normalized difference [...] Read more.

Central Asia comprises a large fraction of the world’s drylands, known to be vulnerable to climate change. We analyzed the inter-annual trends and the impact of climate variability in the vegetation greenness for Central Asia from 1982 to 2011 using GIMMS3g normalized difference vegetation index (NDVI) data. In our study, most areas showed an increasing trend during 1982–1991, but experienced a significantly decreasing trend for 1992–2011. Vegetation changes were closely coupled to climate variables (precipitation and temperature) during 1982–1991 and 1992–2011, but the response trajectories differed between these two periods. The warming trend in Central Asia initially enhanced the vegetation greenness before 1991, but the continued warming trend subsequently became a suppressant of further gains in greenness afterwards. Precipitation expanded its influence on larger vegetated areas in 1992–2011 when compared to 1982–1991. Moreover, the time-lag response of plants to rainfall tended to increase after 1992 compared to the pre-1992 period, indicating that plants might have experienced functional transformations to adapt the climate change during the study period. The impact of climate on vegetation was significantly different for the different sub-regions before and after 1992, coinciding with the collapse of the Union of Soviet Socialist Republics (USSR). It was suggested that these spatio-temporal patterns in greenness change and their relationship with climate change for some regions could be explained by the changes in the socio-economic structure resulted from the USSR collapse in late 1991. Our results clearly illustrate the combined influence of climatic/anthropogenic contributions on vegetation growth in Central Asian drylands. Due to the USSR collapse, this region represents a unique case study of the vegetation response to climate changes under different climatic and socio-economic conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)

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3 pages, 596 KiB

Open AccessBook Review

A Maxwellian Look beyond Opaque Interfaces

by Nicola Masini

CNR-IBAM, C.da S. Loya, 85050 Tito (PZ), Italy

Remote Sens. 2015, 7(3), 2471-2473; https://doi.org/10.3390/rs70302471 - 2 Mar 2015

Viewed by 3657

Abstract

I wonder if James Clerk Maxwell, Scottish mathematical physicist and father of the classical theory of electromagnetic radiation, could have imagined being included on the cover of a book dealing with a sensing technology used to locate the position of buried pipes, to [...] Read more.

I wonder if James Clerk Maxwell, Scottish mathematical physicist and father of the classical theory of electromagnetic radiation, could have imagined being included on the cover of a book dealing with a sensing technology used to locate the position of buried pipes, to analyze the integrity of buildings, and to uncover ancient archaeological sites [...] Full article

35 pages, 1323 KiB

Open AccessArticle

A Region-Based GeneSIS Segmentation Algorithm for the Classification of Remotely Sensed Images

by Stelios K. Mylonas¹, Dimitris G. Stavrakoudis²

, John B. Theocharis^1,* and Paris A. Mastorocostas³

¹ Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

² Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece

³ Department of Computer Engineering, Technological Education Institute of Central Macedonia, Serres 62124, Greece

Remote Sens. 2015, 7(3), 2474-2508; https://doi.org/10.3390/rs70302474 - 3 Mar 2015

Cited by 8 | Viewed by 8906

Abstract

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted [...] Read more.

This paper proposes an object-based segmentation/classification scheme for remotely sensed images, based on a novel variant of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration a single object is extracted via a genetic-based object extraction algorithm. Contrary to the previous pixel-based GeneSIS where the candidate objects to be extracted were evaluated through the fuzzy content of their included pixels, in the newly developed region-based GeneSIS algorithm, a watershed-driven fine segmentation map is initially obtained from the original image, which serves as the basis for the forthcoming GeneSIS segmentation. Furthermore, in order to enhance the spatial search capabilities, we introduce a more descriptive encoding scheme in the object extraction algorithm, where the structural search modules are represented by polygonal shapes. Our objectives in the new framework are posed as follows: enhance the flexibility of the algorithm in extracting more flexible object shapes, assure high level classification accuracies, and reduce the execution time of the segmentation, while at the same time preserving all the inherent attributes of the GeneSIS approach. Finally, exploiting the inherent attribute of GeneSIS to produce multiple segmentations, we also propose two segmentation fusion schemes that operate on the ensemble of segmentations generated by GeneSIS. Our approaches are tested on an urban and two agricultural images. The results show that region-based GeneSIS has considerably lower computational demands compared to the pixel-based one. Furthermore, the suggested methods achieve higher classification accuracies and good segmentation maps compared to a series of existing algorithms. Full article

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34 pages, 49293 KiB

Open AccessArticle

Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

by Giulia F. Curatola Fernández^1,*, Wolfgang A. Obermeier¹, Andrés Gerique², María Fernanda López Sandoval³, Lukas W. Lehnert¹, Boris Thies¹ and Jörg Bendix¹

¹ Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg, Deutschhausstr. 12, Marburg 35032, Germany

² Institute of Geography, University of Erlangen-Nürnberg, Wetterkreuz 15, Erlangen 91058, Germany

³ Department of Development, Environment, and Territory, Latin American Faculty of Social Sciences—FLACSO Ecuador, Calle La Pradera E7-174 y Av. Diego de Almagro, Quito 170516, Ecuador

Remote Sens. 2015, 7(3), 2509-2542; https://doi.org/10.3390/rs70302509 - 3 Mar 2015

Cited by 85 | Viewed by 13148

Abstract

In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and [...] Read more.

In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and the provisioning of ecosystem services. To find sustainable land use options adapted to the local situation, a profound knowledge of the long-term spatiotemporal patterns of land cover change and its drivers is necessary, but hitherto lacking. The complex topography and the high cloud frequency make the use of remote sensing in this area a challenge. To deal with these conditions, we pursued specific pre-processing steps before classifying five Landsat scenes from 1975 to 2001. Then, we quantified land cover changes and habitat fragmentation, and we investigated landscape changes in relation to key spatial elements (altitude, slope, and distance from roads). Good classification results were obtained with overall accuracies ranging from 94.5% to 98.5% and Kappa statistics between 0.75 and 0.98. Forest was strongly fragmented due to the rapid expansion of the arable frontier and the even more rapid invasion by bracken. Unexpectedly, more bracken-infested areas were converted to pastures than vice versa, a practice that could alleviate pressure on forests if promoted. Road proximity was the most important spatial element determining forest loss, while for bracken the altitudinal range conditioned the degree of invasion in deforested areas. The annual deforestation rate changed notably between periods: ~1.5% from 1975 to 1987, ~0.8% from 1987 to 2000, and finally a very high rate of ~7.5% between 2000 and 2001. We explained these inconstant rates through some specific interrelated local and national political and socioeconomic drivers, namely land use policies, credit and tenure incentives, demography, and in particular, a severe national economic and bank crisis. Full article

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59 pages, 10130 KiB

Open AccessArticle

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

by Guiwu Su^1,*, Wenhua Qi¹, Suling Zhang², Timothy Sim³, Xinsheng Liu^4,5, Rui Sun^4,5

, Lei Sun¹ and Yifan Jin¹

¹ Institute of Geology, China Earthquake Administration, Beijing 100029, China

² China Earthquake Networks Center, China Earthquake Administration, Beijing 100045, China

³ Department of Applied Social Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China

⁴ School of Geography, Beijing Normal University, Beijing 100875, China

⁵ State Key Laboratory of Remote Sensing Science, Co-Sponsored by Beijing Normal University and RADI, Beijing 100875, China

Remote Sens. 2015, 7(3), 2543-2601; https://doi.org/10.3390/rs70302543 - 4 Mar 2015

Cited by 30 | Viewed by 8683

Abstract

Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured [...] Read more.

Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local knowledge to resolve this problem. This method includes two key interdependent steps. (1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face. Full article

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25 pages, 16155 KiB

Open AccessArticle

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

by Xiaomin Du^1,2, Sergio Bernardes^3,*, Daiyong Cao¹, Thomas R. Jordan², Zhen Yan⁴, Guang Yang¹ and Zhipeng Li⁵

¹ School of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

² Center for Geospatial Research, Department of Geography, The University of Georgia, Athens, GA 30602, USA

³ Biospheric Sciences Laboratory, The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁴ Department of Statistics, The University of Georgia, Athens, GA 30602, USA

⁵ College of Resources and Environment, The University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2015, 7(3), 2602-2626; https://doi.org/10.3390/rs70302602 - 5 Mar 2015

Cited by 12 | Viewed by 6144

Abstract

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by [...] Read more.

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work “Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 1, Methodology”. The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%–85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site. Full article

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20 pages, 7123 KiB

Open AccessArticle

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

by Leila Hassan-Esfahani^*, Alfonso Torres-Rua

, Austin Jensen and Mac McKee

Utah Water Research Laboratory, Utah State University, 8200 Old Main Hill, Logan, UT 84341, USA

Remote Sens. 2015, 7(3), 2627-2646; https://doi.org/10.3390/rs70302627 - 5 Mar 2015

Cited by 226 | Viewed by 18947

Abstract

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy [...] Read more.

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R²) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system. Full article

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21 pages, 19239 KiB

Open AccessArticle

Combination of Well-Logging Temperature and Thermal Remote Sensing for Characterization of Geothermal Resources in Hokkaido, Northern Japan

by Bingwei Tian^1,2, Ling Wang³, Koki Kashiwaya¹ and Katsuaki Koike^1,*

¹ Department of Urban Management, Graduate School of Engineering, Kyoto University, Kyoto 6158540, Japan

² Institute for Disaster Management and Reconstruction, Sichuan University–Hong Kong Polytechnic University, Chengdu 610207, China

³ Chengdu Institute of Biology, Chinese Academy of Sciences, P.O. Box 416, Chengdu 610041, China

Remote Sens. 2015, 7(3), 2647-2667; https://doi.org/10.3390/rs70302647 - 6 Mar 2015

Cited by 25 | Viewed by 11648

Abstract

Geothermal resources have become an increasingly important source of renewable energy for electrical power generation worldwide. Combined Three Dimension (3D) Subsurface Temperature (SST) and Land Surface Temperature (LST) measurements are essential for accurate assessment of geothermal resources. In this study, subsurface and surface [...] Read more.

Geothermal resources have become an increasingly important source of renewable energy for electrical power generation worldwide. Combined Three Dimension (3D) Subsurface Temperature (SST) and Land Surface Temperature (LST) measurements are essential for accurate assessment of geothermal resources. In this study, subsurface and surface temperature distributions were combined using a dataset comprised of well logs and Thermal Infrared Remote sensing (TIR) images from Hokkaido island, northern Japan. Using 28,476 temperature data points from 433 boreholes sites and a method of Kriging with External Drift or trend (KED), SST distribution model from depths of 100 to 1500 m was produced. Regional LST was estimated from 13 scenes of Landsat 8 images. Resultant SST ranged from around 50 °C to 300 °C at a depth of 1500 m. Most of western and part of the eastern Hokkaido are characterized by high temperature gradients, while low temperatures were found in the central region. Higher temperatures in shallower crust imply the western region and part of the eastern region have high geothermal potential. Moreover, several LST zones considered to have high geothermal potential were identified upon clarification of the underground heat distribution according to 3D SST. LST in these zones showed the anomalies, 3 to 9 °C higher than the surrounding areas. These results demonstrate that our combination of TIR and 3D temperature modeling using well logging and geostatistics is an efficient and promising approach to geothermal resource exploration. Full article

(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)

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24 pages, 3330 KiB

Open AccessArticle

A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images

by Olivier Hagolle^1,*, Mireille Huc¹, David Villa Pascual² and Gerard Dedieu¹

¹ Centre d’études Spatiales de la Biosphère, CESBIO Unite mixte Université de Toulouse-CNES-CNRS-IRD, 18 avenue E.Belin, 31401 Toulouse Cedex 9, France

² Airbus DS—Space Systems, 31 rue des Cosmonautes, 31402 Toulouse, France

Remote Sens. 2015, 7(3), 2668-2691; https://doi.org/10.3390/rs70302668 - 9 Mar 2015

Cited by 244 | Viewed by 16307

Abstract

The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of [...] Read more.

The correction of atmospheric effects is one of the preliminary steps required to make quantitative use of time series of high resolution images from optical remote sensing satellites. An accurate atmospheric correction requires good knowledge of the aerosol optical thickness (AOT) and of the aerosol type. As a first step, this study compares the performances of two kinds of AOT estimation methods applied to FormoSat-2 and LandSat time series of images: a multi-spectral method that assumes a constant relationship between surface reflectance measurements and a multi-temporal method that assumes that the surface reflectances are stable with time. In a second step, these methods are combined to obtain more accurate and robust estimates. The estimated AOTs are compared to in situ measurements on several sites of the AERONET (Aerosol Robotic Network). The methods, based on either spectral or temporal criteria, provide accuracies better than 0.07 in most cases, but show degraded accuracies in some special cases, such as the absence of vegetation for the spectral method or a very quick variation of landscape for the temporal method. The combination of both methods in a new spectro-temporal method increases the robustness of the results in all cases. Full article

(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)

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23 pages, 31720 KiB

Open AccessArticle

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

by Andrés Ceballos¹, Jaime Hernández¹

, Patricio Corvalán¹ and Mauricio Galleguillos^1,2,*

¹ Laboratory of Geomatics and Landscape Ecology, Forestry and Nature Conservation Faculty, University of Chile, Av. Santa Rosa 11315, La Pintana, Santiago, Chile

² Department of Environmental Sciences, Faculty of Agronomic Sciences, University of Chile, Av. Santa Rosa 11315, La Pintana, Santiago, Chile

Remote Sens. 2015, 7(3), 2692-2714; https://doi.org/10.3390/rs70302692 - 9 Mar 2015

Cited by 30 | Viewed by 9758

Abstract

The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. [...] Read more.

The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m²each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R²= 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R²= 0.571, RMSE = 5.05). Selected variables were statistically significant: catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power. Full article

(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)

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16 pages, 1196 KiB

Open AccessArticle

Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

by Jonathan Bennie^*,†, James P. Duffy^†, Thomas W. Davies, Maria Eugenia Correa-Cano and Kevin J. Gaston

¹ Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, Cornwall TR10 9FE, UK

^† These authors contributed equally to this work.

Remote Sens. 2015, 7(3), 2715-2730; https://doi.org/10.3390/rs70302715 - 9 Mar 2015

Cited by 158 | Viewed by 21040

Abstract

The rapid growth in electric light usage across the globe has led to increasing presence of artificial light in natural and semi-natural ecosystems at night. This occurs both due to direct illumination and skyglow - scattered light in the atmosphere. There is increasing [...] Read more.

The rapid growth in electric light usage across the globe has led to increasing presence of artificial light in natural and semi-natural ecosystems at night. This occurs both due to direct illumination and skyglow - scattered light in the atmosphere. There is increasing concern about the effects of artificial light on biological processes, biodiversity and the functioning of ecosystems. We combine intercalibrated Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) images of stable night-time lights for the period 1992 to 2012 with a remotely sensed landcover product (GLC2000) to assess recent changes in exposure to artificial light at night in 43 global ecosystem types. We find that Mediterranean-climate ecosystems have experienced the greatest increases in exposure, followed by temperate ecosystems. Boreal, Arctic and montane systems experienced the lowest increases. In tropical and subtropical regions, the greatest increases are in mangroves and subtropical needleleaf and mixed forests, and in arid regions increases are mainly in forest and agricultural areas. The global ecosystems experiencing the greatest increase in exposure to artificial light are already localized and fragmented, and often of particular conservation importance due to high levels of diversity, endemism and rarity. Night time remote sensing can play a key role in identifying the extent to which natural ecosystems are exposed to light pollution. Full article

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

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21 pages, 46437 KiB

Open AccessArticle

Normalization of Echo Features Derived from Full-Waveform Airborne Laser Scanning Data

by Yu-Ching Lin

Department of Environmental Information and Engineering, Chung Cheng Institute of Technology, National Defense University, No.75, Shiyuan Rd., Taoyuan, Taiwan

Remote Sens. 2015, 7(3), 2731-2751; https://doi.org/10.3390/rs70302731 - 9 Mar 2015

Cited by 9 | Viewed by 5717

Abstract

Full-waveform airborne laser scanning systems provide fundamental observations for each echo, such as the echo width and amplitude. Geometric and physical information about illuminated surfaces are simultaneously provided by a single scanner. However, there are concerns about whether the physical meaning of observations [...] Read more.

Full-waveform airborne laser scanning systems provide fundamental observations for each echo, such as the echo width and amplitude. Geometric and physical information about illuminated surfaces are simultaneously provided by a single scanner. However, there are concerns about whether the physical meaning of observations is consistent among different scanning missions. Prior to the application of waveform features for multi-temporal data classification, such features must be normalized. This study investigates the transferability of normalized waveform features to different surveys. The backscatter coefficient is considered to be a normalized physical feature. A normalization process for the echo width, which is a geometric feature, is proposed. The process is based on the coefficient of variation of the echo widths in a defined neighborhood, for which the Fuzzy Small membership function is applied. The normalized features over various land cover types and flight missions are investigated. The effects of different feature combinations on the classification accuracy are analyzed. The overall accuracy of the combination of normalized features and height-based attributes achieves promising results (>93% overall accuracy for ground, roof, low vegetation, and tree canopy) when different flight missions and classifiers are used. Nevertheless, the combination of all possible features, including raw features, normalized features, and height-based features, performs less well and yields inconsistent results. Full article

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29 pages, 2373 KiB

Open AccessArticle

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

by Reza Hosseini¹, Nathaniel K. Newlands^2,*, Charmaine B. Dean³ and Akimichi Takemura⁴

¹ IBM Research Collaboratory, 9 Changi Business Park Central 1, Singapore 486048, Singapore

² Science and Technology, Agriculture and Agri-Food Canada, Lethbridge Research Centre, 5403 1st Avenue South, P.O. Box 3000, Lethbridge, AB T1J 4B1, Canada

³ Department of Statistics and Actuarial Sciences, University of Western Ontario, 262 Western Science Centre, 1151 Richmond Street, London, ON N6A 5B7, Canada

⁴ Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan

Remote Sens. 2015, 7(3), 2752-2780; https://doi.org/10.3390/rs70302752 - 10 Mar 2015

Cited by 24 | Viewed by 7081

Abstract

We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often [...] Read more.

We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field- to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support. Full article

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27 pages, 4899 KiB

Open AccessArticle

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

by Gillian V. Lui and David A. Coomes^*

Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK

Remote Sens. 2015, 7(3), 2781-2807; https://doi.org/10.3390/rs70302781 - 10 Mar 2015

Cited by 22 | Viewed by 10643

Abstract

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and [...] Read more.

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. Full article

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24 pages, 19737 KiB

Open AccessArticle

Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany)

by Muhammad Ali^1,*, Carsten Montzka¹

, Anja Stadler², Gunter Menz³, Frank Thonfeld⁴

and Harry Vereecken¹

¹ Agrosphere (IBG-3), Research Center Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany

² Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany

³ Remote Sensing Research Group, Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany

⁴ Centre for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Walter-Flex-Straße 3, 53115 Bonn, Germany

Remote Sens. 2015, 7(3), 2808-2831; https://doi.org/10.3390/rs70302808 - 10 Mar 2015

Cited by 46 | Viewed by 14315

Abstract

Leaf Area Index (LAI) is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being [...] Read more.

Leaf Area Index (LAI) is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being labor intensive and site specific. For spatial-explicit applications (from regional to continental scales), satellite remote sensing is a promising source for obtaining LAI with different spatial resolutions. However, satellite-derived LAI measurements using empirical models require calibration and validation with the in situ measurements. In this study, we attempted to validate a direct LAI retrieval method from remotely sensed images (RapidEye) with in situ LAI (LAI_destr). Remote sensing LAI (LAI_rapideye) were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). Additionally, applicability of the newly available red-edge band (RE) was also analyzed through Normalized Difference Red-Edge index (NDRE) and Soil Adjusted Red-Edge index (SARE). The LAI_rapideye obtained from vegetation indices with red-edge band showed better correlation with LAI_destr (r = 0.88 and Root Mean Square Devation, RMSD = 1.01 & 0.92). This study also investigated the need to apply radiometric/atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the the RapidEye Level 3A data set showed that application of the radiometric/atmospheric correction did not improve correlation of the estimated LAI with in situ LAI. Full article

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18 pages, 5759 KiB

Open AccessArticle

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

by Nasem Badreldin and Arturo Sanchez-Azofeifa^*

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB T6G 2E3, Canada

Remote Sens. 2015, 7(3), 2832-2849; https://doi.org/10.3390/rs70302832 - 10 Mar 2015

Cited by 54 | Viewed by 11009

Abstract

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the [...] Read more.

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest’s biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (H_i) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB₉ (Jenkins allometric equation) and HY₉ (Lambert and Ung allometric equation) proved to be the best models (r² = 0.78; root mean square error (RMSE) = 44 Mg/H, r² = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change: 1988, 1990, 2001, 2005, and 2011. Additionally, four pixel-based image comparisons were analyzed (i.e., 1988–1990, 1990–2005, 2005–2009, and 2009–2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance. Full article

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21 pages, 46969 KiB

Open AccessArticle

A New Global Climatology of Annual Land Surface Temperature

by Benjamin Bechtel

Institute of Geography, University of Hamburg, Bundesstraße 55, 20146 Hamburg, Germany

Remote Sens. 2015, 7(3), 2850-2870; https://doi.org/10.3390/rs70302850 - 10 Mar 2015

Cited by 119 | Viewed by 12643

Abstract

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and [...] Read more.

Land surface temperature (LST) is an important parameter in various fields including hydrology, climatology, and geophysics. Its derivation by thermal infrared remote sensing has long tradition but despite substantial progress there remain limited data availability and challenges like emissivity estimation, atmospheric correction, and cloud contamination. The annual temperature cycle (ATC) is a promising approach to ease some of them. The basic idea to fit a model to the ATC and derive annual cycle parameters (ACP) has been proposed before but so far not been tested on larger scale. In this study, a new global climatology of annual LST based on daily 1 km MODIS/Terra observations was processed and evaluated. The derived global parameters were robust and free of missing data due to clouds. They allow estimating LST patterns under largely cloud-free conditions at different scales for every day of year and further deliver a measure for its accuracy respectively variability. The parameters generally showed low redundancy and mostly reflected real surface conditions. Important influencing factors included climate, land cover, vegetation phenology, anthropogenic effects, and geology which enable numerous potential applications. The datasets will be available at the CliSAP Integrated Climate Data Center pending additional processing. Full article

(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)

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28 pages, 11842 KiB

Open AccessArticle

Gradient-Based Assessment of Habitat Quality for Spectral Ecosystem Monitoring

by Carsten Neumann^1,*

, Gabriele Weiss², Sebastian Schmidtlein³, Sibylle Itzerott¹, Angela Lausch⁴

, Daniel Doktor⁴ and Maximilian Brell¹

¹ Helmholtz Center Potsdam, German Research Center for Geosciences, Telegrafenberg, Potsdam 14473, Germany

² Ecostrat GmbH Berlin, Marschnerstraße 10, Berlin 12203, Germany

³ Karlsruhe Institute of Technology (KIT), Institute of Geography and Geoecology, Karlsruhe 76131, Germany

⁴ Helmholtz Center for Environmental Research-UFZ, Permoserstr 15, Leipzig 04318, Germany

Remote Sens. 2015, 7(3), 2871-2898; https://doi.org/10.3390/rs70302871 - 10 Mar 2015

Cited by 36 | Viewed by 9451

Abstract

The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our [...] Read more.

The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R² = 0.79–0.85), whereas second axis of dry heaths (R² = 0.13) and first axis for pioneer grasslands (R² = 0.49) are more difficult to describe. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)

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27 pages, 8785 KiB

Open AccessArticle

Dynamics of Land Cover/Land Use Changes in the Mekong Delta, 1973–2011: A Remote Sensing Analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam

by Hanh Tran^1,2,*, Thuc Tran³ and Matthieu Kervyn¹

¹ Department of Geography, Earth System Science, Vrije Universiteit Brussel, B-1050 Brussels, Belgium

² Faculty of Surveying and Mapping, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam

³ Vietnam Institute of Meteorology, Hydrology and Environment, Hanoi 10000, Vietnam

Remote Sens. 2015, 7(3), 2899-2925; https://doi.org/10.3390/rs70302899 - 12 Mar 2015

Cited by 86 | Viewed by 15305

Abstract

The main objective of this study is to assess the spatio-temporal dynamics of land cover/land use changes in the lower Mekong Delta over the last 40 years with the coastal Tran Van Thoi District of Ca Mau Province, Vietnam as a case study. [...] Read more.

The main objective of this study is to assess the spatio-temporal dynamics of land cover/land use changes in the lower Mekong Delta over the last 40 years with the coastal Tran Van Thoi District of Ca Mau Province, Vietnam as a case study. Land cover/land use change dynamics are derived from moderate to high spatial resolution (Landsat and SPOT) satellite imagery in six time intervals ranging from 1973 to 2011. Multi-temporal satellite images were collected, georeferenced, classified using per-pixel method, validated, and compared in post classification for the land use/land cover change detection in decades. Seven major land cover/land use classes were obtained, including cultivated lands, aquaculture ponds, mangrove forest, melaleuca forest, built up areas, bare lands, and natural water bodies. The accuracies of the land cover/land use maps for 1973, 1979, 1989, 1995, 2004, and 2011 were 81%, 82%, 86%, 87%, 89%, and 89%, respectively. The results show that the area of cultivated lands reduced over the period 1973–2011, however, it still represents the dominant land use in the case study. Aquaculture ponds were almost absent in 1973 but greatly increased from 1995 to 2004, to represent 20% of the land surface in 2011. Overall, from 1973 to 2011, bare lands, cultivated lands, mangrove forest, and melaleuca forest decreased by 104 km², 77 km², 61 km², and 5 km², respectively. In contrast, aquaculture lands and built up areas increased by 123 km² and 120 km², respectively. Temporal analysis highlights that these changes took place mostly between 1995 and 2004. This study is a first step to identify the main drivers of land use changes in this delta region, which include economical policies as well as demographic, socio-economic, and environmental changes. Full article

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16 pages, 6118 KiB

Open AccessArticle

Human-Induced Landcover Changes Drive a Diminution of Land Surface Albedo in the Loess Plateau (China)

by Jun Zhai^1,2

, Ronggao Liu², Jiyuan Liu², Lin Huang^2,* and Yuanwei Qin³

¹ Satellite Environmental Center, Ministry of Environmental Protection, Beijing 100094, China

² Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

³ Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA

Remote Sens. 2015, 7(3), 2926-2941; https://doi.org/10.3390/rs70302926 - 12 Mar 2015

Cited by 33 | Viewed by 8784

Abstract

A large decrease in the land surface albedo of the Loess Plateau was observed from 2000 to 2010, as measured using satellite imagery. In particular, ecological restoration program regions experienced a decrease in peak season land surface albedo exceeding 0.05. In this study, [...] Read more.

A large decrease in the land surface albedo of the Loess Plateau was observed from 2000 to 2010, as measured using satellite imagery. In particular, ecological restoration program regions experienced a decrease in peak season land surface albedo exceeding 0.05. In this study, we examined the spatial and temporal patterns of variation during the peak season albedo in the Loess Plateau and analyzed its relationships with changes of anthropogenic and natural factors at the pixel level. Our analysis revealed that increasing grassland coverage due to returning rangeland to grassland could lead to a maximum albedo decrease of 0.030 in peak season. This result highlighted the human-induced land use change in driving the decreasing albedo on an annual scale. There was no significant correlation between precipitation change and albedo reduction. Precipitation could influence the spatial pattern of albedo in drought years by influencing the natural vegetation water requirement. However, the role of precipitation was not obvious in the ecological restoration program regions. This article demonstrates the substantial role that land use change could play in regional-scale albedo change and climate. Finally, some implications for the radiative forcing of land use change are discussed. Full article

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10 pages, 16163 KiB

Open AccessLetter

Validation of a Simplified Model to Generate Multispectral Synthetic Images

by Ion Sola^*

, María González-Audícana and Jesús Álvarez-Mozos

Department of Projects and Rural Engineering, Campus Arrosadía, Public University of Navarre, 31006 Pamplona, Spain

Remote Sens. 2015, 7(3), 2942-2951; https://doi.org/10.3390/rs70302942 - 12 Mar 2015

Cited by 9 | Viewed by 5241

Abstract

A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from [...] Read more.

A new procedure to assess the quality of topographic correction (TOC) algorithms applied to remote sensing imagery was previously proposed by the authors. This procedure was based on a model that simulated synthetic scenes, representing the radiance an optical sensor would receive from an area under some specific conditions. TOC algorithms were then applied to synthetic scenes and the resulting corrected scenes were compared with a horizontal synthetic scene free of topographic effect. This comparison enabled an objective and quantitative evaluation of TOC algorithms. This approach showed promising results but had some shortcomings that are addressed herein. First, the model, originally built to simulate only broadband panchromatic scenes, is extended to multispectral scenes in the visible, near infrared (NIR), and short wave infrared (SWIR) bands. Next, the model is validated by comparing synthetic scenes with four Satellite pour l'Observation de la Terre 5 (SPOT5) real scenes acquired on different dates and different test areas along the Pyrenees mountain range (Spain). The results obtained show a successful simulation of all the spectral bands. Therefore, the model is deemed accurate enough for its purpose of evaluating TOC algorithms. Full article

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19 pages, 1256 KiB

Open AccessArticle

ASTC-MIMO-TOPS Mode with Digital Beam-Forming in Elevation for High-Resolution Wide-Swath Imaging

by Pingping Huang^1,*

and Wei Xu²

¹ College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China

² Department of Spaceborne Microwave Remote Sensing, Institute of Electronics, Chinese Academy of Sciences (IECAS), Beijing 100190, China

Remote Sens. 2015, 7(3), 2952-2970; https://doi.org/10.3390/rs70302952 - 13 Mar 2015

Cited by 7 | Viewed by 8213

Abstract

Future spaceborne synthetic aperture radar (SAR) missions require complete and frequent coverage of the earth with a high resolution. Terrain Observation by Progressive Scans (TOPS) is a novel wide swath mode but has impaired azimuth resolution. In this paper, an innovative extended TOPS [...] Read more.

Future spaceborne synthetic aperture radar (SAR) missions require complete and frequent coverage of the earth with a high resolution. Terrain Observation by Progressive Scans (TOPS) is a novel wide swath mode but has impaired azimuth resolution. In this paper, an innovative extended TOPS mode named Alamouti Space-time Coding multiple-input multiple-output TOPS (ASTC-MIMO-TOPS) mode combined with digital beam-forming (DBF) in elevation and multi-aperture SAR signal reconstruction in azimuth is proposed. This innovative mode achieves wide-swath coverage with a high geometric resolution and also overcomes major drawbacks in conventional MIMO SAR systems. The data processing scheme of this imaging scheme is presented in detail. The designed system example of the proposed ASTC-MIMO-TOPS mode, which has the imaging capacity of a 400 km wide swath with an azimuth resolution of 3 m, is given. Its system performance analysis results and simulated imaging results on point targets demonstrate the potential of the proposed novel spaceborne SAR mode for high-resolution wide-swath (HRWS) imaging. Full article

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20 pages, 19918 KiB

Open AccessArticle

Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture

by Alessandro Matese^1,*

, Piero Toscano¹

, Salvatore Filippo Di Gennaro^1,2

, Lorenzo Genesio¹, Francesco Primo Vaccari¹

, Jacopo Primicerio^1,3, Claudio Belli⁴, Alessandro Zaldei¹

, Roberto Bianconi⁴ and Beniamino Gioli¹

¹ IBIMET CNR–Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, via G. Caproni 8, 50145 Firenze, Italy

² DSAA-Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Università di Perugia, Borgo XX Giugno 7, 06123 Perugia, Italy

³ Dipartimento di Scienze Agrarie, Forestali e Agroalimentari, Università di Torino, Via Leonardo Da Vinci 44, 10095 Grugliasco, Italy

⁴ Terrasystems.r.l., Via Pacinotti, 5, 01100 Viterbo, Italy

Remote Sens. 2015, 7(3), 2971-2990; https://doi.org/10.3390/rs70302971 - 13 Mar 2015

Cited by 558 | Viewed by 36515

Abstract

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to [...] Read more.

Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops)

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29 pages, 5293 KiB

Open AccessArticle

Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

by András Zlinszky^1,2,*, Balázs Deák³, Adam Kania⁴, Anke Schroiff⁵ and Norbert Pfeifer²

¹ Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Klebelsberg Kuno út 3, Tihany 8237, Hungary

² Vienna University of Technology, Department of Geodesy and Geoinformation, Research Groups Photogrammetry and Remote Sensing, Gußhausstraße 27–29, Vienna 1040, Austria

³ MTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem tér 1, Debrecen 4032, Hungary

⁴ ATMOTERM S.A., ul. Łangowskiego 4, Opole 45-031, Poland

⁵ YggdrasilDiemer, Dudenstr. 38, Berlin 10965, Germany

Remote Sens. 2015, 7(3), 2991-3019; https://doi.org/10.3390/rs70302991 - 13 Mar 2015

Cited by 40 | Viewed by 12768

Abstract

Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility [...] Read more.

Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility with the traditional assessment methodology has not been achieved yet. We aimed to test Airborne Laser Scanning (ALS) as a source for mapping all variables required by the local official conservation status assessment scheme and to develop an automated method that calculates Natura 2000 conservation status at 0.5 m raster resolution for 24 km² of Pannonic Salt Steppe habitat (code 1530). We used multi-temporal (summer and winter) ALS point clouds with full-waveform recording and a density of 10 pt/m². Some required variables were derived from ALS product rasters; others involved vegetation classification layers calculated by machine learning and fuzzy categorization. Thresholds separating favorable and unfavorable values of each variable required by the national assessment scheme were manually calibrated from 10 plots where field-based assessment was carried out. Rasters representing positive and negative scores for each input variable were integrated in a ruleset that exactly follows the Hungarian Natura 2000 assessment scheme for grasslands. Accuracy of each parameter and the final conservation status score and category was evaluated by 10 independent assessment plots. We conclude that ALS is a suitable data source for Natura 2000 assessments in grasslands, and that the national grassland assessment scheme can successfully be used as a GIS processing model for conservation status, ensuring that the output is directly comparable with traditional field based assessments. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)

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17 pages, 65536 KiB

Open AccessArticle

Automatic Boat Identification System for VIIRS Low Light Imaging Data

by Christopher D. Elvidge^1,*, Mikhail Zhizhin², Kimberly Baugh² and Feng-Chi Hsu²

¹ Earth Observation Group, NOAA National Geophysical Data Center, 325 Broadway, Boulder, CO 80305, USA

² Cooperative Institute for Research in the Environmental Sciences, University of Colorado, Boulder, CO 80303, USA

Remote Sens. 2015, 7(3), 3020-3036; https://doi.org/10.3390/rs70303020 - 16 Mar 2015

Cited by 191 | Viewed by 22822

Abstract

The ability for satellite sensors to detect lit fishing boats has been known since the 1970s. However, the use of the observations has been limited by the lack of an automatic algorithm for reporting the location and brightness of offshore lighting features arising [...] Read more.

The ability for satellite sensors to detect lit fishing boats has been known since the 1970s. However, the use of the observations has been limited by the lack of an automatic algorithm for reporting the location and brightness of offshore lighting features arising from boats. An examination of lit fishing boat features in Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) data indicates that the features are essentially spikes. We have developed a set of algorithms for automatic detection of spikes and characterization of the sharpness of spike features. A spike detection algorithm generates a list of candidate boat detections. A second algorithm measures the height of the spikes for the discard of ionospheric energetic particle detections and to rate boat detections as either strong or weak. A sharpness index is used to label boat detections that appear blurry due to the scattering of light by clouds. The candidate spikes are then filtered to remove features on land and gas flares. A validation study conducted using analyst selected boat detections found the automatic algorithm detected 99.3% of the reference pixel set. VIIRS boat detection data can provide fishery agencies with up-to-date information of fishing boat activity and changes in this activity in response to new regulations and enforcement regimes. The data can provide indications of illegal fishing activity in restricted areas and incursions across Exclusive Economic Zone (EEZ) boundaries. VIIRS boat detections occur widely offshore from East and Southeast Asia, South America and several other regions. Full article

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19 pages, 3233 KiB

Open AccessArticle

Terrestrial Laser Scanning Reveals Seagrass Microhabitat Structure on a Tideflat

by Michael Hannam^1,* and L. Monika Moskal²

¹ School of Aquatic and Fisheries Sciences, University of Washington, Seattle, WA 98105, USA

² School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA

Remote Sens. 2015, 7(3), 3037-3055; https://doi.org/10.3390/rs70303037 - 16 Mar 2015

Cited by 14 | Viewed by 8706

Abstract

Local-scale environmental heterogeneity can provide microhabitats that influence the spatial distribution of competing species. Microhabitats may influence the distribution of seagrasses along elevation gradients, but difficulty measuring intertidal microtopography has hindered quantification. Using a terrestrial laser scanner (TLS), we mapped and monitored a [...] Read more.

Local-scale environmental heterogeneity can provide microhabitats that influence the spatial distribution of competing species. Microhabitats may influence the distribution of seagrasses along elevation gradients, but difficulty measuring intertidal microtopography has hindered quantification. Using a terrestrial laser scanner (TLS), we mapped and monitored a 1.84 ha study site for three years to understand spatial and temporal patterns of sediment microtopography. We performed high-accuracy GPS surveys and vegetation surveys of a native and an invasive seagrass. TLS provided sub-decimeter scale precision in digital elevation models (DEMs) of the tideflat. The location and shape of microtopographic features were stable from year to year, but the magnitude of local relief varied. A simple index of topographic context predicted the shoot density of the native seagrass, Zostera marina and the invasive seagrass, Zostera japonica, but the shoot density of the invasive seagrass was better predicted by the shoot density of Z. marina than by topographic context. Microtopographic relief at this site appears to exert a strong influence on the meter-scale distribution of seagrass. We demonstrate the potential for TLS mapping of habitat-relevant microtopography in a soft sediment intertidal environment where TLS faces substantial challenges but promises unique insights. Full article

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32 pages, 38195 KiB

Open AccessArticle

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

by Guangcheng Hu¹

and Li Jia^1,2,*

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

Remote Sens. 2015, 7(3), 3056-3087; https://doi.org/10.3390/rs70303056 - 16 Mar 2015

Cited by 131 | Viewed by 11512

Abstract

As a typical inland river basin, Heihe River basin has been experiencing severe water resource competition between different land cover types, especially in the middle stream and downstream areas. Terrestrial actual evapotranspiration (ETa), including evaporation from soil and water surfaces, evaporation of rainfall [...] Read more.

As a typical inland river basin, Heihe River basin has been experiencing severe water resource competition between different land cover types, especially in the middle stream and downstream areas. Terrestrial actual evapotranspiration (ETa), including evaporation from soil and water surfaces, evaporation of rainfall interception, transpiration of vegetation canopy and sublimation of snow and glaciers, is an important component of the water cycle in the Heihe River basin. We developed a hybrid remotely sensed ETa estimation model named ETMonitor to estimate the daily actual evapotranspiration of the Heihe River basin for the years 2009–2011 at a spatial resolution of 1 km. The model was forced by a variety of biophysical parameters derived from microwave and optical remote sensing observations. The estimated ETa was evaluated using eddy covariance (EC) flux observations at local scale and compared with the annual precipitation and the MODIS ETa product (MOD16) at regional scale. The spatial distribution and the seasonal variation of the estimated ETa were analyzed. The results indicate that the estimated ETa shows reasonable spatial and temporal patterns with respect to the diverse cold and arid landscapes in the upstream, middle stream and downstream regions, and is useful for various applications to improve the rational allocation of water resources in the Heihe River basin. Full article

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26 pages, 10346 KiB

Open AccessArticle

A Study of Coal Fire Propagation with Remotely Sensed Thermal Infrared Data

by Hongyuan Huo^1,2,3, Zhuoya Ni^4,5

, Caixia Gao⁶, Enyu Zhao³, Yuze Zhang³

, Yi Lian¹, Huili Zhang⁷, Shiyue Zhang¹, Xiaoguang Jiang^3,8,9,*, Xianfeng Song³, Ping Zhou¹⁰ and Tiejun Cui¹

¹ College of Urban and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China

² Tianjin Engineering Center for Geospatial Information Technology, Tianjin 300387, China

³ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

⁴ School of Geography, Beijing Normal University, Beijing 100875, China

⁵ ICube Lab, Université de Strasbourg, Boulevard Sebastien Brant, BP10413, Illkirch 67412, France

⁶ Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China

⁷ Nanchang Institute of Technology, Jiangxi 330044, China

⁸ Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China

⁹ College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

¹⁰ College of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

Remote Sens. 2015, 7(3), 3088-3113; https://doi.org/10.3390/rs70303088 - 17 Mar 2015

Cited by 42 | Viewed by 10838

Abstract

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In [...] Read more.

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km² per year. Full article

(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)

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24 pages, 70172 KiB

Open AccessArticle

Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture

by Lei Fan^1,2, Qing Xiao^1,*, Jianguang Wen^1,3, Qiang Liu⁴

, Yong Tang¹, Dongqin You^1,2, Heshun Wang¹, Zhaoning Gong⁵ and Xiaowen Li^6,†

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Joint Center for Global Change Studies, Beijing 100875, China

⁴ College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

⁵ College of Resource Environment and Tourism, Capital Normal University, Beijing100048, China

⁶ School of Geography, Beijing Normal University, Beijing 100875, China

^† This author has been deceased.

Remote Sens. 2015, 7(3), 3114-3137; https://doi.org/10.3390/rs70303114 - 18 Mar 2015

Cited by 31 | Viewed by 8351

Abstract

High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM). An [...] Read more.

High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM). An evaluation of the airborne ΔT_s/Fr space (incorporated with air temperature) revealed that normalized difference vegetation index (NDVI) saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed ΔT_s/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed ΔT_s/Fr space using the evaporative fraction (EF) and temperature vegetation dryness index (TVDI) were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE (0.023 m³·m⁻³) value and a higher correlation coefficient (0.68) than the TVDI. The application of the airborne ΔT_s/Fr space shows that the two modifications proposed in this study strengthen the link between the ΔT_s/Fr space and SM, which is important for improving the precision of the remote sensing Ts/VI space method for monitoring SM. Full article

(This article belongs to the Special Issue The Development and Validation of Remote Sensing Products for Terrestrial, Hydrological, and Ecological Applications at the Regional Scale)

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15 pages, 32235 KiB

Open AccessArticle

Unique Sequence of Events Triggers Manta Ray Feeding Frenzy in the Southern Great Barrier Reef, Australia

by Scarla J. Weeks^1,*

, Marites M. Magno-Canto¹, Fabrice R. A. Jaine^1,2, Jon Brodie³ and Anthony J. Richardson^4,5

¹ Biophysical Oceanography Group, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, QLD 4072, Australia

² Manta Ray and Whale Shark Research Centre, Marine Megafauna Foundation, Praia do Tofo, Inhambane, Mozambique

³ Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Townsville, QLD 4811, Australia

⁴ Oceans and Atmosphere Flagship, CSIRO Marine and Atmospheric Research, EcoSciences Precinct, Dutton Park, Brisbane, QLD 4102, Australia

⁵ Centre for Applications in Natural Resource Mathematics (CARM), School of Mathematics and Physics, The University of Queensland, St Lucia, QLD 4072, Australia

Remote Sens. 2015, 7(3), 3138-3152; https://doi.org/10.3390/rs70303138 - 18 Mar 2015

Cited by 25 | Viewed by 11929

Abstract

Manta rays are classified as Vulnerable to Extinction on the IUCN Red List for Threatened Species. In Australia, a key aggregation site for reef manta rays is Lady Elliot Island (LEI) on the Great Barrier Reef, ~7 km from the shelf edge. Here, [...] Read more.

Manta rays are classified as Vulnerable to Extinction on the IUCN Red List for Threatened Species. In Australia, a key aggregation site for reef manta rays is Lady Elliot Island (LEI) on the Great Barrier Reef, ~7 km from the shelf edge. Here, we investigate the environmental processes that triggered the largest manta ray feeding aggregation yet observed in Australia, in early 2013. We use MODIS sea surface temperature (SST), chlorophyll-a concentration and photic depth data, together with in situ data, to show that anomalous river discharges led to high chlorophyll (anomalies: 10–15 mg∙m⁻³) and turbid (photic depth anomalies: −15 m) river plumes extending out to LEI, and that these became entrained offshore around the periphery of an active cyclonic eddy. Eddy dynamics led to cold bottom intrusions along the shelf edge (6 °C temperature decrease), and at LEI (5 °C temperature decrease). Strongest SST gradients (>1 °C∙km⁻¹) were at the convergent frontal zone between the shelf and eddy-influenced waters, directly overlying LEI. Here, the front intensified on the spring ebb tide to attract and shape the aggregation pattern of foraging manta rays. Future research could focus on mapping the probability and persistence of these ecologically significant frontal zones via remote sensing to aid the management and conservation of marine species. Full article

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31 pages, 15772 KiB

Open AccessArticle

Toward Estimating Wetland Water Level Changes Based on Hydrological Sensitivity Analysis of PALSAR Backscattering Coefficients over Different Vegetation Fields

by Ting Yuan^1,2, Hyongki Lee^1,2,*

and Hahn Chul Jung^3,4

¹ Department of Civil and Environmental Engineering, University of Houston, N107 Engineering Building 1, Houston, TX 77204, USA

² National Center for Airborne Laser Mapping, University of Houston, 5000 Gulf Freeway Building 4 Room 216, Houston, TX 77204, USA

³ Office of Applied Sciences, NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA

⁴ Science Systems and Applications, Inc. (SSAI), 10210 Greenbelt Road, Lanham, MD 20706, USA

Remote Sens. 2015, 7(3), 3153-3183; https://doi.org/10.3390/rs70303153 - 19 Mar 2015

Cited by 33 | Viewed by 8532

Abstract

Synthetic Aperture Radar (SAR) has been successfully used to map wetland’s inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study [...] Read more.

Synthetic Aperture Radar (SAR) has been successfully used to map wetland’s inundation extents and types of vegetation based on the fact that the SAR backscatter signal from the wetland is mainly controlled by the wetland vegetation type and water level changes. This study describes the relation between L-band PALSAR and seasonal water level changes obtained from Envisat altimetry over the island of Île Mbamou in the Congo Basin where two distinctly different vegetation types are found. We found positive correlations between and water level changes over the forested southern Île Mbamou whereas both positive and negative correlations were observed over the non-forested northern Île Mbamou depending on the amount of water level increase. Based on the analysis of sensitivity, we found that denser vegetation canopy leads to less sensitive variation with respect to the water level changes regardless of forested or non-forested canopy. Furthermore, we attempted to estimate water level changes which were then compared with the Envisat altimetry and InSAR results. Our results demonstrated a potential to generate two-dimensional maps of water level changes over the wetlands, and thus may have substantial synergy with the planned Surface Water and Ocean Topography (SWOT) mission. Full article

(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)

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22 pages, 2978 KiB

Open AccessArticle

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

by Rosa Oltra-Carrió^1,*,†, Frédéric Baup^2,†, Sophie Fabre^1,†, Rémy Fieuzal^2,† and Xavier Briottet^1,†

¹ DOTA-ONERA, 2 Avenue Edouard Belin, 31055 Toulouse Cedex 4, France

² CESBIO, 18 avenue. Edouard Belin, 31401 Toulouse Cedex 9, France

^† These authors contributed equally to this work.

Remote Sens. 2015, 7(3), 3184-3205; https://doi.org/10.3390/rs70303184 - 20 Mar 2015

Cited by 43 | Viewed by 9093

Abstract

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches [...] Read more.

The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R²) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOL_CC and NINSON_CC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² superior to 0.76 and Root mean square errors inferior to 8.3% m³∙m⁻³ in all cases) and using in-situ data, where WISOIL, NINSOL_CC and NINSON_CC criteria stand out among the NSMI and CH. Full article

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26 pages, 10321 KiB

Open AccessArticle

Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval

by Simon Zwieback^1,*, Christoph Paulik² and Wolfgang Wagner²

¹ Institute of Environmental Engineering, ETH Zurich, Stefano-Franscini-Platz 3, Zurich 8093, Switzerland

² Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology, Gußhausstraße 27-29, Vienna 1040, Austria

Remote Sens. 2015, 7(3), 3206-3231; https://doi.org/10.3390/rs70303206 - 20 Mar 2015

Cited by 29 | Viewed by 7119

Abstract

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would [...] Read more.

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare. Full article

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18 pages, 1835 KiB

Open AccessArticle

Early Water Stress Detection Using Leaf-Level Measurements of Chlorophyll Fluorescence and Temperature Data

by Zhuoya Ni^1,2, Zhigang Liu^1,*, Hongyuan Huo³

, Zhao-Liang Li^2,4, Françoise Nerry², Qingshan Wang¹ and Xiaowen Li^1,†

¹ State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China

² ICube, CNRS, Université de Strasbourg, 300 Boulevard Sébastien Brant, CS10413, Illkirch 67412, France

³ College of Urban and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China

⁴ Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

^† This author has been deceased.

Remote Sens. 2015, 7(3), 3232-3249; https://doi.org/10.3390/rs70303232 - 20 Mar 2015

Cited by 53 | Viewed by 10785

Abstract

The purpose of this paper was to investigate the early water stress in maize using leaf-level measurements of chlorophyll fluorescence and temperature. In this study, a series of diurnal measurements, such as leaf chlorophyll fluorescence (Fs), leaf spectrum, temperature and photosynthetically active radiation [...] Read more.

The purpose of this paper was to investigate the early water stress in maize using leaf-level measurements of chlorophyll fluorescence and temperature. In this study, a series of diurnal measurements, such as leaf chlorophyll fluorescence (Fs), leaf spectrum, temperature and photosynthetically active radiation (PAR), were conducted for maize during gradient watering and filled watering experiments. Fraunhofer Line Discriminator methods (FLD and 3FLD) were used to obtain fluorescence from leaves spectrum. This simulated work using the SCOPE model demonstrated the variations in fluorescence and temperature in stress levels expressed by different stress factors. In the field measurement, the gradient experiment revealed that chlorophyll fluorescence decreased for plants with water stress relative to well-water plants and T_leaf-T_air increased; the filled watering experiment stated that chlorophyll fluorescence of maize under water stress were similar to those of maize under well-watering condition. In addition, the relationships between the Fs, retrieved fluorescence, T_leaf-T_air and water content were analyzed. The Fs determination resulted to the best coefficients of determination for the normalized retrieved fluorescence FLD/PAR (R²= 0.54), T_leaf-T_air (R² = 0.48) and water content (R² = 0.71). The normalized retrieved fluorescence yielded a good coefficient of determination for T_leaf-T_air (R² = 0.48). This study demonstrated that chlorophyll fluorescence could reflect variations in the physiological states of plants during early water stress, and leaf temperature confirmed the chlorophyll fluorescence analysis results and improved the accuracy of the water stress detection. Full article

(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)

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24 pages, 6390 KiB

Open AccessArticle

Estimation and Validation of Land Surface Temperatures from Chinese Second-Generation Polar-Orbit FY-3A VIRR Data

by Bo-Hui Tang^1,2,3, Kun Shao⁴, Zhao-Liang Li^2,5,*, Hua Wu¹

, Françoise Nerry² and Guoqing Zhou⁶

¹ State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

² ICube, UdS, CNRS, 300 Bld Sébastien Brant, CS10413, 67412 Illkirch, France

³ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

⁴ Hefei University of Technology, Hefei 230009, China

⁵ Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

⁶ Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guangxi 541004, China

Remote Sens. 2015, 7(3), 3250-3273; https://doi.org/10.3390/rs70303250 - 20 Mar 2015

Cited by 67 | Viewed by 8477

Abstract

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water [...] Read more.

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm², provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K. Full article

(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)

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19 pages, 649 KiB

Open AccessArticle

Assessing MODIS GPP in Non-Forested Biomes in Water Limited Areas Using EC Tower Data

by Flor Álvarez-Taboada^1,*

, David Tammadge², Martin Schlerf³ and Andrew Skidmore²

¹ GEOINCA-202, Universidad de León, Campus de Ponferrada C/ Avda. de Astorga s/n, 24401 Ponferrada, León, Spain

² Department of Natural Resources, Faculty of ITC, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands

³ Luxembourg Institute of Science and Technology (LIST), Department for Environmental Research and Innovation, 41, rue du Brill, L-4422 Belvaux, Luxembourg

Remote Sens. 2015, 7(3), 3274-3292; https://doi.org/10.3390/rs70303274 - 20 Mar 2015

Cited by 7 | Viewed by 7121

Abstract

Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to [...] Read more.

Although shrublands, savannas and grasslands account for 37% of the world’s terrestrial area, not many studies have analysed the role of these ecosystems in the global carbon cycle at a regional scale. The MODIS Gross Primary Production (GPP) product is used here to help bridge this gap. In this study, the agreement between the MODIS GPP product (GPPm) and the GPP Eddy Covariance tower data (GPPec) was tested for six different sites in temperate and dry climatic regions (three grasslands, two shrublands and one evergreen forest). Results of this study show that for the non-forest sites in water-limited areas, GPPm is well correlated with GPPec at annual scales (r²= 0.77, n = 12; SEE = 149.26 g C∙m⁻²∙year⁻¹), although it tends to overestimate GPP and it is less accurate in the sites with permanent water restrictions. The use of biome-specific models based on precipitation measurements at a finer spatial resolution than the Data Assimilation Office (DAO) values can increase the accuracy of these estimations. The seasonal dynamics and the beginning and end of the growing season were well captured by GPPm for the sites where (i) the productivity was low throughout the year or (ii) the changes in the flux trend were abrupt, usually due to the restrictions in water availability. The agreement between GPPec and GPPm in non-forested sites was lower on a weekly basis than at an annual scale (0.44 ≤ r² ≤ 0.49), but these results were improved by including meteorological data at a finer spatial scale, and soil water content and temperature measurements in the model developed to predict GPPec (0.52 ≤ r² ≤ 0.65). Full article

(This article belongs to the Special Issue Carbon Cycle, Global Change, and Multi-Sensor Remote Sensing)

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27 pages, 41628 KiB

Open AccessArticle

Geometric Quality Analysis of AVHRR Orthoimages

by Sultan Kocaman Aksakal^1,*,†

, Christoph Neuhaus², Emmanuel Baltsavias³ and Konrad Schindler³

¹ Department of Geomatics Engineering, Hacettepe University, Ankara 06800, Turkey

² Department of Geography, University of Bern, CH-3012 Bern, Switzerland

³ ETH Zurich, Institute of Geodesy and Photogrammetry, CH-8093 Zurich, Switzerland

^† The work has been done while she was at ETH Zurich.

Remote Sens. 2015, 7(3), 3293-3319; https://doi.org/10.3390/rs70303293 - 23 Mar 2015

Cited by 11 | Viewed by 7869

Abstract

The geometric accuracy of 2008 AVHRR orthoimages from Metop-A, NOAA-17 and NOAA-18 satellites over Switzerland have been investigated here. The methods employed in the study are fully automated, with an accuracy of 0.1–0.2 pixels, however, blunders do occur and this requests a careful [...] Read more.

The geometric accuracy of 2008 AVHRR orthoimages from Metop-A, NOAA-17 and NOAA-18 satellites over Switzerland have been investigated here. The methods employed in the study are fully automated, with an accuracy of 0.1–0.2 pixels, however, blunders do occur and this requests a careful blunder detection approach. The investigations include analysis of relative, absolute and band-to-band registration (BBR) accuracy. Regarding relative accuracy, thousands of points are matched between Metop-A, NOAA-17 and NOAA-18 images of the same day. The accuracy is quite high with mean shifts between 0.2 and 0.4 pixels. Systematic stripes have been observed when NOAA-18 images are involved in matching. In spite of many efforts to find the source of this error, no explanation could be found. In addition, large shifts up to 2.9 pixels on some days between September and December 2008 were observed. Regarding absolute accuracy, digitized lakes as reference polygons have been used and a subpixel lake matching method has been applied. The mean shifts generally fulfilled EUMETSAT and GCOS specifications, although some partial results exceed them, especially for Metop-A. Regarding BBR accuracy, six multispectral bands have been compared, also with point matching. The EUMETSAT specification is 0.1 km, however, this specification refers to original images, not orthoimages. Taking also into account the matching errors of 0.1 km, the EUMETSAT specifications are in principle fulfilled in all cases except matching of Metop-A and NOAA-17 Band-2 images with Bands 4 and 5. The overall work showed that although, in general, accuracies are high and fulfill specifications, errors exceeding the specifications can occur and vary depending on the satellite used, time and location. Such errors influence subsequent geometric or thematic processing; thus, an automated and permanent quality control of such images should be executed. Full article

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27 pages, 4067 KiB

Open AccessArticle

On-Orbit Camera Misalignment Estimation Framework and Its Application to Earth Observation Satellite

by Seungwoo Lee^* and Dongseok Shin

Satrec Initiative, 21 Yusung-daero 1628 Beon-gil, Yuseong-gu, Daejeon 305-811, Korea

Remote Sens. 2015, 7(3), 3320-3346; https://doi.org/10.3390/rs70303320 - 23 Mar 2015

Cited by 11 | Viewed by 11178

Abstract

Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, [...] Read more.

Despite the efforts for precise alignment of imaging sensors and attitude sensors before launch, the accuracy of pre-launch alignment is limited. The misalignment between attitude frame and camera frame is especially important as it is related to the localization error of the spacecraft, which is one of the essential factors of satellite image quality. In this paper, a framework for camera misalignment estimation is presented with its application to a high-resolution earth-observation satellite—Deimos-2. The framework intends to provide a solution for estimation and correction of the camera misalignment of a spacecraft, covering image acquisition planning to mathematical solution of camera misalignment. Considerations for effective image acquisition planning to obtain reliable results are discussed, followed by a detailed description on a practical method for extracting many GCPs automatically using reference ortho-photos. Patterns of localization errors that commonly occur due to the camera misalignment are also investigated. A mathematical model for camera misalignment estimation is described comprehensively. The results of simulation experiments showing the validity and accuracy of the misalignment estimation model are provided. The proposed framework was applied to Deimos-2. The real-world data and results from Deimos-2 are presented. Full article

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25 pages, 27913 KiB

Open AccessArticle

A Robust Algorithm of Multiquadric Method Based on an Improved Huber Loss Function for Interpolating Remote-Sensing-Derived Elevation Data Sets

by Chuanfa Chen^1,2,*

, Yanyan Li³, Changqing Yan⁴, Honglei Dai² and Guolin Liu²

¹ State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China

² Shandong Provincial Key Laboratory of Geomatics and Digital Technology, Shandong University of Science and Technology, Qingdao 266590, China

³ Shool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China

⁴ Department of Information Engineering, Shandong University of Science and Technology, Taian 271019, China

Remote Sens. 2015, 7(3), 3347-3371; https://doi.org/10.3390/rs70303347 - 23 Mar 2015

Cited by 21 | Viewed by 7294

Abstract

Remote-sensing-derived elevation data sets often suffer from noise and outliers due to various reasons, such as the physical limitations of sensors, multiple reflectance, occlusions and low contrast of texture. Outliers generally have a seriously negative effect on DEM construction. Some interpolation methods like [...] Read more.

Remote-sensing-derived elevation data sets often suffer from noise and outliers due to various reasons, such as the physical limitations of sensors, multiple reflectance, occlusions and low contrast of texture. Outliers generally have a seriously negative effect on DEM construction. Some interpolation methods like ordinary kriging (OK) are capable of smoothing noise inherent in sample points, but are sensitive to outliers. In this paper, a robust algorithm of multiquadric method (MQ) based on an Improved Huber loss function (MQ-IH) has been developed to decrease the impact of outliers on DEM construction. Theoretically, the improved Huber loss function is null for outliers, quadratic for small errors, and linear for others. Simulated data sets drawn from a mathematical surface with different error distributions were employed to analyze the robustness of MQ-IH. Results indicate that MQ-IH obtains a good balance between efficiency and robustness. Namely, the performance of MQ-IH is comparative to those of the classical MQ and MQ based on the Classical Huber loss function (MQ-CH) when sample points follow a normal distribution, and the former outperforms the latter two when sample points are subject to outliers. For example, for the Cauchy error distribution with the location parameter of 0 and scale parameter of 1, the root mean square errors (RMSEs) of MQ-CH and the classical MQ are 0.3916 and 1.4591, respectively, whereas that of MQ-IH is 0.3698. The performance of MQ-IH is further evaluated by qualitative and quantitative analysis through a real-world example of DEM construction with the stereo-images-derived elevation points. Results demonstrate that compared with the classical interpolation methods, including natural neighbor (NN), OK and ANUDEM (a program that calculates regular grid digital elevation models (DEMs) with sensible shape and drainage structure from arbitrarily large topographic data sets), and two versions of MQ, including the classical MQ and MQ-CH, MQ-IH has a better ability of maximally reducing the impact of outliers, while faithfully preserving terrain features. Theoretically, MQ-IH is not a promising interpolation method, and some side effects can be found from its simulation results. For example, the contours of MQ-IH are coarser than ANUDEM in some locations of the real-world study site, and its hill shade may not strictly agree with the real-world surface at rough terrain. Furthermore, the computing cost of MQ-IH is much bigger than that of the classical interpolation methods. However, compared with the classical interpolation methods, MQ-IH has significant potential for interpolating remote-sensing-derived elevation data sets. Full article

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28 pages, 3899 KiB

Open AccessArticle

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

by Gareth Ireland¹, Michele Volpi² and George P. Petropoulos^1,*

¹ Department of Geography and Earth Sciences, University of Aberystwyth, Aberystwyth SY23 3DB, UK

² Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh EH8 9AB, UK

Remote Sens. 2015, 7(3), 3372-3399; https://doi.org/10.3390/rs70303372 - 23 Mar 2015

Cited by 87 | Viewed by 9531

Abstract

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a [...] Read more.

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way. Full article

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Remote Sens., Volume 7, Issue 3 (March 2015) – 50 articles , Pages 2238-3425

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