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Remote Sens., Volume 4, Issue 7 (July 2012), Pages 1887-2198

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Research

Open AccessArticle Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification
Remote Sens. 2012, 4(7), 1887-1912; doi:10.3390/rs4071887
Received: 28 April 2012 / Revised: 19 June 2012 / Accepted: 20 June 2012 / Published: 26 June 2012
Cited by 13 | PDF Full-text (2983 KB) | HTML Full-text | XML Full-text
Abstract
The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is [...] Read more.
The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological processes in these minerotrophic peatlands (fens) in order to provide effective monitoring tools to water managers. The objective of this study was to use VHR remote sensing data to understand the seasonal dynamics of the hydrology in fens. A series of 10 multispectral pan-sharpened GeoEye-1 images (with a spatial resolution of 40 cm) were acquired during the snow-free season (May to October) in 2009 and 2010, centered on two study sites in the Laforge sector (54°06'N; 72°30'W). These are two fens instrumented for continuous hydrometeorological monitoring (water level, discharge, precipitation, air temperature). An object-based classification procedure was set up and applied. It consisted of segmenting the imagery into objects using the multiresolution segmentation algorithm (MRIS) to delineate internal structures in the peatlands (aquatic, semi-aquatic, and terrestrial). Then, the objects were labeled using a fuzzy logic based algorithm. The overall classification accuracy of the 10 images was assessed to be 82%. The time series of the peatland mapping demonstrated the existence of important intra-seasonal spatial dynamics in the aquatic and semi-aquatic compartments. It was revealed that the dynamics amplitude depended on the morphological features of the fens. The observed spatial dynamics was also closely related to the evolution of the measured water levels. Full article
Open AccessArticle Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data
Remote Sens. 2012, 4(7), 1913-1928; doi:10.3390/rs4071913
Received: 25 April 2012 / Revised: 24 May 2012 / Accepted: 20 June 2012 / Published: 27 June 2012
Cited by 16 | PDF Full-text (1255 KB) | HTML Full-text | XML Full-text
Abstract
Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial [...] Read more.
Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy) the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR) true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring. Full article
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Open AccessArticle Distribution Patterns of Burned Areas in the Brazilian Biomes: An Analysis Based on Satellite Data for the 2002–2010 Period
Remote Sens. 2012, 4(7), 1929-1946; doi:10.3390/rs4071929
Received: 20 May 2012 / Revised: 19 June 2012 / Accepted: 21 June 2012 / Published: 29 June 2012
Cited by 9 | PDF Full-text (772 KB) | HTML Full-text | XML Full-text
Abstract
Fires modify the structure of vegetation communities, the carbon and water cycles, the soil’s chemistry, and affect the climate system. Within this context, this work aimed to understand the distribution patterns of burned areas in Brazil, during the period of 2002 to [...] Read more.
Fires modify the structure of vegetation communities, the carbon and water cycles, the soil’s chemistry, and affect the climate system. Within this context, this work aimed to understand the distribution patterns of burned areas in Brazil, during the period of 2002 to 2010, taking into consideration each one of the six Brazilian biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pampa and Pantanal) and the respective major land cover classes. Data from the MODIS MCD45A1 product (burned area), as well as thermal anomalies (MOD14 and MYD14) and precipitation (TRMM), were analyzed according to the 2002 Brazilian official land cover and land use map (PROBIO). The Brazilian savanna biome, known as Cerrado, presented the largest concentration of burned areas detected by MODIS (73%), followed by the Amazon (14%), Pantanal (6%), Atlantic Forest (4%), Caatinga (3%), and Pampa (0,06%) biomes. Indeed, in the years of 2007 and 2010, 90% and 92% of Brazil’s burned areas were concentrated in the Cerrado and Amazon biomes, respectively. TRMM data indicated that during these two years there was a significant influence of La Niña, causing low rainfall in the Amazon, Cerrado, Caatinga, and Atlantic Forest biomes. Regarding the land cover classes, approximately 81% of the burned areas occurred over remnant vegetation areas. Although no unequivocal correlation can be established between burned areas and new land conversions, the conspicuous concentration of fire scars, particularly in Amazon–Cerrado transition (i.e., the Arc of Deforestation) is certainly not a simple coincidence. Such patterns and trends corroborate the need of improved territorial governance, in addition to the implementation of systematic fire warning and preventive systems. Full article
Open AccessArticle Utility of Satellite and Aerial Images for Quantification of Canopy Cover and Infilling Rates of the Invasive Woody Species Honey Mesquite (Prosopis Glandulosa) on Rangeland
Remote Sens. 2012, 4(7), 1947-1962; doi:10.3390/rs4071947
Received: 9 May 2012 / Revised: 5 June 2012 / Accepted: 25 June 2012 / Published: 29 June 2012
Cited by 6 | PDF Full-text (3740 KB) | HTML Full-text | XML Full-text
Abstract
Woody plant encroachment into grasslands and rangelands is a world-wide phenomenon but detailed descriptions of changes in geographical distribution and infilling rates have not been well documented at large land scales. Remote sensing with either aerial or satellite images may provide a [...] Read more.
Woody plant encroachment into grasslands and rangelands is a world-wide phenomenon but detailed descriptions of changes in geographical distribution and infilling rates have not been well documented at large land scales. Remote sensing with either aerial or satellite images may provide a rapid means for accomplishing this task. Our objective was to compare the accuracy and utility of two types of images with contrasting spatial resolutions (1-m aerial and 30-m satellite) for classifying woody and herbaceous canopy cover and determining woody infilling rates in a large area of rangeland (800 km2) in north Texas that has been invaded by honey mesquite (Prosopis glandulosa). Accuracy assessment revealed that the overall accuracies for the classification of four land cover types (mesquite, grass, bare ground and other) were 94 and 87% with kappa coefficients of 0.89 and 0.77 for the 1-m and 30-m images, respectively. Over the entire area, the 30-m image over-estimated mesquite canopy cover by 9 percentage units (10 vs. 19%) and underestimated grass canopy cover by the same amount when compared to the 1-m image. The 30-m resolution image typically overestimated mesquite canopy cover within 225 4-ha sub-cells that contained a range of mesquite covers (1–70%) when compared to the 1-m image classification and was not suitable for quantifying infilling rates of this native invasive species. Documenting woody and non-woody canopy cover on large land areas is important for developing integrated, regional-scale management strategies for rangeland and grassland regions that have been invaded by woody plants. Full article
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Open AccessArticle The Impact of Subscale Inhomogeneity on Oxygen A Band Cloud-Top Pressure Estimates: Using ESA’s MERIS as a Proxy for DSCOVR-EPIC
Remote Sens. 2012, 4(7), 1963-1973; doi:10.3390/rs4071963
Received: 9 May 2012 / Revised: 25 June 2012 / Accepted: 26 June 2012 / Published: 29 June 2012
Cited by 1 | PDF Full-text (1008 KB) | HTML Full-text | XML Full-text
Abstract
Medium Spectral Resolution Imaging Spectrometer (MERIS) oxygen A band measurements were used as a proxy for the Earth Polychromatic Imaging Camera (EPIC),to be launched on NASA’s Deep Space Climate Observatory (DSCOVR). The high spatial resolution of MERIS (1 × 1 km2 [...] Read more.
Medium Spectral Resolution Imaging Spectrometer (MERIS) oxygen A band measurements were used as a proxy for the Earth Polychromatic Imaging Camera (EPIC),to be launched on NASA’s Deep Space Climate Observatory (DSCOVR). The high spatial resolution of MERIS (1 × 1 km2) is exploited to study the effects of subscale spatialheterogeneity of clouds on the cloud-top pressure retrieved at the coarser spatial resolutionof EPIC (10 × 10 km2). In general, for a sub-scale cloud fraction less than 1, a shift of cloud-top pressure toward the middle atmosphere is found, with a low-bias for highclouds and a high-bias for low clouds. In addition, the deviation is found to be a function of surface reflectance. The subscale variability of fully clouded EPIC pixels causes a weak underestimation of cloud-top pressure, when compared to averaged high-resolution retrievals. Full article
Open AccessArticle High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire
Remote Sens. 2012, 4(7), 1974-1994; doi:10.3390/rs4071974
Received: 28 April 2012 / Revised: 7 June 2012 / Accepted: 26 June 2012 / Published: 29 June 2012
Cited by 6 | PDF Full-text (752 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, [...] Read more.
Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May–September, 2006–2010). Image derived estimates of water and peat moisture were sensitive (R2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Open AccessArticle Radiation Mapping in Post-Disaster Environments Using an Autonomous Helicopter
Remote Sens. 2012, 4(7), 1995-2015; doi:10.3390/rs4071995
Received: 7 May 2012 / Revised: 2 July 2012 / Accepted: 2 July 2012 / Published: 5 July 2012
Cited by 14 | PDF Full-text (5282 KB) | HTML Full-text | XML Full-text
Abstract
Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima [...] Read more.
Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima nuclear reactor explosion, an unmanned helicopter capable of obtaining overhead images, gathering radiation measurements, and mapping both the structural and radiation content of the environment would have given the response team invaluable data early in the disaster, thereby allowing them to understand the extent of the damage and areas where dangers to personnel existed. With this motivation, the Unmanned Systems Lab at Virginia Tech has developed a remote sensing system for radiation detection and aerial imaging using a 90 kg autonomous helicopter and sensing payloads for the radiation detection and imaging operations. The radiation payload, which is the sensor of focus in this paper, consists of a scintillating type detector with associated software and novel search algorithms to rapidly and effectively map and locate sources of high radiation intensity. By incorporating this sensing technology into an unmanned aerial vehicle system, crucial situational awareness can be gathered about a post-disaster environment and response efforts can be expedited. This paper details the radiation mapping and localization capabilities of this system as well as the testing of the various search algorithms using simulated radiation data. The various components of the system have been flight tested over a several-year period and a new production flight platform has been built to enhance reliability and maintainability. The new system is based on the Aeroscout B1-100 helicopter platform, which has a one-hour flight endurance and uses a COFDM radio system that gives the helicopter an effective range of 7 km. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications
Remote Sens. 2012, 4(7), 2016-2032; doi:10.3390/rs4072016
Received: 15 May 2012 / Revised: 28 June 2012 / Accepted: 30 June 2012 / Published: 6 July 2012
Cited by 4 | PDF Full-text (4102 KB) | HTML Full-text | XML Full-text
Abstract
Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6–22 m2 and decomposed them into a dual scale process by [...] Read more.
Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6–22 m2 and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities. Full article
Open AccessArticle The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China
Remote Sens. 2012, 4(7), 2033-2056; doi:10.3390/rs4072033
Received: 14 May 2012 / Revised: 25 June 2012 / Accepted: 27 June 2012 / Published: 6 July 2012
Cited by 24 | PDF Full-text (2979 KB) | HTML Full-text | XML Full-text
Abstract
The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention for its significant relationship to local climatic change and habitat comfort. Using quantitative thermal remote sensing and spatial statistics methods, here we analyze four Landsat TM/ETM+ images [...] Read more.
The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention for its significant relationship to local climatic change and habitat comfort. Using quantitative thermal remote sensing and spatial statistics methods, here we analyze four Landsat TM/ETM+ images of Guangzhou in South China acquired respectively on 13 October 1990, 2 January 2000, 23 November 2005, and 2 January 2009, to investigate the spatiotemporal variations in the land surface temperature (LST) over five land use/land cover (LULC) types and over different urban/rural zones. The emphases of this study are placed on the urban heat island (UHI) intensity and the relationships among LST, the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI). Results show that: (1) the UHI effect existed obviously over the period from 1990 to 2009 and high temperature anomalies were closely associated with built-up land and densely populated and heavily industrialized districts; (2) the UHI intensities represented by the mean LST difference between the urban downtown area and the suburban area were on average 0.88, 0.49, 0.90 and 1.16 K on the four dates, at the 99.99% confidence level; and (3) LST is related positively with NDBI and negatively with NDVI. The spatiotemporal variation of UTE of Guangzhou could be attributed to rapid urbanization, especially to the expanding built-up and developing land, declining vegetation coverage, and strengthening of anthropogenic and industrial activities which generate increasing amounts of waste heat. This study provides useful information for understanding the local climatic and environment changes that occur during rapid urbanization. Full article
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Open AccessArticle Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI
Remote Sens. 2012, 4(7), 2057-2075; doi:10.3390/rs4072057
Received: 20 May 2012 / Revised: 30 June 2012 / Accepted: 2 July 2012 / Published: 9 July 2012
Cited by 5 | PDF Full-text (1269 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, [...] Read more.
Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species. Full article
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Open AccessArticle Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle
Remote Sens. 2012, 4(7), 2076-2111; doi:10.3390/rs4072076
Received: 17 May 2012 / Revised: 2 July 2012 / Accepted: 4 July 2012 / Published: 12 July 2012
Cited by 11 | PDF Full-text (1660 KB) | HTML Full-text | XML Full-text
Abstract
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new [...] Read more.
This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
Open AccessArticle Utility of Remotely Sensed Imagery for Assessing the Impact of Salvage Logging after Forest Fires
Remote Sens. 2012, 4(7), 2112-2132; doi:10.3390/rs4072112
Received: 1 February 2012 / Revised: 4 July 2012 / Accepted: 5 July 2012 / Published: 13 July 2012
Cited by 2 | PDF Full-text (950 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. [...] Read more.
Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. Three wildfires with varying post-fire salvage activities and variable ground truth data were used to evaluate the utility of remotely sensed imagery for disturbance classification. The Red Eagle Fire in northwestern Montana had intensive ground truthing with GPS-equipment logging equipment to map their travel paths, the Tripod Fire in north central Washington had ground truthed disturbance transects, and the School Fire in southeastern Washington had no salvage-specific ground truthing but pre-and post-salvage images were available. Spectral mixture analysis (SMA) and principle component analysis (PCA) were used to evaluate the imagery. Our results showed that soil exposure (disturbance) was measureable when pre-and post-salvage QuickBird images were compared at one site. At two of the sites, only post-salvage imagery was available, and the soil exposure correlated well to salvage logging equipment disturbance at one site. When ground disturbance transects were compared to NAIP imagery two years after the salvage operation, it was difficult to identify disturbance due to vegetation regrowth. These results indicate that soil exposure (ground disturbance) by salvage operation can be detected with remotely sensed imagery especially if the images are taken less than two years after the salvage operation. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
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Open AccessArticle C-Band SAR Imagery for Snow-Cover Monitoring at Treeline, Churchill, Manitoba, Canada
Remote Sens. 2012, 4(7), 2133-2155; doi:10.3390/rs4072133
Received: 17 May 2012 / Revised: 17 June 2012 / Accepted: 25 June 2012 / Published: 13 July 2012
Cited by 5 | PDF Full-text (1620 KB) | HTML Full-text | XML Full-text
Abstract
RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band [...] Read more.
RADARSAT and ERS-2 data collected at multiple incidence angles are used to characterize the seasonal variations in the backscatter of snow-covered landscapes in the northern Hudson Bay Lowlands during the winters of 1997/98 and 1998/99. The study evaluates the usefulness of C-band SAR systems for retrieving the snow water equivalent under dry snow conditions in the forest–tundra ecotone. The backscatter values are compared against ground measurements at six sampling sites, which are taken to be representative of the land-cover types found in the region. The contribution of dry snow to the radar return is evident when frost penetrates the first 20 cm of soil. Only then does the backscatter respond positively to changes in snow water equivalent, at least in the open and forested areas near the coast, where 1-dB increases in backscatter for each approximate 5–10 mm of accumulated water equivalent are observed at 20–31° incidence angles. Further inland, the backscatter shows either no change or a negative change with snow accumulation, which suggests that the radar signal there is dominated by ground surface scattering (e.g., fen) when not attenuated by vegetation (e.g., forested and transition). With high-frequency ground-penetrating radar, we demonstrate the presence of a 10–20-cm layer of black ice underneath the snow cover, which causes the reduced radar returns (−15 dB and less) observed in the inland fen. A correlation between the backscattering and the snow water equivalent cannot be determined due to insufficient observations at similar incidence angles. To establish a relationship between the snow water equivalent and the backscatter, only images acquired with similar incidence angles should be used, and they must be corrected for both vegetation and ground effects. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Open AccessArticle Analysis of the Scaling Effects in the Area-Averaged Fraction of Vegetation Cover Retrieved Using an NDVI-Isoline-Based Linear Mixture Model
Remote Sens. 2012, 4(7), 2156-2180; doi:10.3390/rs4072156
Received: 16 May 2012 / Revised: 11 July 2012 / Accepted: 11 July 2012 / Published: 18 July 2012
Cited by 3 | PDF Full-text (503 KB) | HTML Full-text | XML Full-text
Abstract
The spectral unmixing of a linear mixture model (LMM) with Normalized Difference Vegetation Index (NDVI) constraints was performed to estimate the fraction of vegetation cover (FVC) over the earth’s surface in an effort to facilitate long-term surface vegetation monitoring using a set [...] Read more.
The spectral unmixing of a linear mixture model (LMM) with Normalized Difference Vegetation Index (NDVI) constraints was performed to estimate the fraction of vegetation cover (FVC) over the earth’s surface in an effort to facilitate long-term surface vegetation monitoring using a set of environmental satellites. Although the integrated use of multiple sensors improves the spatial and temporal quality of the data sets, area-averaged FVC values obtained using an LMM-based algorithm suffer from systematic biases caused by differences in the spatial resolutions of the sensors, known as scaling effects. The objective of this study is to investigate the scaling effects in area-averaged FVC values using analytical approaches by focusing on the monotonic behavior of the scaling effects as a function of the spatial resolution. The analysis was conducted based on a resolution transformation model introduced recently by the authors in the accompanying paper (Obata et al., 2012). The maximum value of the scaling effects present in FVC values was derived analytically and validated numerically. A series of derivations identified the error bounds (inherent uncertainties) of the averaged FVC values caused by the scaling effect. The results indicate a fundamental difference between the NDVI and the retrieved FVC from NDVI, which should be noted for accuracy improvement of long-term observation datasets. Full article
Open AccessArticle Application of MODIS Imagery for Intra-Annual Water Clarity Assessment of Minnesota Lakes
Remote Sens. 2012, 4(7), 2181-2198; doi:10.3390/rs4072181
Received: 30 May 2012 / Revised: 2 July 2012 / Accepted: 11 July 2012 / Published: 23 July 2012
Cited by 4 | PDF Full-text (545 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring of water clarity trends is necessary for water resource managers. Remote sensing based methods are well suited for monitoring clarity in water bodies such as the inland lakes in Minnesota, United States. This study evaluated the potential of using imagery from [...] Read more.
Monitoring of water clarity trends is necessary for water resource managers. Remote sensing based methods are well suited for monitoring clarity in water bodies such as the inland lakes in Minnesota, United States. This study evaluated the potential of using imagery from NASA’s MODIS sensor to study intra-annual variations in lake clarity. MODIS reflectance images from six dates throughout the 2006 growing season were used with field collected Secchi disk transparency data to estimate water clarity in large lakes throughout Minnesota. The results of this research indicate the following: water clarity estimates derived from MODIS imagery are largely similar to those derived from lower temporal resolution sensors such as Landsat, robust water clarity estimates can be derived using MODIS for many dates throughout a growing season (R2 values between 0.32 and 0.71), and the relatively low spatial resolution of MODIS restricts its applicability to a subset of the largest inland lakes (>160 ha, or 400 acres). This study suggests that water clarity maps developed with MODIS imagery and bathymetry data may be useful tools for resource managers concerned with intra- and inter-annual variations in large inland lakes. Full article

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