A Comparison of Standard Modeling Techniques Using Digital Aerial Imagery with National Elevation Datasets and Airborne LiDAR to Predict Size and Density Forest Metrics in the Sapphire Mountains MT, USA

Ahl, R. S.; Hogland, John; Brown, S.

doi:10.3390/ijgi8010024

Cited by 4 publications

(5 citation statements)

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“…We were able to significantly improve estimates of the amount, location and condition of longleaf pine and other forest ecosystems across the Fort Stewart SGA using Landsat 8 imagery and FIA field data. Similar to other studies that relate field data with remotely sensed information to estimate aspects of the forested condition, BAH tended to be more strongly correlated with spectral metrics than TPH [12,[41][42][43]. Additionally, our study also supports the idea that multi-temporal imagery and finer resolution imagery such as NAIP provides additional information over using only single season imagery when predicting forest characteristics [41,44].…”

Section: Discussionsupporting

confidence: 87%

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

et al. 2019

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This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

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Section: Discussionsupporting

confidence: 87%

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

et al. 2019

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“…In this study, we showed that lidar-derived canopy metrics produced better models of forest structure when compared with Sentinel-2-only models, though this is not always that case, as [9] demonstrated. Additionally, when we combined imagery and lidar variables in our forest metric models, we created marginally better estimates for two out of our three forest metrics.…”

Section: Discussionmentioning

confidence: 57%

“…However, van Ewijk et al, 2011 used field data in combination with lidar data to estimate QMD, and not just to train their model [45]. Ahl et al, 2019 used random forest (RF) to model basal area and QMD from lidar, with RMSEs of 9.0 and 11.7, respectively [9]. These studies had higher lidar point densities and covered smaller study areas with a smaller range of forest types than our study, yet our best model results for BAH and QMD had similar or better r 2 and RMSE values (adj.…”

Section: Discussionmentioning

confidence: 99%

“…When combined with field plots, lidar data can be used to estimate forest stand density, basal area, vegetation biomass [4][5][6][7], and understory canopy structure [8], which gives lidar data a distinct advantage over passive spectral sensors that cannot observe structure under dense-canopied forests. Some recent studies have shown that fine-resolution remotely sensed imagery can be as, or more, accurate than lidar at estimating some common forest structure metrics [6,9]. However, in structurally diverse forests in the Southeastern U.S., lidar's ability to observe multiple forest canopy layers has the potential to significantly improve estimation of forest metrics.…”

Section: Introductionmentioning

confidence: 99%

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Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

et al. 2021

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Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes.

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“…Single tree detection based on remote sensing images is a crucial technology for establishing a single tree database and monitoring single tree plantation resources, which is of great significance to urban landscape planning and ecological environment monitoring (Congalton et al, 2014;Faridatul and Wu, 2018;Ahl et al, 2019). Single tree detection is a cross-research field of computer vision, measurement, single tree management, and remote sensing (Kupidura et al, 2019;Zhang et al, 2020;Belcore et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery

Zheng

2021

Front. Environ. Sci.

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Using high-resolution remote sensing images to automatically identify individual trees is of great significance to forestry ecological environment monitoring. Urban plantation has realistic demands for single tree management such as catkin pollution, maintenance of famous trees, landscape construction, and park management. At present, there are problems of missed detection and error detection in dense plantations and complex background plantations. This paper proposes a single tree detection method based on single shot multibox detector (SSD). Optimal SSD is obtained by adjusting feature layers, optimizing the aspect ratio of a preset box, reducing parameters and so on. The optimal SSD is applied to single tree detection and location in campuses, orchards, and economic plantations. The average accuracy based on SSD is 96.0, 92.9, and 97.6% in campus green trees, lychee plantations, and palm plantations, respectively. It is 11.3 and 37.5% higher than the latest template matching method and chan-vese (CV) model method, and is 43.1 and 54.2% higher than the traditional watershed method and local maximum method. Experimental results show that SSD has a strong potential and application advantage. This research has reference significance for the application of an object detection framework based on deep learning in agriculture and forestry.

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A Comparison of Standard Modeling Techniques Using Digital Aerial Imagery with National Elevation Datasets and Airborne LiDAR to Predict Size and Density Forest Metrics in the Sapphire Mountains MT, USA

Cited by 4 publications

References 14 publications

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

Single Shot MultiBox Detector for Urban Plantation Single Tree Detection and Location With High-Resolution Remote Sensing Imagery

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