Herbaceous Vegetation Height Map on Riverdike Derived from UAV LiDAR Data

2018 ◽  
Author(s):  
Naoko Miura ◽  
Shigehiro Yokota ◽  
Tamayo F. Koyanagi ◽  
Susumu Yamada
Keyword(s):  
Lidar Data ◽  
Herbaceous Vegetation ◽  
Vegetation Height

Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging

2007 ◽  
Vol 16 (3) ◽  
pp. 341 ◽  
Author(s):  
David Riaño ◽  
Emilio Chuvieco ◽  
Susan L. Ustin ◽  
Javier Salas ◽  
José R. Rodríguez-Pérez ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Improvement ◽  
Accurate Estimate ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Fuel Type ◽  
Vegetation Height ◽  
Shrub Canopy ◽  
Central Portugal

A fuel-type map of a predominantly shrub-land area in central Portugal was generated for a fire research experimental site, by combining airborne light detection and ranging (LiDAR), and simultaneous color infrared ortho imaging. Since the vegetation canopy and the ground are too close together to be easily discerned by LiDAR pulses, standard methods of processing LiDAR data did not provide an accurate estimate of shrub height. It was demonstrated that the standard process to generate the digital ground model (DGM) sometimes contained height values for the top of the shrub canopy rather than from the ground. Improvement of the DGM was based on separating canopy from ground hits using color infrared ortho imaging to detect shrub cover, which was measured simultaneously with the LiDAR data. Potentially erroneous data in the DGM was identified using two criteria: low vegetation height and high Normalized Difference Vegetation Index (NDVI), a commonly used spectral index to identify vegetated areas. Based on the height of surrounding pixels, a second interpolation of the DGM was performed to extract those erroneously identified as ground in the standard method. The estimation of the shrub height improved significantly after this correction, and increased determination coefficients from R2 = 0.48 to 0.65. However, the estimated shrub heights were still less than those observed in the field.

scholarly journals Assessing Potential Safety Zone Suitability Using a New Online Mapping Tool

Fire ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Michael J. Campbell ◽  
Philip E. Dennison ◽  
Matthew P. Thompson ◽  
Bret W. Butler
Keyword(s):  
Burn Injury ◽  
Wildland Fire ◽  
Satellite Image ◽  
Safety Information ◽  
Separation Distance ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Lidar Data ◽  
Landsat 8 ◽  
Vegetation Height

Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation of SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use.

Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands

2009 ◽  
Vol 18 (7) ◽  
pp. 848 ◽  
Author(s):  
Cheng Wang ◽  
Nancy F. Glenn
Keyword(s):  
Fire Severity ◽  
Remote Sensing Data ◽  
Biomass Combustion ◽  
Sagebrush Steppe ◽  
Lidar Data ◽  
Vegetation Height ◽  
Severity Estimation ◽  
Before And After ◽  
Forested Areas ◽  
The Difference

Reflectance-based indices derived from remote-sensing data have been widely used for detecting fire severity in forested areas. Rangeland ecosystems, such as sparsely vegetated shrub-steppe, have unique spectral reflectance differences before and after fire events that may not make reflectance-based indices appropriate for fire severity estimation. As an alternative, average vegetation height change ( dh ) derived from pre- and post-fire Light Detection and Ranging (LiDAR) data were used in this study for fire severity estimation. Theoretical deductions were conducted to demonstrate that LiDAR-derived dh is related to biomass combustion and thus can be used for fire severity estimation in rangeland areas. The Jeffreys–Matusita (JM) distance was calculated to evaluate the separability for each pair of fire severity classes, with an average JM distance of 1.14. Thresholds for classifying the level of fire severity were determined according to the mean and standard deviation of each class. A fire-severity classification map with 84% overall accuracy was obtained from the LiDAR dh method. Importantly, this method was sensitive to the difference between the moderate and high fire-severity classes.

Using lidar to assess impacts of forest harvest landings on vegetation height by harvest season and the potential for recovery over time

2016 ◽  
Vol 46 (6) ◽  
pp. 869-875 ◽  
Author(s):  
Robert A. Slesak ◽  
Tyler Kaebisch
Keyword(s):  
Soil Compaction ◽  
Positive Relationship ◽  
Tree Regeneration ◽  
Lidar Data ◽  
Forest Harvest ◽  
Vegetation Height ◽  
Time Period ◽  
Wide Range ◽  
The Difference ◽  
Over Time

Tree regeneration and growth is generally reduced at forest harvest landing areas because of significant soil compaction, but it is commonly believed that harvesting in winter can reduce these impacts and that recovery occurs naturally with time. We used lidar data to assess differences in vegetation height between landing and general harvest areas across 79 sites in northern Minnesota, United States, that had been harvested in either summer/fall or winter and between 2 and 175 months since harvest. Vegetation height was significantly lower at landing areas compared with general harvest areas; however, there was no effect of harvest season on the difference (p = 0.50), indicating that impacts occur during all seasons. There was a significant (p < 0.01) positive relationship between the difference in vegetation height and time, regardless the harvest season, providing evidence that recovery occurs across a wide range of conditions within our time period of assessment. Sites with three landings present had the lowest relative landing area and also had the lowest differences in vegetation height between landing and general harvest areas, demonstrating the potential for optimized landing configurations to minimize impacts to growth. Based on our findings, landing areas should be kept as small as reasonably possible during all seasons of harvest, but the need for active reclamation practices is probably not warranted given that recovery occurs within the first few decades after harvest.

scholarly journals MONITORING PHENOLOGY OF FLOODPLAIN GRASSLAND AND HERBACEOUS VEGETATION WITH UAV IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 569-571 ◽  
Author(s):  
W. K. van Iersel ◽  
M. W. Straatsma ◽  
E. A. Addink ◽  
H. Middelkoop
Keyword(s):  
Remote Sensing Data ◽  
Water Levels ◽  
Ecological Value ◽  
Herbaceous Vegetation ◽  
Vegetation Development ◽  
Hydraulic Roughness ◽  
Vegetation Height ◽  
Multi Temporal ◽  
One Year ◽  
Single Epoch

River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE&thinsp;=&thinsp;29-42&thinsp;cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.

scholarly journals Relationship between Vegetation Height and Species richness of birds in some wetlands of Sinnar state, Sudan

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Mohamed Elmekki Ali Elbadawi Hussien
Keyword(s):  
Species Richness ◽  
Dry Season ◽  
Herbaceous Vegetation ◽  
Vegetation Height ◽  
Central Sudan ◽  
Line Transects ◽  
The Relationship

Objectives of this study were to: identify the relationship between vegetation higheit and  birds’ species at various wetlands of Sinnar State; This study was conducted in Sinnar state, central Sudan (Latitudes 05º - 12º to 05º -14º N and longitudes 32.9º - 35.4º E), covering 12 wetlands (mayas) during the wet and the dry season duration 2011 - 2013; the wetlands are Ronga, Allahmaana, Gladeem, Elban, Rahad Kobri 45, Lawni, Kinnaf Tura 5, Rigaba, Shamiya, Wad elggack, Homrani and Sinnar Dam reservoir.The study focused on abundance of herbaceous vegetation, Parmeters of herbaceous vegetation were determined at 50-m intervals along line transects; these parameters were plant counts and vegetation height in a 1-m circular quadrat. Birds were counted twice a day (morning and evening) in all wetlands with the help of telescopes and binoculars, and species utilizing each site of the wetlands are identified. Excell programme was used for data analysis.Vegetation height is negatively correlated with birds’ species richness in wet seasons of 2011 – 2012 and 2012 – 2013, positively correlated in the dry season when herbaceous vegetation is tall, but negatively correlated when it is short. 

scholarly journals Bird-habitat associations in coastal rangelands of southern Brazil

2014 ◽  
Vol 104 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Rafael A. Dias ◽  
Vinicius A. G. Bastazini ◽  
Andros T. Gianuca
Keyword(s):  
Management Practices ◽  
Habitat Degradation ◽  
Bird Community ◽  
Herbaceous Plants ◽  
Woody Vegetation ◽  
Southern Brazil ◽  
Avian Diversity ◽  
Herbaceous Vegetation ◽  
Vegetation Height ◽  
Vegetation Patchiness

Nearly all remnants of temperate grasslands in southeastern South America are used for livestock ranching and are subject to habitat degradation resulting from this activity. Exploring how habitat features affect the composition of grassland avifaunal communities is a first step to understand how current cattle-ranching management practices impact avian diversity. We used canonical ordination to test for relationships between five habitat variables and the composition of the bird community in coastal grasslands in southern Brazil. We sampled pastures with different heights, from overgrazed short-grass to tall herbaceous vegetation. We recorded 1,535 individuals and 27 species of birds. The first ordination axis indicated a strong contribution of mean vegetation height on the composition of the bird community, whereas the second axis revealed the influence of herbaceous vegetation patchiness and woody vegetation cover. Three groups of species were revealed by the ordination: one more diffuse associated with intermediate and tall herbaceous vegetation, another with short grass, and a third with vegetation patchiness and woody vegetation. Species restricted to tall herbaceous vegetation are negatively impacted from habitat degradation resulting from overgrazing and trampling by livestock, and mowing and burning of tall plants. Occurrence of these species in our study area is related with the presence of swales immediately behind the dune system and where remnants of tall vegetation persist. Birds of pastures with ample cover of short herbaceous plants, including one globally threatened species and six other restricted to short-grass habitat, apparently benefit from local livestock management practices. Woody vegetation possibly functions as a keystone structure, enabling the occurrence in grasslands of avian species that rely on shrubby habitat. Although livestock ranching promotes the diversity of habitats by creating distinct patches of vegetation height in grasslands, current management practices directed to the maintenance of short grass pastures may eliminate an entire subset of species, including regionally threatened taxa, and reduce avian diversity. The maintenance of large patches of tall herbaceous plants is needed to ensure the survival of species reliant on this type of grassland structure in our study area.

scholarly journals Poster about mapping the risk of forest wind damage using airborne scanning LiDAR

2020 ◽  
Author(s):  
Ninni Saarinen ◽  
Mikko Vastaranta ◽  
Eija Honkavaara ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
...  
Keyword(s):  
Forest Management ◽  
Regression Model ◽  
Risk Model ◽  
Canopy Height ◽  
Wind Damage ◽  
Forest Damage ◽  
Multivariate Logistic Regression Model ◽  
Lidar Data ◽  
Vegetation Height ◽  
Damage Risk

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts especially when wind damage events have increased in recent years.The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage and to provide information that could be used to support decision making in forest management planning, as well as in other sectors (e.g. electricity companies). To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. LiDAR data can provide wall-to-wall coverage and are best suited for monitoring of the dominant trees. In addition multitemporal LiDAR is highly capable of monitoring abiotic tree or stand level changes. The LiDAR data used are openly accessible for public from NLS and are mainly used for generating digital terrain model (DTM). Potential drivers associated with the probability of wind-induced forest damage were examined using a multivariate logistic regression model which was well suited to the discrete nature of the dependent variable (i.e., damage, no damage) and it has been used widely in the modelling of forest disturbances. Risk model predictors related to topography and vegetation height were extracted from the LiDAR-derived surface models such as DTM and canopy height model (CHM). The strongest predictors in the risk model were mean canopy height and mean elevation. Damaged sample grid cells covered 45,6% of the entire sample and they were mainly dominated by Norway spruce. CHM mean and maximum were higher in damaged sample cells which can be expected to correlate with the result where mean volume was also larger in damaged sample cells than in undamaged. Regression model output was a continuous probability surface whereby the probability for wind damage is interpreted as risk (e.g. areas with high probability of wind damage can be described as high risk areas). With increasing frequency of wind damage events, there is a need to identify areas of high wind damage risk. The selected predictor variables, mean elevation describing local topography and mean canopy height, can provide valuable information on the damage probability (i.e. risk) in a robust way.

scholarly journals A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1962 ◽  
Author(s):  
Xiaoxiao Zhu ◽  
Sheng Nie ◽  
Cheng Wang ◽  
Xiaohuan Xi ◽  
Zhenyue Hu
Keyword(s):  
Spline Interpolation ◽  
Photon Counting ◽  
Terrestrial Ecosystems ◽  
Noise Removal ◽  
Vegetation Coverage ◽  
Retrieval Algorithm ◽  
Lidar Data ◽  
Vegetation Height ◽  
Pulse Photon ◽  
Better Than

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.

scholarly journals MONITORING PHENOLOGY OF FLOODPLAIN GRASSLAND AND HERBACEOUS VEGETATION WITH UAV IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 569-571 ◽  
Author(s):  
W. K. van Iersel ◽  
M. W. Straatsma ◽  
E. A. Addink ◽  
H. Middelkoop
Keyword(s):  
Remote Sensing Data ◽  
Water Levels ◽  
Ecological Value ◽  
Herbaceous Vegetation ◽  
Vegetation Development ◽  
Hydraulic Roughness ◽  
Vegetation Height ◽  
Multi Temporal ◽  
One Year ◽  
Single Epoch

River restoration projects, which aim at improved flood safety and increased ecological value, have resulted in more heterogeneous vegetation. However, they also resulted in increasing hydraulic roughness, which leads to higher flood water levels during peak discharges. Due to allowance of vegetation development and succession, both ecological and hydraulic characteristics of the floodplain change more rapidly over time. Monitoring of floodplain vegetation has become essential to document and evaluate the changing floodplain characteristics and associated functioning. Extraction of characteristics of low vegetation using single-epoch remote sensing data, however, remains challenging. The aim of this study was to (1) evaluate the performance of multi-temporal, high-spatial-resolution UAV imagery for extracting temporal vegetation height profiles of grassland and herbaceous vegetation in floodplains and (2) to assess the relation between height development and NDVI changes. Vegetation height was measured six times during one year in 28 field plots within a single floodplain. UAV true-colour and false-colour imagery of the floodplain were recorded coincidently with each field survey. We found that: (1) the vertical accuracy of UAV normalized digital surface models (nDSMs) is sufficiently high to obtain temporal height profiles of low vegetation over a growing season, (2) vegetation height can be estimated from the time series of nDSMs, with the highest accuracy found for combined imagery from February and November (RMSE&thinsp;=&thinsp;29-42&thinsp;cm), (3) temporal relations between NDVI and observed vegetation height show different hysteresis behaviour for grassland and herbaceous vegetation. These results show the high potential of using UAV imagery for increasing grassland and herbaceous vegetation classification accuracy.

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