Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

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Estimating Terrain Slope from ICESat-2 Data in Forest Environments

Remote Sensing ◽

10.3390/rs12203300 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3300

Author(s):

Xiaoxiao Zhu ◽

Sheng Nie ◽

Cheng Wang ◽

Xiaohuan Xi ◽

Dong Li ◽

...

Keyword(s):

Surface Topography ◽

Ground Surface ◽

The United States ◽

Airborne Lidar ◽

Estimation Methods ◽

Estimation Accuracy ◽

Biophysical Parameters ◽

Waveform Data ◽

Terrain Slope ◽

Slope Estimation

The global digital elevation measurement (DEM) products such as SRTM DEM and GDEM have been widely used for terrain slope retrieval in forests. However, the slope estimation accuracy is generally limited due to the DEMs’ low vertical accuracy over complex forest environments. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission shows excellent potential for slope estimation because of the high elevation accuracy and unique design of beam pairs. This study aimed to explore the possibility of ICESat-2 data for terrain slope retrieval in the United States forests. First, raw ICESat-2 data were processed to obtain accurate ground surfaces. Second, two different methods based on beam pairs were proposed to derive terrain slopes from the ground surfaces. Third, the estimated slopes were validated by airborne LiDAR-derived slopes and compared with SRTM-derived slopes and GDEM-derived slopes. Finally, we further explored the influence of surface topography and ground elevation error on slope estimation from ICESat-2 data. The results show that the ground surface can be accurately extracted from all scenarios of ICESat-2 data, even weak beams in the daytime, which provides the basis for terrain slope retrieval from ICESat-2 beam pairs. The estimated slope has a strong correlation with airborne LiDAR-derived slopes regardless of slope estimation methods, which demonstrates that the ICESat-2 data are appropriate for terrain slope estimation in complex forest environments. Compared with the method based on along- and across-track analysis (method 1), the method based on plane fitting of beam pairs (method 2) has a high estimation accuracy of terrain slopes, which indicates that method 2 is more suitable for slope estimation because it takes full advantage of more ground surface information. Additionally, the results also indicate that ICESat-2 performs much better than SRTM DEMs and GDEMs in estimating terrain slopes. Both ground elevation error and surface topography have a significant impact on terrain slope retrieval from ICESat-2 data, and ground surface extraction should be improved to ensure the accuracy of terrain slope retrieval over extremely complex environments. This study demonstrates for the first time that ICESat-2 has a strong capability in terrain slope retrieval. Additionally, this paper also provides effective solutions to accurately estimate terrain slopes from ICESat-2 data. The ICESat-2 slopes have many potential applications, including the generation of global slope products, the improvement of terrain slopes derived from the existing global DEM products, and the correction of vegetation biophysical parameters retrieved from space-borne LiDAR waveform data.

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An Improved Surface Slope Estimation Model Using Space-Borne Laser Altimetric Waveform Data Over the Antarctic Ice Sheet

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3124224 ◽

2021 ◽

pp. 1-1

Author(s):

Huan Xie ◽

Hong Tang ◽

Yanmin Jin ◽

Binbin Li ◽

Zhijie Zhang ◽

...

Keyword(s):

Ice Sheet ◽

Surface Slope ◽

Estimation Model ◽

Antarctic Ice Sheet ◽

Waveform Data ◽

Slope Estimation ◽

The Antarctic

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Effect of Field Sample Size on Large-Scale Subtropic Forest Inventory Attribute Estimation Based on Airborne Laser Scanner Data

10.20944/preprints202106.0530.v1 ◽

2021 ◽

Author(s):

Yu Zhu ◽

Zhou Xiang Bei ◽

Lin Xin ◽

Chen Zhong Chao ◽

Zhou Mei ◽

...

Keyword(s):

Sample Size ◽

Large Scale ◽

Forest Type ◽

Airborne Lidar ◽

Estimation Accuracy ◽

Estimation Model ◽

Technical Application ◽

Eucalyptus Forest ◽

Forest Attributes ◽

Estimation Models

Exploring the effect of the sample size on the estimation accuracy of airborne LiDAR forest attributes in a large-scale area can help in optimizing the technical application scheme of operational ALS-based large-scale forest stand inventories. In our study, sample datasets composed of different sample plots were constructed by repeated sampling from 1003 sample plots in a subtropical study area covering 2376 × 103 km2. Sixteen multiplicative power models were built in each forest type consisting of four forest attributes. Through these models, the variations of standard deviation (SD) and coefficient of variation (CV) of R2 and rRMSE of forest attribute estimation models for different quantity levels of sample plots were also analyzed. The results showed that, first, when the sample size increased from 30 to the top limit, the SD of the forest attributes and LiDAR variables showed a decreasing trend. Second, as the sample size increased, the rRMSE of the 16 forest attribute estimation models gradually decreased, while the R2 gradually increased. Third, when the sample size was small, both the SD of R2 and rRMSE of the models were large, and the SD of R2 and rRMSE gradually decreased as the sample size increased. In 50 models conducted for each attribute at the same sample size, for the mean standard deviations of forest attributes, the ten best performing models were lower than those of the total 50 models, and the worst ten models were the opposite. When the sample size increased, the accuracy of each forest attribute estimation model for each forest type gradually improved. The variation of forest attributes and the LiDAR variable of the construction model are critical factors that affect the model’s accuracy. To efficiently apply airborne LiDAR in order to survey large-scale subtropical forest resources, the sample size of the Chinese fir forest, pine forest, eucalyptus forest, and broad-leaved forest should be 110, 80, 85, and 70, respectively.

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Analytical Model for High–Level Area Estimation of FPGA Design

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/ijertcs.2016070103 ◽

2016 ◽

Vol 7 (2) ◽

pp. 35-44

Author(s):

Rachna Singh ◽

Arvind Rajawat

Keyword(s):

Early Stage ◽

Estimation Error ◽

Research Work ◽

Performance Estimation ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Fpga Design ◽

Estimation Model ◽

Area Estimation ◽

High Level

FPGAs have been used as a target platform because they have increasingly interesting in system design and due to the rapid technological progress ever larger devices are commercially affordable. These trends make FPGAs an alternative in application areas where extensive data processing plays an important role. Consequently, the desire emerges for early performance estimation in order to quantify the FPGA approach. A mathematical model has been presented that estimates the maximum number of LUTs consumed by the hardware synthesized for different FPGAs using LLVM.. The motivation behind this research work is to design an area modeling approach for FPGA based implementation at an early stage of design. The equation based area estimation model permits immediate and accurate estimation of resources. Two important criteria used to judge the quality of the results were estimation accuracy and runtime. Experimental results show that estimation error is in the range of 1.33% to 7.26% for Spartan 3E, 1.6% to 5.63% for Virtex-2pro and 2.3% to 6.02% for Virtex-5.

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Development of a Practical Method to Estimate the Eco-Level of Driver Performance

Journal of Advanced Transportation ◽

10.1155/2020/8151720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jianbin Zheng ◽

Yiping Wu

Keyword(s):

Fuel Consumption ◽

Fuel Efficiency ◽

Back Propagation ◽

Practical Method ◽

Estimation Accuracy ◽

Road Transportation ◽

Estimation Model ◽

Driver Performance ◽

Study Results ◽

Propagation Network

Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.

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Modeling Wildland Firefighter Travel Rates by Terrain Slope: Results from GPS-Tracking of Type 1 Crew Movement

Fire ◽

10.3390/fire3030052 ◽

2020 ◽

Vol 3 (3) ◽

pp. 52

Author(s):

Patrick R. Sullivan ◽

Michael J. Campbell ◽

Philip E. Dennison ◽

Simon C. Brewer ◽

Bret W. Butler

Keyword(s):

Airborne Lidar ◽

Gps Tracking ◽

Escape Route ◽

Terrain Slope ◽

Precise Understanding ◽

Rate Functions ◽

Wildland Firefighting ◽

Upper Third ◽

Travel Rate

Escape routes keep firefighters safe by providing efficient evacuation pathways from the fire line to safety zones. Effectively utilizing escape routes requires a precise understanding of how much time it will take firefighters to traverse them. To improve this understanding, we collected GPS-tracked travel rate data from US Interagency Hotshot “Type 1” Crews during training in 2019. Firefighters were tracked while hiking, carrying standard loads (e.g., packs, tools, etc.) along trails with a precisely-measured terrain slope derived from airborne lidar. The effects of the slope on the instantaneous travel rate were assessed by three models generated using non-linear quantile regression, representing low (bottom third), moderate (middle third), and high (upper third) rates of travel, which were validated using k-fold cross-validation. The models peak at about a −3° (downhill) slope, similar to previous slope-dependent travel rate functions. The moderate firefighter travel rate model mostly predicts faster movement than previous slope-dependent travel rate functions, suggesting that firefighters generally move faster than non-firefighting personnel while hiking. Steepness was also found to have a smaller effect on firefighter travel rates than previously predicted. The travel rate functions produced by this study provide guidelines for firefighter escape route travel rates and allow for more accurate and flexible wildland firefighting safety planning.

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SAR Observation Error Estimation Based on Maximum Relative Projection Matching

International Journal of Antennas and Propagation ◽

10.1155/2020/3517834 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Y. Zhang ◽

B. P. Wang ◽

Y. Fang ◽

Z. X. Song

Keyword(s):

Error Estimation ◽

Estimation Method ◽

Estimation Methods ◽

Estimation Accuracy ◽

Reconstruction Method ◽

Observation Error ◽

Estimation Model ◽

Reconstruction Process ◽

Single Observation ◽

Imaging Results

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.

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Monitoring tropical forest carbon stocks and emissions using Planet satellite data

Scientific Reports ◽

10.1038/s41598-019-54386-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 18

Author(s):

Ovidiu Csillik ◽

Pramukta Kumar ◽

Joseph Mascaro ◽

Tara O’Shea ◽

Gregory P. Asner

Keyword(s):

Climate Change ◽

Large Scale ◽

Cost Effective ◽

Carbon Stocks ◽

Airborne Lidar ◽

Aboveground Carbon ◽

Lidar Measurements ◽

High Resolution Map ◽

Aboveground Carbon Density ◽

Planet Satellite

AbstractTropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, we need to measure and monitor carbon stocks and emissions at high spatial and temporal resolution. We developed the first large-scale very high-resolution map of aboveground carbon stocks and emissions for the country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into a random forest machine learning regression workflow, obtaining an R2 of 0.70 and RMSE of 25.38 Mg C ha−1 for the nationwide estimation of aboveground carbon density (ACD). The diverse ecosystems of Peru harbor 6.928 Pg C, of which only 2.9 Pg C are found in protected areas or their buffers. We found significant carbon emissions between 2012 and 2017 in areas aggressively affected by oil palm and cacao plantations, agricultural and urban expansions or illegal gold mining. Creating such a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries will serve as a transformative tool to quantify the climate change mitigation services that forests provide.

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