Light Detection And Ranging Imagery Research Articles

Multi-Temporal Trend Analysis of Coastal Vegetation Using Metrics Derived from Hyperspectral and LiDAR Data

Monitoring and modeling of coastal vegetation and wetland systems are considered major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem conditions and change. This study involves the extraction of vegetation metrics from airborne LiDAR (Light Detection and Ranging) and hyperspectral imagery (HSI) to quantify coastal dune vegetation characteristics and assesses landscape-level trends from those derived metrics. HSI- and LiDAR-derived elevation (digital elevation model) and vegetation metrics (canopy height model, leaf area index, and normalized difference vegetation index) were used in conjunction with per-pixel linear regression and hot spot analyses to evaluate hurricane-induced spatial and temporal changes in elevation and vegetation properties. These assessments showed areas with greatest decreases in vegetation metric values were associated with direct tropical storm energies and processes (i.e., overwashing events eroding beach and dune features), while those with the greatest increases in vegetation metric values were in areas where overwashed sediments were distributed. This study narrows existing gaps in dune vegetation data by advancing new methodologies to classify, quantify, and estimate critical coastal vegetation metrics. The tools and methods developed in this study will ultimately improve future estimates and predictions of nearshore dynamics and impacts from disturbance events.

Three-dimensional numerical modelling of floods in river corridor with complex vegetation quantified using airborne LiDAR imagery

Excessive flood flow over the historic diversion weir in the vegetated Asahi River in Okayama Prefecture, Japan, was recently recorded for the first time after its renovation work. Fluvial researchers analysed the diversion discharge for flood mitigation measures through laboratory studies and conventional two-dimensional (2-D) depth-averaged simulations. The existing model was insufficient for simulation of certain phenomena such as flow resistance caused by vegetation branches and leaves and vertical flow distribution around the river corridor. Therefore, we developed a three-dimensional (3-D) vegetation resistance porous model by estimating topography, land cover, and vegetation distribution from airborne light detection and ranging (LiDAR) topo-bathymetry (ALB) data. Results show that the water level and flow regime were more reproducible than by referenced 2-D calculations when compared to space-time image velocimetry (STIV) data and field measurements. The diversion discharge designed using the proposed model is feasible with the current riverbed and vegetation conditions.

LiDAR predictive modeling of Pacific Northwest mound sites: A study of Willamette Valley Kalapuya Mounds, Oregon (USA)

Archaeologists need new methods to survey large areas and overcome environmental and archaeological barriers to site discovery in heavily forested regions. LiDAR (light detection and ranging) technology is one possible solution to these challenges as LiDAR digitally clears away vegetation, facilitating large-scale remote sensing survey. The Calapooia Watershed, located in the southern Willamette Valley of Oregon, is an ideal area to utilize LiDAR. While valley lowlands are cleared for agriculture, riverine areas remain heavily wooded and are known to contain hundreds of low-lying earthwork features created by pre-colonial Kalapuyan people. To assess the potential application of LiDAR in this region, we developed and tested a mound detection predictive model using LiDAR and aerial imagery. Field testing of the model identified seven new Kalapuyan mounds and verified the location of several others. Our model was 44 percent successful in identifying cultural mounds and 100 percent successful in identifying extant previously identified mounds. The model is effective and can be used to identify and preserve mound features in the Pacific Northwest; the model can also be modified and used to identify earthwork features in other regions.

Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.

Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery

Wetlands are among the most valuable natural resources, being highly beneficial to both the environment and humans. Therefore, it is very important to map and monitor wetlands. Although various remote sensing datasets, including optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR) imagery, have been widely applied to classify wetlands, it is still required to discuss the advantages/limitations of each of these datasets and suggest the best remote sensing methodology for wetland mapping. Thus, the Terra Nova National Park, located in Newfoundland, Canada, was initially selected as the study area to develop a supervised classification method along with object-based image analysis. To this end, different remote sensing-based scenarios were investigated using individual optical, SAR, and LiDAR datasets, as well as their various combinations. In addition, for achieving the highest accuracy, the effects of segmentation scales and the tuning parameters of the random forest (RF) classifier were examined. The results showed that a combination of optical, SAR, and LiDAR images with the segmentation scale of 150, the RF depth of 20, and the RF minimum sample number of 5 provided the highest classification accuracy with the overall accuracy of 87.2%. Moreover, based on the results, approximately 21% and 79% of the study area are covered by wetlands and nonwetlands, respectively. The proposed methodology shows an optimum scenario for future wetland classification tasks and can assist stakeholders in the effective management of wetlands and establishment of necessary policies.

EXTRACTION OF HOUSES FROM POINT CLOUD LIDAR: PROBLEMS AND CHALLENGE

Abstract. Although many efforts have been made on the extraction of houses from LiDAR (Light Detection and Ranging) and/or aerial imagery and/or their fusion, little investigation using co-registration between the orthoimage map and LiDAR on the basis of geodetic coordinates as element for house extraction. For this reason, this paper first overviews the advances of LiDAR and investigates the advantages and disadvantages of LiDAR system vs. traditional photogrammetry, and then indicates that LiDAR technology has not yet resolved all existing problems that traditional photogrammetry remained so far, such as texture information, LiDAR point cloud density. A comprehensive comparison in extraction of houses (feature information) from LiDAR data set and from aerial imagery are also presented. It has been widely accepted and admitted that full automation for extraction of houses (feature information in city area) from LiDAR point cloud has still been difficult. Therefore, this paper proposes a human-computer interaction operation for houses extraction through combination of LiDAR point cloud and the orthorectified high-resolution aerial imagery. The real data is utilized for validation of the proposed method.

Tracing the Resilience and Revitalisation of Historic Taro Production in Waipi`o Valley, Hawai`i

The resilience and revitalisation of taro/kalo agriculture in the Hawaiian contact period is analysed in Waipi‘o Valley, on the Big Island of Hawai‘i. Historic work has demonstrated the effects of colonial contact on the people of Waipi‘o. Documents from the Māhele period, census information and missionary records are combined to paint a picture of how life unfolded in Waipi‘o Valley over time. What is alluded to, and yet unexplored, is the changing production system and an overall trend of decreasing and fluctuating wetland taro production, where traditional cultivation is transformed by the introduction of rice farming. Later in time this too fades out, when taro again becomes dominant. Interestingly, wetland taro cultivation in Waipi‘o is still practiced today, with interest in revitalising the capacity of a once intensively cultivated valley. Here, the impact of rice, and other crop introductions, is explored in terms of revitalising these wetland traditions. This was done by generating “snapshots” of the landscape through time. Information detailing traditional owners, plot locations and pondfields metrics were derived from digitised historic survey maps, and modern remote sensing techniques such as high resolution LiDAR (Light detection and ranging) imagery. Combining this information not only catalogued the historic trend of declining wetland irrigation, but directly illustrates the influence of past agricultural choices on modern wetland revitalisation agendas.

Assessing changes of urban vegetation cover and aboveground carbon stocks using LiDAR and Landsat imagery data in Auckland, New Zealand

ABSTRACTUrban vegetation can help to offset carbon emissions. However, urban vegetation cover is vulnerable to urbanization. This study attempts to detect the change in vegetation cover and to quantify its impact on aboveground carbon (AGC) stocks in Auckland, New Zealand, between 1989 and 2014. Field-measured vegetation parameters were used to calculate the amount of carbon stored in plants at the plot-level. Plot-level AGC stocks were linked with vegetation spectral/structural features derived from Landsat images and Light Detection and Ranging (LiDAR) data. These data were also used to map vegetation cover and to estimate AGC stock. Vegetation cover decreased from 394.0 km2 in 1989 to 379.4 km2 in 2014. AGC stock in 1989 was estimated at 1,001,184 Mg C from Landsat 4 data. The total AGC in 2014 was estimated at 1,459,530 Mg C from Landsat 8 data. Thus, total AGC stock increased by 458,346 Mg C (45.8%) in spite of a 3.7% decrease in vegetation cover (14.6 km2) during the same period. The increase in AGC stock was derived partly from tree growth and tree plantings. Vegetation growth contributed more to the increase in AGC stock than its gain from non-vegetation to vegetation changes. The AGC stored in trees and shrubs estimated at 1,333,011 Mg C from the 2014 Landsat data is 5.7% lower than 1,414,607 Mg C estimated from the 2013 LiDAR data, due to the inability of optical imagery to capture the sub-canopy structure of forests and the saturation effect. Thus, LiDAR data provided a more accurate estimate of AGC stock, especially when the stock density is high (e.g. >97.9 Mg C ha–1).

Identification and Mapping of Soil Erosion Processes Using the Visual Interpretation of LiDAR Imagery

Soil erosion processes are a type of geological hazard. They cause soil loss and sediment production, landscape dissection, and economic damage, which can, in the long term, result in land abandonment. Thus, identification of soil erosion processes is necessary for sustainable land management in an area. This study presents the potential of visual interpretation of high resolution LiDAR (light detection and ranging) imagery for direct and unambiguous identification and mapping of soil erosion processes, which was tested in the study area of the Vinodol Valley (64.57 km2), in Croatia. Eight LiDAR images were derived from the 1 m airborne LiDAR DTM (Digital Terrain Model) and were used to identify and map gully erosion, sheet erosion, and the combined effect of rill and sheet erosion, with the ultimate purpose to create a historical erosion inventory. The two-step procedure in a visual interpretation of LiDAR imagery was performed: preliminary and detailed. In the preliminary step, possibilities and limitations for unambiguous identification of the soil erosion processes were determined for representative portions of the study area, and the exclusive criteria for the accurate and precise manual delineation of different types of erosion phenomena were established. In the detailed step, the findings from the preliminary step were used to map the soil erosion phenomena in the entire studied area. Results determined the highest potential for direct identification and mapping of the gully erosion phenomena. A total of 236 gullies were identified and precisely delineated, although most of them were previously unknown, due to the lack of previous investigations on soil erosion processes in the study area. On the other hand, the used method was proven to be inapplicable for direct identification and accurate mapping of the sheet erosion. Sheet erosion, however, could have been indirectly identified on certain LiDAR imagery, based on recognition of colluvial deposits accumulated at the foot of the eroded slopes. Furthermore, the findings of this study present which of the used LiDAR imagery, and what features of the imagery used, are most effective for identification and mapping of different types of erosion processes.

Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods.

It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables; three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios.

Developing an Automated City of Whittlesea Urban Tree Canopy Inventory Using Airborne LiDAR and Aerial Imagery

The City of Whittlesea is in Melbourne’s north and is one of the largest municipalities in metropolitan Melbourne. The council GIS team using current aerial photos from 2017 and LiDAR (“LiDAR” stands for Light Detection and Ranging) point clouds 2009 products to identify trends in urban tree canopy cover in the established suburbs of the municipality between 2009 and 2017. The tree canopy coverage was calculated for residential properties, road reservations, public land and park sites within urban parts of Bundoora, Epping, Lalor, Mill Park, Thomastown, and South Morang. From the total project area of approximately 3499 hectares a test site was selected based on an extension of one of the LIDAR data tiles (e325n5828). This project investigates only 6 meters and higher tree canopy cover within the project area. The objectives of the project were to establish a methodology to calculate the urban tree canopy coverage from LiDAR 2009 data, that can be replicated in future calculations. The calculated urban tree canopy coverage from LiDAR 2009 data is 9%, compared to 8% coverage in 2017, based on aerial photography. This estimate was compared to similar Local Governance Areas that range from 12% to 40%. A key to planning and managing urban tree canopy is first to understand the quantity, quality, tree density and distribution of the resource across the landscape. Tree inventories and urban tree canopy analyses comprise an assortment of tools, technologies, and procedures that help us understand the structure and function of our urban tree canopy. The data and information gleaned from urban tree canopy assessments enable resource professionals and policymakers to make informed decisions about ordinances, housing diversity strategy, and budgeting, future tree planting programs and reduce urban heat islands.

A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs.

A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada

Native grassland in North America is considered one of the most imperiled and altered ecosystems. Unfortunately, an assessment of how much native grassland remains in North America is difficult because all nation-wide landcover mapping products do not reliably distinguish native grasslands from grasslands that have been deliberately planted with tame grasses and forbs (i.e., tame grasslands). We established a 218.5 km2 study area in southwestern Saskatchewan, Canada to evaluate the use of high-resolution Light Detection and Ranging (LiDAR) for classification of native (i.e., fields with native-dominant species mixes or fields that were formerly native species dominated but have been invaded by exotic species) and tame grasslands (i.e., grasslands dominated by exotic grass and forb species), and compared these classifications to the best-available landcover mapping product that is currently available for this area. We used the presence of tractor furrows, identified from the LiDAR digital terrain hillshade product, in tame-dominated grasslands to distinguish them from native-dominated grasslands that had an absence of tractor furrows. The LiDAR method achieved substantially better classification success (Cohen's Kappa = 0.57) at distinguishing native-dominated (N = 82) from tame-dominated grasslands (N = 45), than the currently available landcover product (Cohen's Kappa = 0.13) over our 218.5 km2 study area. Misclassification by LiDAR of fields that had been planted with tame grasses and forbs, but were starting to be re-established by native plants appeared to be one weakness of the method in the study area. Our research highlights a novel and time-efficient method for classifying LiDAR imagery using easily available image analysis features in ArcGIS.

Integrating LiDAR and high-resolution imagery for object-based mapping of forest habitats in a heterogeneous temperate forest landscape

ABSTRACTLand-cover mapping (LCM) at a fine scale would be useful for forest management across heterogeneous natural landscapes. However, the heterogeneity of land covers at such scales results in complex spectral and textural properties that hinder the applicability of LCM. Besides, the method suffers from, e.g. inconsistent representation of different land-cover types, lack of sufficient and balanced training samples, and instability of classifiers trained by a high number of predictor variables. Even well-known object-based classification approaches are challenged with an objective evaluation of segmentation outputs. Here we classified partially ambiguous land-cover types across heterogeneous forest landscapes in the Bavarian Forest National Park (Germany) by combining metrics from airborne light detection and ranging (LiDAR) and colour infrared (CIR) imagery data and a random forest classifier implemented in an object-based paradigm. We evaluated the segmentation results by creating a global quality score based on inter- and intra-measurements of variance and the number of segments. Selected segmentation outputs were combined with balanced training samples to run the classification algorithm based on representative blocks within the national park. The entire processing chain was implemented in an open-source domain. The final segmentation consisted of LiDAR-based height, image-based Normalized Difference Vegetation Index (NDVI) and red band, with 20 cluster seeds and a minimum segment size of 40 pixels. In the classification, the most important variables included the height of the top layer, NDVI, Enhanced Vegetation Index (EVI) and Green–Red Vegetation Index (GRVI). The average values of 500 random forest runs indicated an overall accuracy of 86.6% and an estimated Cohen’s kappa coefficient of 85.2%, with different probabilities of correct classification for land-cover classes. Mature deciduous, standing deadwood, fallen deadwood, meadow, and bare soil classes were classified most accurately, whereas classification of young coniferous, intermediate-age coniferous, mature coniferous, young deciduous, and intermediate-age deciduous were associated with the highest uncertainties. Our methodology is sufficiently robust to be applied to other similarly structured sites across temperate forested landscapes. The versatility of the method is partially guaranteed by the proposed segmentation quality score, which satisfactorily corrects under- and over-segmentation.

Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multiscale Local Geometric Descriptors and Visual Analytics

Topographic Light Detection and Ranging (LiDAR) captures geometric information of the topography of a geographical region, often using airborne platforms. The research and practice of analysis of point clouds acquired using LiDAR is more recent in comparison to that of LiDAR imagery. Point clouds are unstructured datasets, where its geometric or structural classification labels the constituent points as belonging to line-, surface-, or point-type features. We focus on line-type features in the LiDAR point clouds of urban residential areas, which enables extraction of building outlines. We use a multiscale local geometric descriptor (LGD), computed using tensor voting and gradient energy tensor to enhance specific line-type features, e.g., gable roofs. Given that LGDs are positive-semidefinite second-order tensors, we propose a tensor-based data analytic workflow for extraction of boundaries in building roofs using the LGD. We use the tensor representation of the LGD to extract “tensorlines,” which are then postprocessed for extracting feature lines of the building roofs. Our proposed workflow provides the flexibility to the human-in-the-loop for exploration of point clouds for roof boundary tracing for selected buildings. We demonstrate the workflow for a two-plane gable roof.

Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile Mapping Platform

Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.

The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.

Using LiDAR surveys to document floods: A case study of the 2008 Iowa flood

Can we use Light Detection and Ranging (LiDAR), an emergent remote sensing technology with wide applications, to document floods with high accuracy? To explore the feasibility of this application, we propose a method to extract distributed inundation depths from a LiDAR survey conducted during flooding. This method consists of three steps: (1) collecting LiDAR data during flooding; (2) classifying the LiDAR observational points as flooded water surface points and non-flooded points, and generating a floodwater surface elevation model; and (3) subtracting the bare earth Digital Terrain Model (DTM) from the flood surface elevation model to obtain a flood depth map. We applied this method to the 2008 Iowa flood in the United States and evaluated the results using the high-water mark measurements, flood extent extracted from SPOT (Small Programmable Object Technology) imagery, and the near-simultaneously acquired aerial photography. The root mean squared error of the LiDAR-derived floodwater surface profile to high-water marks was 30cm, the consistency between the two flooded areas derived from LiDAR and SPOT imagery was 72% (81% if suspicious isolated ponds in the SPOT-derived extent were removed), and LiDAR-derived flood extent had a horizontal resolution of ∼3m. This work demonstrates that LiDAR technology has the potential to provide calibration and validation reference data with appreciable accuracy for improved flood inundation modeling.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

ABSTRACTForests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45 m.

Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery

Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As the population along coasts rises there is growing concern about water quality and increased sedimentation rates. Therefore, these smaller tidal creek watersheds are at risk to pollution, decreased environmental health, and deterioration of protective salt marshes. The purpose of this study was to test methods for high spatial resolution mapping of benthic (submerged) and emergent habitats as well as the derivation of bathymetry using DigitalGlobe’s WorldView-2 imagery. An intensive field effort was conducted to test and assess several image processing techniques. Results concluded that: (1) supervised habitat classification produced the highest map accuracy (95%); (2) sand, water, scrub/shrub, and docks/rubble were mapped the most accurately at greater than 95%; (3) saltmarsh habitats (high and low density cordgrass, Spartina alterniflora, and black needlerush, Juncus roemerianus), mud, and oyster beds were between 80 and 85% accurate; (4) pan-sharpening and atmospheric correction did not improve map accuracy; (5) LiDAR (light detection and ranging) data increased habitat map accuracy; and (6) WorldView-2 imagery was capable of deriving water depth and these data increased the map accuracy of benthic habitats. The project produced habitat maps for benthic and emergent species at high spatial resolution (4 m2) which will be useful for studying the dynamic processes in this tidal environment. The data and methods developed here could be used by state and local government planning agencies to assess potential long-term changes and develop appropriate management strategies.