Remote Sensing

805 KiB

Open AccessArticle

Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data

by Hirofumi Hashimoto, Weile Wang, Cristina Milesi, Michael A. White, Sangram Ganguly, Minoru Gamo, Ryuichi Hirata, Ranga B. Myneni and Ramakrishna R. Nemani

Remote Sens. 2012, 4(1), 303-326; https://doi.org/10.3390/rs4010303 - 23 Jan 2012

Cited by 46 | Viewed by 10453

Abstract

Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach [...] Read more.

Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) in capturing the seasonal and the annual variability of GPP estimates from an optimal network of 21 FLUXNET forest towers sites. The tested indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by plant canopies (FPAR). Our results indicated that single vegetation indices captured 50–80% of the variability of tower-estimated GPP, but no one index performed universally well in all situations. In particular, EVI outperformed the other MODIS products in tracking seasonal variations in tower-estimated GPP, but annual mean MODIS LAI was the best estimator of the spatial distribution of annual flux-tower GPP (GPP = 615 × LAI − 376, where GPP is in g C/m²/year). This simple algorithm rehabilitated earlier approaches linking ground measurements of LAI to flux-tower estimates of GPP and produced annual GPP estimates comparable to the MODIS 17 GPP product. As such, remote sensing-based estimates of GPP continue to offer a useful alternative to estimates from biophysical models, and the choice of the most appropriate approach depends on whether the estimates are required at annual or sub-annual temporal resolution. Full article

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1702 KiB

Open AccessArticle

Environmental and Sensor Limitations in Optical Remote Sensing of Coral Reefs: Implications for Monitoring and Sensor Design

by John D. Hedley, Chris M. Roelfsema, Stuart R. Phinn and Peter J. Mumby

Remote Sens. 2012, 4(1), 271-302; https://doi.org/10.3390/rs4010271 - 23 Jan 2012

Cited by 103 | Viewed by 13423

Abstract

A generic method was developed for analysing the capabilities of optical remote sensing of aquatic systems in terms of environmental components and imaging sensor configurations. The method was based on a component based model of the entire system in which not only benthic [...] Read more.

A generic method was developed for analysing the capabilities of optical remote sensing of aquatic systems in terms of environmental components and imaging sensor configurations. The method was based on a component based model of the entire system in which not only benthic composition but other environmental components such as water inherent optical properties (IOPs), bathymetry, sun elevation, wind speed and sensor noise characteristics were defined by datasets with the potential to include across-image variation. The model was applied to data from Pacific Ocean reefs in an airborne sensor context to estimate the primary environmental or sensor factors confounding discrimination of benthic mixtures of key reef types: live coral, bleached coral, dead coral and macroalgae. Results indicate that spectral variation of benthic types and sub-pixel mixing is the primary limiting factor for benthic mapping objectives, whereas instrument noise levels are a minor factor. Full article

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Graphical abstract

726 KiB

Open AccessArticle

Characterization of Rape Field Microwave Emission and Implications to Surface Soil Moisture Retrievals

by Florian Schlenz, Joachim Fallmann, Philip Marzahn, Alexander Loew and Wolfram Mauser

Remote Sens. 2012, 4(1), 247-270; https://doi.org/10.3390/rs4010247 - 16 Jan 2012

Cited by 15 | Viewed by 7732

Abstract

In the course of Soil Moisture and Ocean Salinity (SMOS) mission calibration and validation activities, a ground based L-band radiometer ELBARA II was situated at the test site Puch in Southern Germany in the Upper Danube Catchment. The experiment is described and the [...] Read more.

In the course of Soil Moisture and Ocean Salinity (SMOS) mission calibration and validation activities, a ground based L-band radiometer ELBARA II was situated at the test site Puch in Southern Germany in the Upper Danube Catchment. The experiment is described and the different data sets acquired are presented. The L-band microwave emission of the biosphere (L-MEB) model that is also used in the SMOS L2 soil moisture algorithm is used to simulate the microwave emission of a winter oilseed rape field in Puch that was also observed by the radiometer. As there is a lack of a rape parameterization for L-MEB the SMOS default parameters for crops are used in a first step which does not lead to satisfying modeling results. Therefore, a new parameterization for L-MEB is developed that allows us to model the microwave emission of a winter oilseed rape field at the test site with better results. The soil moisture retrieval performance of the new parameterization is assessed in different retrieval configurations and the results are discussed. To allow satisfying results, the periods before and after winter have to be modeled with different parameter sets as the vegetation behavior is very different during these two development stages. With the new parameterization it is possible to retrieve soil moisture from multiangular brightness temperature data with a root mean squared error around 0.045–0.051 m³/m³ in a two parameter retrieval with soil moisture and roughness parameter H_r as free parameters. Full article

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842 KiB

Open AccessArticle

Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data and Ground Measurements

by Pavel Propastin and Martin Kappas

Remote Sens. 2012, 4(1), 220-246; https://doi.org/10.3390/rs4010220 - 13 Jan 2012

Cited by 15 | Viewed by 8035

Abstract

A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has [...] Read more.

A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has been retrieved using an algorithm based on a physical radiative transfer model establishing a relationship between LAI and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation at the per-pixel scale. The results revealed high consistencies between the produced AVHRR LAI data set and ground truth information and the 30-m resolution Landsat ETM+ LAI estimated using the similar algorithm. Differences in LAI between the AVHRR-based product and the Landsat ETM+-based product are lower than 0.4 LAI units in terms of RMSE. The produced Kazakhstan-wide LAI was also compared with the global 8-km AVHRR LAI (LAI_PAL_BU_V3) and 1-km MODIS LAI (MOD15A2 LAI) products. Results show remarkable consistency of the spatial distribution and temporal dynamics between the new LAI product and both examined global LAI products. However, the results also revealed several discrepancies in LAI estimates when comparing the global and the Kazakhstan-wide products. The discrepancies in LAI estimates were outlined and discussed. Full article

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1376 KiB

Open AccessArticle

Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

by Rebecca Barker and Douglas J. King

Remote Sens. 2012, 4(1), 194-219; https://doi.org/10.3390/rs4010194 - 11 Jan 2012

Cited by 13 | Viewed by 10352

Abstract

Blanding’s turtle (Emydoidea blandingii) is a threatened species under Canada’s Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of [...] Read more.

Blanding’s turtle (Emydoidea blandingii) is a threatened species under Canada’s Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding’s turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer’s accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding’s turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management. Full article

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Graphical abstract

856 KiB

Open AccessArticle

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

by Christian Nansen

Remote Sens. 2012, 4(1), 180-193; https://doi.org/10.3390/rs4010180 - 11 Jan 2012

Cited by 24 | Viewed by 8864

Abstract

A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids ( [...] Read more.

A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L.) at multiple time points in 2 data sets (229 hyperspectral images), and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters), it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values) could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf) and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods. Full article

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

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301 KiB

Open AccessArticle

Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

by Kenta Obata, Takahiro Wada, Tomoaki Miura and Hiroki Yoshioka

Remote Sens. 2012, 4(1), 160-179; https://doi.org/10.3390/rs4010160 - 10 Jan 2012

Cited by 12 | Viewed by 7636

Abstract

Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple [...] Read more.

Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple synchronized satellite sensors permits long-term monitoring with high spatial and temporal resolutions. However, differences in the spatial resolution of images collected by different sensors can introduce systematic biases, called scaling effects, into the biophysical retrievals. This study investigates the mechanism by which the scaling effects distort normalized difference vegetation index (NDVI). This study focused on the monotonicity of the area-averaged NDVI as a function of the spatial resolution. A monotonic relationship was proved analytically by using the resolution transform model proposed in this study in combination with a two-endmember linear mixture model. The monotonicity allowed the inherent uncertainties introduced by the scaling effects (error bounds) to be explicitly determined by averaging the retrievals at the extrema of theresolutions. Error bounds could not be estimated, on the other hand, for non-monotonic relationships. Numerical simulations were conducted to demonstrate the monotonicity of the averaged NDVI along spatial resolution. This study provides a theoretical basis for the scaling effects and develops techniques for rectifying the scaling effects in biophysical retrievals to facilitate cross-sensor calibration for the long-term monitoring of vegetation dynamics. Full article

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2116 KiB

Open AccessArticle

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

by Cristina Gómez, Michael A. Wulder, Fernando Montes and José A. Delgado

Remote Sens. 2012, 4(1), 135-159; https://doi.org/10.3390/rs4010135 - 10 Jan 2012

Cited by 43 | Viewed by 10708

Abstract

Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon [...] Read more.

Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR) imagery and related techniques to model structural parameters at the stand level (n = 490) in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively) served to model structural parameters. Classification and Regression Tree Analysis (CART) was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD) (R² = 0.8; RMSE = 0.13 m) with an average error of 17% while basal area (BA) models produced an average error of 22% (RMSE = 5.79 m²/ha). When the measured number of trees per unit area (N) was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from HSR imagery captured from space-borne platforms have capacity to inform on local structural parameters of Mediterranean pines. The nascent program for annual national coverages of HSR imagery over Spain offers unique opportunities for forest structural attribute estimation; whereby, depletions can be readily captured and successive annual collections of data can support or enable refinement of attributes. Further, HSR imagery and associated attribute estimation techniques can be used in conjunction, not necessarily in competition to, more traditional forest inventory with synergies available through provision of data within an inventory cycle and the capture of forest disturbance or depletions. Full article

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349 KiB

Open AccessArticle

Detecting Climate Effects on Vegetation in Northern Mixed Prairie Using NOAA AVHRR 1-km Time-Series NDVI Data

by Zhaoqin Li and Xulin Guo

Remote Sens. 2012, 4(1), 120-134; https://doi.org/10.3390/rs4010120 - 6 Jan 2012

Cited by 42 | Viewed by 9437

Abstract

Grasslands hold varied grazing capacity, provide multiple habitats for diverse wildlife, and are a key component of carbon stock. Research has indicated that grasslands are experiencing effects related to recent climate trends. Understanding how grasslands respond to climate variation thus is essential. However, [...] Read more.

Grasslands hold varied grazing capacity, provide multiple habitats for diverse wildlife, and are a key component of carbon stock. Research has indicated that grasslands are experiencing effects related to recent climate trends. Understanding how grasslands respond to climate variation thus is essential. However, it is difficult to separate the effects of climate variation from grazing. This study aims to document vegetation condition under climate variation in Grasslands National Park (GNP) of Canada, a grassland ecosystem without grazing for over 20 years, using Normalized Difference Vegetation Index (NDVI) data to establish vegetation baselines. The main findings are (1) precipitation has more effects than temperature on vegetation; (2) the growing season of vegetation had an expanding trend indicated by earlier green-up and later senescence; (3) phenologically-tuned annual NDVI had an increasing trend from 1985 to 2007; and (4) the baselines of annual NDVI range from 0.13 to 0.32, and only the NDVI in 1999 is beyond the upper bound of the baseline. Our results indicate that vegetation phenology and condition have slightly changed in GNP since 1985, although vegetation condition in most years was still within the baselines. Full article

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215 KiB

Open AccessCommunication

Beyond Range: Innovating Fluorescence Microscopy

by Alessandro Esposito

Remote Sens. 2012, 4(1), 111-119; https://doi.org/10.3390/rs4010111 - 5 Jan 2012

Cited by 16 | Viewed by 9695

Abstract

Time-of-Flight (ToF) technologies are developed mainly for range estimations in industrial applications or consumer products. Recently, it was realized that ToF sensors could also be used for the detection of fluorescence and of the minute changes in the nanosecond-lived electronic states of fluorescent [...] Read more.

Time-of-Flight (ToF) technologies are developed mainly for range estimations in industrial applications or consumer products. Recently, it was realized that ToF sensors could also be used for the detection of fluorescence and of the minute changes in the nanosecond-lived electronic states of fluorescent molecules. This capability can be exploited to report on the biochemical processes occurring within living organisms. ToF technologies, therefore, provide new opportunities in molecular and cell biology, diagnostics, and drug discovery. In this short communication, the convergence of the engineering and biomedical communities onto ToF technologies and its potential impact on basic, applied and translational sciences are discussed. Full article

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

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2172 KiB

Open AccessArticle

Extraction of Objects from Terrestrial Laser Scans by Integrating Geometry Image and Intensity Data with Demonstration on Trees

by Shahar Barnea and Sagi Filin

Remote Sens. 2012, 4(1), 88-110; https://doi.org/10.3390/rs4010088 - 5 Jan 2012

Cited by 19 | Viewed by 9613

Abstract

Terrestrial laser scanning is becoming a standard for 3D modeling of complex scenes. Results of the scan contain detailed geometric information about the scene; however, the lack of semantic details still constitutes a gap in ensuring this data is usable for mapping. This [...] Read more.

Terrestrial laser scanning is becoming a standard for 3D modeling of complex scenes. Results of the scan contain detailed geometric information about the scene; however, the lack of semantic details still constitutes a gap in ensuring this data is usable for mapping. This paper proposes a framework for recognition of objects in laser scans; aiming to utilize all the available information, range, intensity and color information integrated into the extraction framework. Instead of using the 3D point cloud, which is complex to process since it lacks an inherent neighborhood structure, we propose a polar representation which facilitates low-level image processing tasks, e.g., segmentation and texture modeling. Using attributes of each segment, a feature space analysis is used to classify segments into objects. This process is followed by a fine-tuning stage based on graph-cut algorithm, which considers the 3D nature of the data. The proposed algorithm is demonstrated on tree extraction and tested on scans containing complex objects in addition to trees. Results show a very high detection level and thereby the feasibility of the proposed framework. Full article

(This article belongs to the Special Issue Terrestrial Laser Scanning)

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Graphical abstract

1253 KiB

Open AccessArticle

Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

by Ana Paula Dal’Asta, Newton Brigatti, Silvana Amaral, Maria Isabel Sobral Escada and Antonio Miguel Vieira Monteiro

Remote Sens. 2012, 4(1), 68-87; https://doi.org/10.3390/rs4010068 - 4 Jan 2012

Cited by 10 | Viewed by 8095

Abstract

Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway [...] Read more.

Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera) images (5 m) merged with CBERS-CCD (Charge Coupled Device) images (20 m) were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon. Full article

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2419 KiB

Open AccessArticle

Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

by Misganu Debella-Gilo and Andreas Kääb

Remote Sens. 2012, 4(1), 43-67; https://doi.org/10.3390/rs4010043 - 4 Jan 2012

Cited by 51 | Viewed by 10547

Abstract

Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching [...] Read more.

Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM) approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10⁻⁴ (one level of significance below the measured values) as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated) circumstances and by about 25% for real multi-temporal images of mass movements. Full article

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427 KiB

Open AccessArticle

Understanding and Ameliorating Non-Linear Phase and Amplitude Responses in AMCW Lidar

by John P. Godbaz, Michael J. Cree and Adrian A. Dorrington

Remote Sens. 2012, 4(1), 21-42; https://doi.org/10.3390/rs4010021 - 23 Dec 2011

Cited by 28 | Viewed by 9228

Abstract

Amplitude modulated continuous wave (AMCW) lidar systems commonly suffer from non-linear phase and amplitude responses due to a number of known factors such as aliasing and multipath inteference. In order to produce useful range and intensity information it is necessary to remove these [...] Read more.

Amplitude modulated continuous wave (AMCW) lidar systems commonly suffer from non-linear phase and amplitude responses due to a number of known factors such as aliasing and multipath inteference. In order to produce useful range and intensity information it is necessary to remove these perturbations from the measurements. We review the known causes of non-linearity, namely aliasing, temporal variation in correlation waveform shape and mixed pixels/multipath inteference. We also introduce other sources of non-linearity, including crosstalk, modulation waveform envelope decay and non-circularly symmetric noise statistics, that have been ignored in the literature. An experimental study is conducted to evaluate techniques for mitigation of non-linearity, and it is found that harmonic cancellation provides a significant improvement in phase and amplitude linearity. Full article

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

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1397 KiB

Open AccessArticle

Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest

by L. Monika Moskal and Guang Zheng

Remote Sens. 2012, 4(1), 1-20; https://doi.org/10.3390/rs4010001 - 23 Dec 2011

Cited by 190 | Viewed by 17577

Abstract

We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived [...] Read more.

We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile- or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments. Full article

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Remote Sens., Volume 4, Issue 1 (January 2012) – 15 articles , Pages 1-326

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