Tropical Forest Canopy Structure and Change Assessment Using Landsat, GEDI, and Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

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Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

10.5194/bg-2021-95 ◽

2021 ◽

Author(s):

Trina Merrick ◽

Stephanie Pau ◽

Matteo Detto ◽

Eben North Broadbent ◽

Stephanie Bohlman ◽

...

Keyword(s):

Tropical Forest ◽

Primary Productivity ◽

Forest Canopy ◽

Vegetation Index ◽

Near Infrared ◽

Canopy Structure ◽

Gross Primary Productivity ◽

Spatiotemporal Variability ◽

Individual Tree ◽

Tropical Forest Canopy

Abstract. Presented here for the first time are emerging vegetation indicators: near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation, for a tropical forest canopy calculated using UAS-based hyperspectral data. Fine-scale tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad, is investigated using unmanned aerial vehicle data and eddy covariance-based gross primary productivity estimates. By exploiting near-infrared signals, emerging vegetation indicators captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI), then the normalized difference vegetation index (NDVI), which saturates. Wavelet analyses showed the dominant spatial variability of all indicators is driven by tree clusters and larger-than-tree-crown size gaps (not individual tree crowns or leaf clumps), but emerging indices and EVI captured structural information at smaller spatial scales (~50 m) than NDVI (~90 m) and lidar (~70 m). As predicted in previous studies, we confirm that NIRv and FCVI are virtually identical for a dense green canopy despite the differences in how these indices were derived. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated most strongly with gross primary productivity and photosynthetically active radiation. These emerging indicators, which are related to canopy structure and the radiation regime of vegetation canopies are promising tools to improve understanding of tropical forest canopy structure and function.

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Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

Biogeosciences ◽

10.5194/bg-18-6077-2021 ◽

2021 ◽

Vol 18 (22) ◽

pp. 6077-6091

Author(s):

Trina Merrick ◽

Stephanie Pau ◽

Matteo Detto ◽

Eben N. Broadbent ◽

Stephanie A. Bohlman ◽

...

Keyword(s):

Tropical Forest ◽

Forest Canopy ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Canopy Structure ◽

Temporal Heterogeneity ◽

Spatial And Temporal Heterogeneity ◽

And Function ◽

Tropical Forest Canopy

Abstract. Recently, remotely sensed measurements of the near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation have emerged as indicators of vegetation structure and function with potential to enhance or improve upon commonly used indicators, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The applicability of these remotely sensed indices to tropical forests, key ecosystems for global carbon cycling and biodiversity, has been limited. In particular, fine-scale spatial and temporal heterogeneity of structure and physiology may contribute to variation in these indices and the properties that are presumed to be tracked by them, such as gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR). In this study, fine-scale (approx. 15 cm) tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad and by lidar-derived height is investigated and compared to NIRv and EVI using unoccupied aerial system (UAS)-based hyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv, FCVI, and NIRvrad captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI) and then the normalized difference vegetation index (NDVI). Wavelet analyses showed the dominant spatial scale of variability of all indicators was driven by tree clusters and larger-than-tree-crown size gaps rather than individual tree crowns. NIRv, FCVI, NIRvrad, and EVI captured variability at smaller spatial scales (∼ 50 m) than NDVI (∼ 90 m) and the lidar-based surface model (∼ 70 m). We show that spatial and temporal patterns of NIRv and FCVI were virtually identical for a dense green canopy, confirming predictions in earlier studies. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated more strongly with GPP and PAR than did other indicators. NIRv, FCVI, and NIRvrad, which are related to canopy structure and the radiation regime of vegetation canopies, are promising tools to improve understanding of tropical forest canopy structure and function.

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Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

10.32920/14669124 ◽

2021 ◽

Author(s):

Yonghua Qu ◽

Ahmed Shaker ◽

Carlos Alberto Silva ◽

Carine Klauberg ◽

Ekena Rangel Pinagé

Keyword(s):

Tropical Forest ◽

Leaf Area Index ◽

Leaf Area ◽

Tropical Forests ◽

Airborne Lidar ◽

Lidar Data ◽

Area Index ◽

Dense Forest ◽

Modis Lai ◽

Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

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