Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data

Humagain, Kamal; Portillo-Quintero, Carlos; Cox, Robert D.; Cain, James W.

doi:10.1016/j.apgeog.2018.07.024

Cited by 12 publications

(7 citation statements)

References 71 publications

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“…However, detection and identification of objects in high and very high resolution images, approaches that are only based on the pixel values tend to be less accurate, because most reflectance and radiation objects are less sensitive to the dimensions of the object. Some studies, proven that the object-based approach may provide better accuracy [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

Apriyanto

Jaya

Puspaningsih

2019

IJEECS

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In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e<sup>0.032C</sup> with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e<sup>16.48TNDVI</sup> with a mean deviation of 5.37%.

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Section: Introductionmentioning

confidence: 99%

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

Apriyanto

Jaya

Puspaningsih

2019

IJEECS

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“…Canopy closure represents the extent to which tree crowns in forests cover the ground, indicating the density and vertical distribution of vegetation cover. Humagain et al (2018) demonstrated that variations in canopy closure affect how vegetation indices are calculated from remote-sensing images, especially in areas with a complex forest structure and high AGB. Increased canopy closure indicates denser vegetation cover, with tree crowns obstructing each other.…”

Section: Auxiliary Factors That Influence Accurate Agb Estimationmentioning

confidence: 99%

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

Su,

Mura,

Zheng

et al. 2024

JGR Biogeosciences

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Accurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

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“…The repetitive coverage of satellite remote sensing with various spatial and temporal resolutions offers a timely and cost-effective approach for estimating FCD at regional scale based on remotely sensed data, ground-measured sample data 1 , 22 , 23 . The commonly used optical image data include Landsat TM (Thematic Mapper), 24 , 25 ETM+(Enhanced Thematic Mapper Plus), 26 OLI (Operational Land Imager), 27 SPOT imagery, 28 Sentinel-2 imagery, 20 hyperspectral data, 29 among others 30 – 32 Light detection and ranging (LiDAR) data is also commonly used in mapping FCD 4 , 33 …”

Section: Introductionmentioning

confidence: 99%

Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

Tian

Dong

Xing

et al. 2023

J. Appl. Rem. Sens.

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.Forest canopy density (FCD) is an essential factor in forest resources investigation and plays a vital role in forest resources management. To replace the traditional time-consuming and labor-intensive survey methods, it is of great importance to study how to select a suitable model at specific temporal and spatial scales for rapid and accurate estimation of FCD in large areas. To reduce workload and errors, we present an approach combing Landsat 5 TM data and biophysical analysis algorithms without considering thermal index (TI) information to model FCD in a temperate forest in Northern China. The scaled vegetation index (SVD) and scaled shadow index were used as variables for modeling and mapping FCD. The dimidiate pixel model based on the normalized difference vegetation index was also used to estimate FCD. We compared the forest management inventory data of 2012 with the FCD results from the biophysical analysis model and the dimidiate pixel model. The results showed that the biophysical analysis model (prediction precision P = 93.62 % , coefficient of determination R2 = 0.886, root mean square error RMSE = 0.057) has higher accuracy than the dimidiate pixel model (P = 89.92 % , R2 = 0.725, RMSE = 0.078) in estimating FCD. This study shows that the biophysical analysis model without TI information has better performance than the dimidiate pixel model, and can be used for monitoring large scale FCD.

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Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data

Cited by 12 publications

References 71 publications

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

Examining the object-based and pixel-based image analyses for developing stand volume estimator model

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density

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