Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information

Article Environmental Sciences

Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

Rodrigo Vieira Leite et al.

Summary: This study developed a novel framework using GEDI data and machine learning models to predict multi-layer fuel load in the Brazilian tropical savanna. The models exhibited better performance for woody fuels and total fuel loads and can be extended to other fire-prone regions for accurate measurements of fuel components.

REMOTE SENSING OF ENVIRONMENT (2022)

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Article Environmental Sciences

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Laura Duncanson et al.

Summary: This paper presents the development of models used by NASA's Global Ecosystem Dynamics Investigation (GEDI) to estimate forest aboveground biomass density (AGBD). The models were developed using globally distributed field and airborne lidar data, with simulated relative height metrics as predictor variables. The study found that stratification by geographic domain and the use of square root transformation improved model performance.

REMOTE SENSING OF ENVIRONMENT (2022)

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Article Environmental Sciences

The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring

David P. Roy et al.

Summary: The study investigates the impact of geolocation uncertainty on forest canopy height estimation using GEDI LiDAR data, and finds that this uncertainty can affect the reliability of canopy height retrieval, especially in cases of spatially heterogeneous canopies. The research highlights the need to consider GEDI geolocation uncertainty in forest change monitoring, particularly over fragmented or structurally diverse canopies.

SCIENCE OF REMOTE SENSING (2021)

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Article Computer Science, Interdisciplinary Applications

The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories

Andreas Hill et al.

Summary: Forest inventories use random sampling and remote sensing information to assess forest development and state, aiming to reduce costs and improve estimation precision. The R package forestinventory offers a comprehensive set of regression estimators for multiphase forest inventories and has been proven effective in various scientific studies.

JOURNAL OF STATISTICAL SOFTWARE (2021)

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Article Environmental Sciences