Historical forest biomass dynamics modelled with Landsat spectral trajectories

Gómez, Cristina; White, Joanne C.; Wulder, Michael A.; Alejandro, Pablo

doi:10.1016/j.isprsjprs.2014.03.008

Cited by 59 publications

(27 citation statements)

References 107 publications

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“…The optical imagery-based technologies are commonly used for biomass estimation due to high correlations between spectral bands and biomass [4,6,[17][18][19][20][21][22][23][24][25][26][27][28]. In particular, Landsat images have been the most widely used for forest aboveground biomass (AGB) estimation in the past three decades [5,6,20,24,26,[28][29][30][31][32][33][34][35][36], mainly because they are freely downloadable, have a long history, and have medium spatial resolution. The studies deal with different climate zones and forest ecosystems, from tropical to subtropical, temperate, and boreal forests [4][5][6][7][12][13][14][15]20,28,32,[37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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Abstract:The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg/ha. Relatively low AGB (e.g., less than 40 Mg/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80-120 Mg/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.

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Section: Introductionmentioning

confidence: 99%

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

et al. 2016

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“…In recent years, remotely sensed data has provided spatially complete prediction information regarding forest cover and change across large areas [11][12][13][14][15]. Various estimation approaches could be used to derive AGB based on field plots' observations, including empirical models ranging from simple linear regression to machine-based modeling [5,14,16].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Shen

Huang

et al. 2016

Remote Sensing

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Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007)(2008)(2009)(2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R 2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones.

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“…Still, the difference between a gradual forest stress and stability is likely different for different forest compositions and environments, and single thresholds are not likely to be adequate for large mapping projects. Different vegetation indices relate to different ecologically relevant information, such as above ground biomass, forest density [52], and canopy chlorophyll content [53], and account for particular site-specific conditions [54]. In our comparison of VeRDET's performance across different vegetation indices, the high performance of NDMI may be explained in part by its sensitivity to biomass [45], which may enable it to detect actual disturbances and to track regenerating biomass instead of just initial, rapid regreening from forbs.…”

Section: Discussionmentioning

confidence: 99%

Patch-Based Forest Change Detection from Landsat Time Series

Hughes

Kaylor

Hayes

2017

Forests

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Abstract:In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetation Regeneration and Disturbance Estimates through Time (VeRDET), which employ a novel patch-based approach to detect periods of vegetation disturbance, stability, and growth from the historical Landsat image records. VeRDET generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization, an algorithm originally designed for signal denoising. This study explores VeRDET's effectiveness in detecting forest change using four vegetation indices and two parameters controlling the spatial and temporal scales of segmentation within a calibration region. We then evaluate algorithm effectiveness within a 386,000 km 2 area in the Eastern United States where VeRDET has overall error of 23% and omission error across disturbances ranging from 22% to 78% depending on agent.

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Historical forest biomass dynamics modelled with Landsat spectral trajectories

Cited by 59 publications

References 107 publications

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data

Patch-Based Forest Change Detection from Landsat Time Series

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