Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Abstract

Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km 2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R 2 (calibration) = 0.89, R 2 (validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha −1 , and RMSE(validation) = 14.80 t·ha −1 ). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

“…In fact, the finding of correlation analysis that variables significantly correlated with vegetation biomass varies largely with vegetation type implies that type-specific biomass estimations models should be constructed. Similarly, non-species-specific allometric growth models yielded larger errors than species-specific ones [69]. Urban vegetation cannot be regarded as a single vegetation type as it varies largely in biophysical characteristics and thus biomass.…”

Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

“…PLS is based on linear transitions from several original variables to relatively few orthogonal factors or latent variables. It is useful for establishing predictive models when the predictor variables are collinear [52]. As metrics extracted from GLAS may be collinear, a PLS model was developed.…”

Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales

“…Los resultados de este estudio son consistentes con otros de estimación de biomasa aérea en bosques tropicales. Cada una de estas métricas (P80, P90 y P95) se combinan con otras métricas de densidad o estadísticas de la distribución de altura para conformar los modelos generados por Clark et al (2011) en Costa Rica, en manglares de Brasil (Rocha de Souza et al, 2018), así como en bosques templados (Lefsky et al, 2002). Los percentiles P80 y P90 (9.6 m -10.6 m) corresponden al patrón de altura promedio para este tipo de vegetación.…”

“…Por otro lado, en los dos tipos de selva estudiados, el percentil 95 y la altura media, además de dos métricas relacionadas a la cobertura del dosel (el porcentaje de los primeros retornos encima de 2 m y el porcentaje de los primeros retornos encima de la altura media) contribuyeron significativamente a predecir la biomasa mediante modelos de regresión lineal. La idea principal de usar dos métricas en cada modelo fue crear una relación simple que involucre la distribución de altura y la profundidad en que los retornos LiDAR penetran el arbolado, como lo señalan Rocha de Souza et al (2018), además, es importante señalar que la adición de más variables en los modelos no incrementó significativamente la variación explicada.…”

“…En este sentido, estudios de propósito similar (Deo et al, 2017;Rocha de Souza et al, 2018) registran errores de geoposicionamiento de los sitios de muestreo menores a 1 m. No obstante, White et al (2013) recomiendan errores de geoposicionamiento de hasta 5 m en los sitios de muestreo en campo, además de otros métodos para disminuir el error, como registrar con el global positioning system (GPS) un mínimo de 500 puntos por parcela; aplicar corrección diferencial o incrementar el tamaño de parcela para asegurar un traslape suficiente.…”