Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

Derwin, Jill M.; Thomas, Valerie A.; Wynne, Randolph H.; Coulston, John W.; Liknes, Greg C.; Bender, S.; Blinn, Christine E.; Brooks, Evan B.; Ruefenacht, Bonnie; Benton, Robert; Finco, Mark; Megown, Kevin

doi:10.1016/j.jag.2019.101985

Cited by 20 publications

(15 citation statements)

References 33 publications

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“…Even so, contaminated pixels were still present in the images. To eliminate the effects of the remaining contaminated pixels (Zhu and Woodcock, 2014), we used only 11 images that have over 70% valid pixels (Derwin et al, 2020). The ultra-blue band that is useful for coastal and aerosol studies (Acharya and Yang, 2015) was eliminated, with reference to other canopy cover studies (Korhonen et al, 2017;Derwin et al, 2020).…”

Section: Landsat Time Seriesmentioning

confidence: 99%

“…Measurement of TCC using wall-to-wall airborne lidar is comparable to that using field data (Korhonen et al, 2011) but can provide a large size of samples and enables random samples to be used in inaccessible terrain (Adhikari et al, 2016). However, sensor-based TCC modelling relies heavily on the quality of satellite images that require valid observations in a successive time series (Baccini et al, 2008;Margono et al, 2012), which makes cloud-free satellite images greatly preferred in existing TCC modelling studies (Yang et al, 2012;Karlson et al, 2015;Derwin et al, 2020). The abandonment of incomplete images, in turn, shrinks the amount of data available for TCC modelling in tropical regions, where continuous cloudy weather occurs during rainy seasons (Anderson et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to SCs and STMs, Fourier (harmonic) analysis has long been used to assess vegetation dynamics and classify basic vegetation formations (Moody and Johnson, 2001;Wang et al, 2019), and delivers good performance in characterizing land surface phenology (Moody and Johnson, 2001). Harmonic regression (HR) coefficients were found to be better predictors for TCC modelling than median composites in Derwin et al (2020) and Wilson et al (2018). These two studies, however, did not examine the sensitivity of HR coefficients concerning gaps, as the former used almost cloud-free Landsat images, and the latter pointed out that HR associated with TCC modelling provides a means for overcoming gaps in Landsat time series but lacked a sensitive test for the gaps.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Tang

Adhikari

Pellikka

et al. 2022

Front. Remote Sens.

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Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, preprocessing procedure, but its necessity and effects in numerous Landsat applications, such as tree canopy cover (TCC) modelling, are rarely examined. We address this barrier by providing a quantitative comparison of TCC modelling using predictor variables derived from Landsat time series that included gap-filling versus those that did not include gap-filling and evaluating the effects that gap-filling has on modelling TCC. With 1-year Landsat time series from a tropical region located in Taita Hills, Kenya, and a reference TCC map in 0–100 scales derived from airborne laser scanning data, we designed comparable random forest modelling experiments to address the following questions: 1) Does gap-filling improve TCC modelling based on time series predictor variables including the seasonal composites (SC), spectral-temporal metrics (STMs), and harmonic regression (HR) coefficients? 2) What is the difference in TCC modelling between using gap-filled pixels and using valid (actual or cloud-free) pixels? Two gap-filling methods, one temporal-based method (Steffen spline interpolation) and one hybrid method (MOPSTM) have been examined. We show that gap-filled predictors derived from the Landsat time series delivered better performance on average than non-gap-filled predictors with the average of median RMSE values for Steffen-filled and MOPSTM-filled SC’s being 17.09 and 16.57 respectively, while for non-gap-filled predictors, it was 17.21. MOPSTM-filled SC is 3.7% better than non-gap-filled SC on RMSE, and Steffen-filled SC is 0.7% better than non-gap-filled SC on RMSE. The positive effects of gap-filling may be reduced when there are sufficient high-quality valid observations to generate a seasonal composite. The single-date experiment suggests that gap-filled data (e.g. RMSE of 16.99, 17.71, 16.24, and 17.85 with 100% gap-filled pixels as training and test datasets for four seasons) may deliver no worse performance than valid data (e.g. RMSE of 15.46, 17.07, 16.31, and 18.14 with 100% valid pixels as training and test datasets for four seasons). Thus, we conclude that gap-filling has a positive effect on the accuracy of TCC modelling, which justifies its inclusion in image preprocessing workflows.

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Section: Landsat Time Seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Tang

Adhikari

Pellikka

et al. 2022

Front. Remote Sens.

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“…This model was used to assess variable importance. The metric used to assess variable importance was mean decrease in accuracy, a common metric used in Random Forest studies [65,66].…”

Section: Random Forest Model Variable Importancementioning

confidence: 99%

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

et al. 2021

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Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

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“…Brooks et al [62] found that LTS-derived maps performed well as the source of strata for post-stratified estimation of FIA attributes. Derwin et al [166] used harmonic regression coefficients from LTS in a canopy cover estimation approach, and found that they performed better than other LTS approaches when compared to FIA data. Research into creative uses of LTS for improving FIA business processes is in its early stages.…”

Section: Use Of Lts-derived Covariates For Mapping Of Fia Attributesmentioning

confidence: 99%

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Lister

Andersen

Frescino

et al. 2020

Forests

Self Cite

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Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

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Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

Cited by 20 publications

References 33 publications

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

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