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5811 KiB

Open AccessArticle

Data Base Design with GIS in Ecosystem Based Multiple Use Forest Management in Artvin, Turkey: A Case Study in Balcı Forest Management Planning Unit

by Hacı Ahmet Yolasığmaz and Sedat Keleş

Sensors 2009, 9(3), 1644-1661; https://doi.org/10.3390/s90301644 - 10 Mar 2009

Cited by 5 | Viewed by 12128

Abstract

In Turkey, the understanding of planning focused on timber production has given its place on Multiple Use Management (MUM). Because the whole infrastructure of forestry with inventory system leading the way depends on timber production, some cases of bottle neck are expected during [...] Read more.

In Turkey, the understanding of planning focused on timber production has given its place on Multiple Use Management (MUM). Because the whole infrastructure of forestry with inventory system leading the way depends on timber production, some cases of bottle neck are expected during the transition period. Database design, probably the most important stage during the transition to MUM, together with the digital basic maps making up the basis of this infrastructure constitute the main point of this article. Firstly, the forest management philosophy of Turkey in the past was shortly touched upon in the article. Ecosystem Based Multiple Use Forest Management (EBMUFM) approaches was briefly introduced. The second stage of the process of EBMUFM, database design was described by examining the classical planning infrastructure and the coverage to be produced and consumed were suggested in the form of lists. At the application stage, two different geographical databases were established with GIS in Balcı Planning Unit of the years 1984 and 2006. Following that the related basic maps are produced. Timely diversity of the planning unit of 20 years is put forward comparatively with regard to the stand parameters such as tree types, age class, development stage, canopy closure, mixture, volume and increment. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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271 KiB

Open AccessArticle

Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data

by Tim De Roeck, Tim Van de Voorde and Frank Canters

Sensors 2009, 9(1), 22-45; https://doi.org/10.3390/s90100022 - 5 Jan 2009

Cited by 13 | Viewed by 10852

Abstract

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for [...] Read more.

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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615 KiB

Open AccessArticle

Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression

by Georgios Mallinis and Nikos Koutsias

Sensors 2008, 8(12), 8067-8085; https://doi.org/10.3390/s8128067 - 8 Dec 2008

Cited by 19 | Viewed by 11623

Abstract

Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral [...] Read more.

Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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539 KiB

Open AccessArticle

An Annual Plant Growth Proxy in the Mojave Desert Using MODIS-EVI Data

by Cynthia S.A. Wallace and Kathryn A. Thomas

Sensors 2008, 8(12), 7792-7808; https://doi.org/10.3390/s8127792 - 3 Dec 2008

Cited by 24 | Viewed by 14783

Abstract

In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models [...] Read more.

In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models as proxies for annual plant cover using metrics that captured phenological variability in Moderate-Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite images. We used landscape phenologies revealed in MODIS data together with ecological knowledge of annual plant seasonality to develop a suite of metrics to describe annual growth on a yearly basis. Each of these metrics was applied to temporally-composited MODIS-EVI images to develop a relative model of annual growth. Each model was evaluated by testing how well it predicted field estimates of annual cover collected during 2003 and 2005 at the Mojave National Preserve. The best performing metric was the spring difference metric, which compared the average of three spring MODIS-EVI composites of a given year to that of 2002, a year of record drought. The spring difference metric showed correlations with annual plant cover of R² = 0.61 for 2005 and R² = 0.47 for 2003. Although the correlation is moderate, we consider it supportive given the characteristics of the field data, which were collected for a different study in a localized area and are not ideal for calibration to MODIS pixels. A proxy for annual growth potential was developed from the spring difference metric of 2005 for use as an environmental data layer in desert tortoise habitat modeling. The application of the spring difference metric to other imagery years presents potential for other applications such as fuels, invasive species, and dust-emission monitoring in the Mojave Desert. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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771 KiB

Open AccessArticle

Classification Metrics for Improved Atmospheric Correction of Multispectral VNIR Imagery

by Rudolf Richter

Sensors 2008, 8(11), 6999-7011; https://doi.org/10.3390/s8116999 - 5 Nov 2008

Cited by 6 | Viewed by 10379

Abstract

Multispectral visible/near-infrared (VNIR) earth observation satellites, e.g., Ikonos, Quickbird, ALOS AVNIR-2, and DMC, usually acquire imagery in a few (3 – 5) spectral bands. Atmospheric correction is a challenging task for these images because the standard methods require at least one shortwave infrared [...] Read more.

Multispectral visible/near-infrared (VNIR) earth observation satellites, e.g., Ikonos, Quickbird, ALOS AVNIR-2, and DMC, usually acquire imagery in a few (3 – 5) spectral bands. Atmospheric correction is a challenging task for these images because the standard methods require at least one shortwave infrared band (around 1.6 or 2.2 µm) or hyperspectral instruments to derive the aerosol optical thickness. New classification metrics for defining cloud, cloud over water, haze, water, and saturation are presented to achieve improvements for an automatic processing system. The background is an ESA contract for the development of a prototype atmospheric processor for the optical payload AVNIR-2 on the ALOS platform. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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Graphical abstract

1154 KiB

Open AccessCommunication

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey

by Selçuk Reis

Sensors 2008, 8(10), 6188-6202; https://doi.org/10.3390/s8106188 - 1 Oct 2008

Cited by 216 | Viewed by 32893

Abstract

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard [...] Read more.

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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Graphical abstract

757 KiB

Open AccessArticle

Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries

by Alexander Savelyev and Ramanathan Sugumaran

Sensors 2008, 8(8), 5055-5068; https://doi.org/10.3390/s8085055 - 25 Aug 2008

Cited by 15 | Viewed by 14698

Abstract

The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, [...] Read more.

The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixelto-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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145 KiB

Open AccessArticle

Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

by Raita Säynäjoki, Petteri Packalén, Matti Maltamo, Mikko Vehmas and Kalle Eerikäinen

Sensors 2008, 8(8), 5037-5054; https://doi.org/10.3390/s8085037 - 25 Aug 2008

Cited by 24 | Viewed by 12567

Abstract

The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m [...] Read more.

The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90^th percentile and the proportion of laser points reflected at the 60^th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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3372 KiB

Open AccessArticle

Empirical Retrieval of Surface Melt Magnitude from Coupled MODIS Optical and Thermal Measurements over the Greenland Ice Sheet during the 2001 Ablation Season

by Derrick Lampkin and Rui Peng

Sensors 2008, 8(8), 4915-4947; https://doi.org/10.3390/s8084915 - 22 Aug 2008

Cited by 5 | Viewed by 11500

Abstract

Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of [...] Read more.

Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of melt onset or extent. Retrieval of surface melt magnitude is necessary to improve understanding of ice sheet flow and surface melt coupling. In this paper, we report on a new technique to quantify the magnitude of surface melt. Cloud-free dates of June 10, July 5, 7, 9, and 11, 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance Band 5 (1.230-1.250μm) and surface temperature images rescaled to 1km over western Greenland were used in the retrieval algorithm. An optical-thermal feature space partitioned as a function of melt magnitude was derived using a one-dimensional thermal snowmelt model (SNTHERM89). SNTHERM89 was forced by hourly meteorological data from the Greenland Climate Network (GC-Net) at reference sites spanning dry snow, percolation, and wet snow zones in the Jakobshavn drainage basin in western GIS. Melt magnitude or effective melt (E-melt) was derived for satellite composite periods covering May, June, and July displaying low fractions (0-1%) at elevations greater than 2500m and fractions at or greater than 15% at elevations lower than 1000m assessed for only the upper 5 cm of the snow surface. Validation of E-melt involved comparison of intensity to dry and wet zones determined from QSCAT backscatter. Higher intensities (> 8%) were distributed in wet snow zones, while lower intensities were grouped in dry zones at a first order accuracy of ~ ±2%. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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6855 KiB

Open AccessArticle

Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

by Martin Rutzinger, Bernhard Höfle, Markus Hollaus and Norbert Pfeifer

Sensors 2008, 8(8), 4505-4528; https://doi.org/10.3390/s8084505 - 4 Aug 2008

Cited by 131 | Viewed by 20093

Abstract

Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and [...] Read more.

Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (> 20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements directly, i.e. the acquired points. Gridding of the data is not necessary, a process which is inherently coupled to loss of data and precision. The 3D properties provide especially a good separability of buildings and terrain points respectively, if they are occluded by vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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1262 KiB

Open AccessArticle

Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

by Camille C. D. Lelong, Philippe Burger, Guillaume Jubelin, Bruno Roux, Sylvain Labbé and Frédéric Baret

Sensors 2008, 8(5), 3557-3585; https://doi.org/10.3390/s8053557 - 26 May 2008

Cited by 336 | Viewed by 24555

Abstract

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In [...] Read more.

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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300 KiB

Open AccessArticle

Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.

by Minha Choi and Jennifer M. Jacobs

Sensors 2008, 8(4), 2617-2627; https://doi.org/10.3390/s8042617 - 14 Apr 2008

Cited by 22 | Viewed by 11206

Abstract

Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM [...] Read more.

Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E’s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R² values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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597 KiB

Open AccessArticle

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval

by Desheng Liu and Ruiliang Pu

Sensors 2008, 8(4), 2695-2706; https://doi.org/10.3390/s8042695 - 6 Apr 2008

Cited by 97 | Viewed by 11964

Abstract

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for [...] Read more.

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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422 KiB

Open AccessArticle

Satellite-based Flood Modeling Using TRMM-based Rainfall Products

by Amanda Harris, Sayma Rahman, Faisal Hossain, Lance Yarborough, Amvrossios C. Bagtzoglou and Greg Easson

Sensors 2007, 7(12), 3416-3427; https://doi.org/10.3390/s7123416 - 20 Dec 2007

Cited by 75 | Viewed by 13198

Abstract

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25^o to [...] Read more.

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25^o to 0.50^o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km² Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA’s Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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4904 KiB

Open AccessArticle

Global Distribution and Density of Constructed Impervious Surfaces

by Christopher D. Elvidge, Benjamin T. Tuttle, Paul C. Sutton, Kimberly E. Baugh, Ara T. Howard, Cristina Milesi, Budhendra Bhaduri and Ramakrishna Nemani

Sensors 2007, 7(9), 1962-1979; https://doi.org/10.3390/s7091962 - 21 Sep 2007

Cited by 402 | Viewed by 25487

Abstract

We present the first global inventory of the spatial distribution and density ofconstructed impervious surface area (ISA). Examples of ISA include roads, parking lots,buildings, driveways, sidewalks and other manmade surfaces. While high spatialresolution is required to observe these features, the new product reports [...] Read more.

We present the first global inventory of the spatial distribution and density ofconstructed impervious surface area (ISA). Examples of ISA include roads, parking lots,buildings, driveways, sidewalks and other manmade surfaces. While high spatialresolution is required to observe these features, the new product reports the estimateddensity of ISA on a one-km² grid based on two coarse resolution indicators of ISA – thebrightness of satellite observed nighttime lights and population count. The model wascalibrated using 30-meter resolution ISA of the USA from the U.S. Geological Survey.Nominally the product is for the years 2000-01 since both the nighttime lights andreference data are from those two years. We found that 1.05% of the United States landarea is impervious surface (83,337 km²) and 0.43 % of the world’s land surface (579,703km²) is constructed impervious surface. China has more ISA than any other country(87,182 km²), but has only 67 m² of ISA per person, compared to 297 m² per person in theUSA. The distribution of ISA in the world’s primary drainage basins indicates that watersheds damaged by ISA are primarily concentrated in the USA, Europe, Japan, China and India. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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1816 KiB

Open AccessArticle

Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment

by Markus Hollaus, Wolfgang Wagner, Bernhard Maier and Klemens Schadauer

Sensors 2007, 7(8), 1559-1577; https://doi.org/10.3390/s7081559 - 17 Aug 2007

Cited by 96 | Viewed by 14671

Abstract

Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth’s surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas most [...] Read more.

Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth’s surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas most of the laser pulses are reflected by the leaves and branches of the trees, but a certain fraction of the laser pulses reaches the forest floor through small gaps in the canopy. Thus it is possible to reconstruct both the three-dimensional structure of the forest canopy and the terrain surface. For the retrieval of quantitative forest parameters such as stem volume or biomass it is necessary to use models that combine ALS with inventory data. One approach is to use multiplicative regression models that are trained with local inventory data. This method has been widely applied over boreal forest regions, but so far little experience exists with applying this method for mapping alpine forest. In this study the transferability of this approach to a 128 km2 large mountainous region in Vorarlberg, Austria, was evaluated. For the calibration of the model, inventory data as operationally collected by Austrian foresters were used. Despite these inventory data are based on variable sample plot sizes, they could be used for mapping stem volume for the entire alpine study area. The coefficient of determination R2 was 0.85 and the root mean square error (RMSE) 90.9 m3ha-1 (relative error of 21.4%) which is comparable to results of ALS studies conducted over topographically less complex environments. Due to the increasing availability, ALS data could become an operational part of Austrian’s forest inventories. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

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