Remote Sensing

1493 KiB

Open AccessArticle

Roads as Drivers of Change: Trajectories across the Tri‑National Frontier in MAP, the Southwestern Amazon

by Jane Southworth, Matt Marsik, Youliang Qiu, Stephen Perz, Graeme Cumming, Forrest Stevens, Karla Rocha, Amy Duchelle and Grenville Barnes

Remote Sens. 2011, 3(5), 1047-1066; https://doi.org/10.3390/rs3051047 - 24 May 2011

Cited by 109 | Viewed by 11969

Abstract

Regional studies of land cover change are often limited by available data and in terms of comparability across regions, by the transferability of methods. This research addresses the role of roads and infrastructure improvements across a tri-national frontier region with similar climatic and [...] Read more.

Regional studies of land cover change are often limited by available data and in terms of comparability across regions, by the transferability of methods. This research addresses the role of roads and infrastructure improvements across a tri-national frontier region with similar climatic and biophysical conditions but very different trajectories of forest clearing. The standardization of methodologies and the extensive spatial and temporal framework of the analysis are exciting as they allow us to monitor a dynamic region with global significance as it enters an era of increased road connectivity and massive potential forest loss. Our study region is the “MAP” frontier, which covers Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia. This tri-national frontier is being integrated into the global economy via the paving of the Inter-Oceanic Highway which links the region to ports in the Atlantic and Pacific, constituting a major infrastructure change within just the last decade. Notably, there are differences in the extent of road paving among the three sides of the tri-national frontier, with paving complete in Acre, underway in Madre de Dios, and incipient in Pando. Through a multi-temporal analysis of land cover in the MAP region from 1986 to 2005, we found that rates of deforestation differ across the MAP frontier, with higher rates in Acre, followed by Madre de Dios and the lowest rates in Pando, although the dominant land cover across the region is still stable forest cover (89% overall). For all dates in the study period, deforestation rates drop with distance from major roads although the distance before this drop off appears to relate to development, with Acre influencing forests up to around 45 km out, Madre de Dios to about 18 km out and less of a discernable effect or distance value in Pando. As development occurs, the converted forest areas saturate close to roads, resulting in increasing rates of deforestation at further distances and patch consolidation of clearings over time. We can use this trend as a basis for future change predictions, with Acre providing a guide to likely future development for Madre de Dios, and in time potentially for Pando. Given the correspondence of road paving to deforestation, our findings imply that as road paving increases connectivity, flows of people and goods will accelerate across this landscape, increasing the likelihood of dramatic future changes on all sides of the tri‑national frontier. Full article

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1042 KiB

Open AccessArticle

Spatial and Temporal Homogeneity of Solar Surface Irradiance across Satellite Generations

by Rebekka Posselt, Richard Mueller, Reto Stöckli and Jörg Trentmann

Remote Sens. 2011, 3(5), 1029-1046; https://doi.org/10.3390/rs3051029 - 20 May 2011

Cited by 34 | Viewed by 9202

Abstract

Solar surface irradiance (SIS) is an essential variable in the radiation budget of the Earth. Climate data records (CDR’s) of SIS are required for climate monitoring, for climate model evaluation and for solar energy applications. A 23 year long (1983–2005) continuous and validated [...] Read more.

Solar surface irradiance (SIS) is an essential variable in the radiation budget of the Earth. Climate data records (CDR’s) of SIS are required for climate monitoring, for climate model evaluation and for solar energy applications. A 23 year long (1983–2005) continuous and validated SIS CDR based on the visible channel (0.45–1 μm) of the MVIRI instruments onboard the first generation of Meteosat satellites has recently been generated using a climate version of the well established Heliosat method. This version of the Heliosat method includes a newly developed self-calibration algorithm and an improved algorithm to determine the clear sky reflection. The climate Heliosat version is also applied to the visible narrow-band channels of SEVIRI onboard the Meteosat Second Generation Satellites (2004–present). The respective channels are observing the Earth in the wavelength region at about 0.6 μm and 0.8 μm. SIS values of the overlapping time period are used to analyse whether a homogeneous extension of the MVIRI CDR is possible with the SEVIRI narrowband channels. It is demonstrated that the spectral differences between the used visible channels leads to significant differences in the solar surface irradiance in specific regions. Especially, over vegetated areas the reflectance exhibits a high spectral dependency resulting in large differences in the retrieved SIS. The applied self-calibration method alone is not able to compensate the spectral differences of the channels. Furthermore, the extended range of the input values (satellite counts) enhances the cloud detection of the SEVIRI instruments resulting in lower values for SIS, on average. Our findings have implications for the application of the Heliosat method to data from other geostationary satellites (e.g., GOES, GMS). They demonstrate the need for a careful analysis of the effect of spectral and technological differences in visible channels on the retrieved solar irradiance. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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400 KiB

Open AccessArticle

A Comparison of Three Geometric Self-Calibration Methods for Range Cameras

by Derek D. Lichti and Changjae Kim

Remote Sens. 2011, 3(5), 1014-1028; https://doi.org/10.3390/rs3051014 - 20 May 2011

Cited by 39 | Viewed by 7869

Abstract

Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents [...] Read more.

Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents a review and a quantitative comparison of three methods for range camera self-calibration in order to determine which, if any, is superior. Two different SwissRanger range cameras have been calibrated using each method. Though differences of up to 2 mm (in object space) in both the observation precision and accuracy measures exist between the methods, they are of little practical consequence when compared to the magnitude of these measures (12 mm to 18 mm). One of the methods was found to underestimate the principal distance but overestimate the rangefinder offset in comparison to the other two methods whose estimates agreed more closely. Strong correlations among the rangefinder offset, periodic error terms and the camera position co-ordinates are indentified and their cause explained in terms of network geometry and observation range. Full article

(This article belongs to the Special Issue Time-of-Flight Range-Imaging Cameras)

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426 KiB

Open AccessArticle

Cloud Remote Sensing Using Midwave IR CO₂ and N₂O Slicing Channels near 4.5 μm

by Bo-Cai Gao, Rong-Rong Li and Eric P. Shettle

Remote Sens. 2011, 3(5), 1006-1013; https://doi.org/10.3390/rs3051006 - 17 May 2011

Cited by 6 | Viewed by 7433

Abstract

Narrow channels located in the longwave IR CO₂ absorption region between approximately 13.2 and 14.5 μm, the well known CO₂ slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well [...] Read more.

Narrow channels located in the longwave IR CO₂ absorption region between approximately 13.2 and 14.5 μm, the well known CO₂ slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well as atmospheric temperature profiles. The designs of some of the near-future multi-channel earth observing satellite sensors cannot accommodate these longwave IR channels. Based on the analysis of the multi-channel imaging data collected with the NASA Moderate Resolution Imaging SpectroRadiometer (MODIS) instrument and on theoretical cloud radiative transfer modeling, we have found that narrow channels located at the midwave IR region between approximately 4.2 and 4.55 μm, where the combined CO₂ and N₂O absorption effects decrease rapidly with increasing wavelength, have similar properties as the longwave IR CO₂ slicing channels. The scattering of solar radiation by clouds on the long wavelength side of the 4.3 μm CO₂ absorption makes only a small contribution to the upwelling radiances. In order to retain the crucial cloud and temperature sensing capabilities, future satellite sensors should consider including midwave IR CO₂ and N₂O slicing channels if the longwave IR channels cannot be implemented on the sensors. The hyperspectral data covering the 3.7-15.5 mm wavelength range and measured with the Infrared Atmospheric Sounding Interferometer (IASI) can be used to further assess the utility of midwave IR channels for satellite remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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1204 KiB

Open AccessArticle

Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada

by Jennifer D. O’Neill, Maycira Costa and Tara Sharma

Remote Sens. 2011, 3(5), 975-1005; https://doi.org/10.3390/rs3050975 - 16 May 2011

Cited by 34 | Viewed by 11066

Abstract

Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal [...] Read more.

Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal ecosystem management. A proposed tool for such spatial monitoring is remote imagery, which can cost- and time-effectively cover large and inaccessible areas frequently. However, to effectively apply this technology, an understanding is required of the spectral behavior of eelgrass and its associated substrates. In this study, in situ hyperspectral measurements were used to define key spectral variables that provide the greatest spectral separation between Z. marina and associated submerged substrates. For eelgrass classification of an in situ above water reflectance dataset, the selected variables were: slope 500–530 nm, first derivatives (R’) at 566 nm, 580 nm, and 602 nm, yielding 98% overall accuracy. When the in situ reflectance dataset was water-corrected, the selected variables were: 566:600 and 566:710, yielding 97% overall accuracy. The depth constraint for eelgrass identification with the field spectrometer was 5.0 to 6.0 m on average, with a range of 3.0 to 15.0 m depending on the characteristics of the water column. A case study involving benthic classification of hyperspectral airborne imagery showed the major advantage of the variable selection was meeting the sample size requirements of the more statistically complex Maximum Likelihood classifier. Results of this classifier yielded eelgrass classification accuracy of over 85%. The depth limit of eelgrass spectral detection for the AISA sensor was 5.5 m. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)

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482 KiB

Open AccessArticle

A Multi-Sensor Approach to Examining the Distribution of Total Suspended Matter (TSM) in the Albemarle-Pamlico Estuarine System, NC, USA

by Richard L. Miller, Cheng-Chien Liu, Christopher J. Buonassissi and An-Ming Wu

Remote Sens. 2011, 3(5), 962-974; https://doi.org/10.3390/rs3050962 - 13 May 2011

Cited by 40 | Viewed by 10755

Abstract

For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have [...] Read more.

For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have demonstrated that TSM or suspended sediments can be successfully mapped using MODIS 250 m data for relatively large water bodies, MODIS 250 m data is of more limited use for smaller estuaries and bays or aquatic systems with complex shoreline geometry. To adequately examine TSM in the Albemarle-Pamlico Estuarine System (APES) of North Carolina, the large-scale synoptic view of MODIS and the higher spatial resolution of other sensors are required. MODIS, Landsat 7 ETM+ and FORMOSAT-2 remote sensing instrument (RSI) data were collected on 8 November, 24 November and 10 December, 2010. Using TSM images (mg/L) derived from MODIS 250 m band 1 (620–670 nm) data, Landsat 7 ETM+ 30 m band 3 (630–690 nm) and FORMOSAT-2 RSI 8 m band 3 (630−690 nm) atmospherically corrected images were calibrated to TSM for select areas of the APES. There was a significant linear relationship between both Landsat 7 ETM+ (r² = 0.87, n = 599, P < 0.001) and FORMOSAT-2 RSI (r² = 0.95, n = 583, P < 0.001) reflectance images and MODIS-derived TSM concentrations, thus providing consistent estimates of TSM at 250, 30 and 8 m pixel resolutions. This multi-sensor approach will support a broad range of investigations on the water quality of the APES and help guide sampling schemes of future field campaigns. Full article

(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)

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938 KiB

Open AccessArticle

Comparison of Grid-Based and Segment-Based Estimation of Forest Attributes Using Airborne Laser Scanning and Digital Aerial Imagery

by Sakari Tuominen and Reija Haapanen

Remote Sens. 2011, 3(5), 945-961; https://doi.org/10.3390/rs3050945 - 12 May 2011

Cited by 12 | Viewed by 7273

Abstract

Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory [...] Read more.

Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and the forest variables estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image segments are particularly useful as the basis for generation of the silvicultural treatment and cutting units since their boundaries should follow the actual stand borders, whereas when using grid elements it is typical that some of them cover parts of several forest stands. The proportion of the so-called mixed cells depends on the size of the grid elements and the average size and shape of the stands. In this study, we carried out automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating micro-stands that are homogeneous in relation to their forest attributes. Further, we extracted laser and aerial image features for both systematic grid elements and segments. For both units, the feature set used for estimating the forest attributes was selected by means of a genetic algorithm. Of the features selected, the majority (61–79%) were based on the airborne laser scanning data. Despite the theoretical advantages of the image segments, the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in estimation of forest attributes. We conclude that estimation should be carried out at grid level with an area-specific combination of features and estimates for image segments to be derived on the basis of the grid-level estimates. Full article

(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)

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1292 KiB

Open AccessArticle

Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data

by Christoph Straub and Barbara Koch

Remote Sens. 2011, 3(5), 929-944; https://doi.org/10.3390/rs3050929 - 4 May 2011

Cited by 32 | Viewed by 8764

Abstract

So far, only a few studies have been carried out in central European forests to estimate individual tree stem volume of pine trees from high resolution remote sensing data. In this article information derived from airborne laser scanner and multispectral line scanner data [...] Read more.

So far, only a few studies have been carried out in central European forests to estimate individual tree stem volume of pine trees from high resolution remote sensing data. In this article information derived from airborne laser scanner and multispectral line scanner data were tested to predict the stem volume of 178 pines (Pinus sylvestris) in a study site in the south-west of Germany. First, tree crowns were automatically delineated using both multispectral and laser scanner data. Next, tree height, crown diameter and crown volume were derived for each crown segment. All combinations of the derived tree features were used as explanatory variables in allometric models to predict the stem volume. A model with tree height and crown diameter had the best performance with respect to the prediction accuracy determined by a leave-one-out cross-validation: Root Mean Square Error (RMSE) = 24.02% and Bias = 1.36%. Full article

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1133 KiB

Open AccessReview

Remote Sensing of Mangrove Ecosystems: A Review

by Claudia Kuenzer, Andrea Bluemel, Steffen Gebhardt, Tuan Vo Quoc and Stefan Dech

Remote Sens. 2011, 3(5), 878-928; https://doi.org/10.3390/rs3050878 - 27 Apr 2011

Cited by 573 | Viewed by 49025

Abstract

Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal [...] Read more.

Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations. Full article

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2164 KiB

Open AccessArticle

Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data

by Damian Bargiel and Sylvia Herrmann

Remote Sens. 2011, 3(5), 859-877; https://doi.org/10.3390/rs3050859 - 27 Apr 2011

Cited by 70 | Viewed by 12166

Abstract

Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the [...] Read more.

Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems. Full article

(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)

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669 KiB

Open AccessArticle

Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

by Kande R.M.U. Bandara, Lal Samarakoon, Rajendra P. Shrestha and Yoshikazu Kamiya

Remote Sens. 2011, 3(5), 845-858; https://doi.org/10.3390/rs3050845 - 27 Apr 2011

Cited by 18 | Viewed by 10876

Abstract

At present, most of the digital data acquisition methods generate Digital Surface Model (DSM) and not a Digital Elevation Model (DEM). Conversion from DSM to DEM still has some drawbacks, especially the removing of off terrain point clouds and subsequently the generation of [...] Read more.

At present, most of the digital data acquisition methods generate Digital Surface Model (DSM) and not a Digital Elevation Model (DEM). Conversion from DSM to DEM still has some drawbacks, especially the removing of off terrain point clouds and subsequently the generation of DEM within these spaces even though the methods are automated. In this paper it was intended to overcome this issue by attempting to project off terrain point clouds to the terrain in forest areas using Artificial Neural Networks (ANN) instead of removing them and then filling gaps by interpolation. Five sites were tested and accuracies assessed. They all give almost the same results. In conclusion, the ANN has ability to obtain the DEM by projecting the DSM point clouds and greater accuracies of DEMs were obtained. If the size of the hollow areas resulting from the removal of DSM point clouds are larger the accuracies are reduced. Full article

(This article belongs to the Special Issue Remote Sensing in Natural and Cultural Heritage)

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839 KiB

Open AccessArticle

Global Distribution of Cloud Top Height as Retrieved from SCIAMACHY Onboard ENVISAT Spaceborne Observations

by Alexander Kokhanovsky, Marco Vountas and John P. Burrows

Remote Sens. 2011, 3(5), 836-844; https://doi.org/10.3390/rs3050836 - 27 Apr 2011

Cited by 14 | Viewed by 8422

Abstract

The spatial and temporal analysis of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT global cloud top height data for 2003–2006 is presented. The cloud top height is derived using a semi-analytical cloud top height retrieval algorithm based on an [...] Read more.

The spatial and temporal analysis of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT global cloud top height data for 2003–2006 is presented. The cloud top height is derived using a semi-analytical cloud top height retrieval algorithm based on an asymptotic solution of the radiative transfer equation in the oxygen A-band. The analysis is valid for thick clouds only. As expected, clouds are higher in the equatorial region. The cloud altitudes decrease towards the Poles due to the general decrease of the troposphere height. The global average cloud top height as derived from SCIAMACHY measurements is 7.3 km. We also studied the planetary reflectivity R at 443 nm and found that the annual average is R = 0.49 ± 0.08 for the years analyzed. Full article

(This article belongs to the Special Issue Atmospheric Remote Sensing)

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Remote Sens., Volume 3, Issue 5 (May 2011) – 12 articles , Pages 836-1066

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