Drones and machine learning for estimating forest carbon storage

Remote Sensing

Biomass is important in monitoring global carbon storage and the carbon cycle, which quickly and accurately estimates forest biomass. Precision forestry and forest modeling place high requirements on obtaining the individual parameters of various tree species in complex stands, and studies have included both the overall stand and individual trees. Most of the existing literature focuses on calculating the individual tree species’ biomass in a single stand, and there is little research on calculating the individual tree biomass in complex stands. This paper calculates the individual tree biomass of various tree species in complex stands by combining multispectral and light detection and ranging (LIDAR) data. The main research steps are as follows. First, tree species are classified through multispectral data combined with field investigations. Second, multispectral classification data are combined with LIDAR point cloud data to classify point cloud tree species. Finally, the divided ...

Computers and Electronics in Agriculture, 2020

The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches-namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting-on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (fieldderived) such as diameter at breast height, total height, and timber volume. A total of 370 trees had their dbh and height measured for validation purposes. Using LAStools we generated normalized Light Detection and Ranging (Lidar) point clouds and created a raster canopy height model at a 0.5x0.5 m spatial resolution following the construction of a digital terrain model and a digital surface model. The R package 'lidR' was set with the functions tree_detection (local maximum filter algorithm) and lastrees. Subsequently, we applied the function tree_metrics to extract individual metrics. Machine learning techniques were applied to the derived metrics to estimate dendrometric field measures. The machine learning models (MLM) with optimal hyperparameters showed similar predictive performances for modeling the variables diameter, height, and volume. All models had a rRMSE below 15% (for diameter at breast height), 9% (for height) and 29% (for volume). The Support Vector Regression algorithm showed the best performance. Our work demonstrates that all tested machine learning models are adequate and robust to handle the high dimensionality of UAV-Lidar data for the estimation of individual attributes, with Support Vector Regression model being the best performer in terms of minimal error rates.

Remote Sensing, 2022

Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.

Biodiversitas Journal of Biological Diversity

For low-income nations, a low-cost Unmanned Aerial Vehicle (UAV) offers an alternative to the traditional time-intensive forest survey that requires many resources. This study was conducted in the remnant forest with the dominant tree species, Dipterocarpus alatus Roxb., forming the upper canopy. Photogrammetry techniques with UAV images were used to obtain the Canopy Height Model (CHM). The results of tree height, individual tree detection, biomass, and carbon sequestration were compared between ground truthing and photogrammetry estimation. The large percentages of trees were automatically recognized. However, due to a closed forest canopy, some trees might have been left out from the actual count, leading to an undercounting of trees and an underestimation of the aboveground biomass (AGB). The photogrammetric dataset demonstrated a good tree height extraction accuracy and did not differ significantly from that determined by ground truthing (RMSE=2.59 m and 8.24%). The mean predicted height AGB from direct measurement was 24.76 tons ha-1 , higher than those obtained from single-and multi-set photogrammetry, 5.41 and 17.99 tons ha-1 , respectively. AGB and carbon sequestration estimated from photogrammetry were 72.66% of the ground truthing value. The accuracy of the photogrammetry results was acceptable and feasible for detecting individual tree heights, biomass, and carbon sequestration in the remnant forest. Overall, low-cost UAV could create a cost, time-efficient, and reasonably accurate localscale forest inventory.

PLOS ONE, 2020

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree ...

2019

Forests play a vital role in the global carbon cycle by sequestering carbon from the atmosphere, thereby helping in the regulation of climate. The monitoring of aboveground biomass (AGB), which accounts for most of the stored carbon stock, is essential for the execution of the REDD+ (Reducing Emissions from Deforestation and forest Degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) programme, that mandates regular, precise and reliable AGB estimation and its spatiotemporal variations. Remote sensing-based methods such as high-resolution optical sensor and Light Detection and Ranging (LiDAR) have been widely used to estimate forest AGB for resolving the limitations of traditional approaches. This study aims to integrate and optimise the parameters available from LiDAR and optical RapidEye datasets with the in-situ measurements for accurate estimation and mapping of AGB in tropical forests of Terai Arc Landscape (TAL) area in Nepal. This is performed using machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM). 52 LiDAR metrics are extracted using height and intensity information. 27 spectral, including band reflectance and vegetation indices (VIs), variables are derived in addition to 8 texture measure of each band (i.e., 40 textural variables). Seven prediction models ((1) LiDAR metrics, (2) spectral variables, (3) textural variables, (4) spectral and textural, (5) LiDAR and spectral, (6) LiDAR and textural, and (7) LiDAR, spectral, and textural combined variables models) are formed to compare and select the best model using RF and SVM based regression algorithms. With the help of LiDAR returns, two canopy height models (CHM), normal CHM and pit-free CHM are created. It was observed that pit-free CHM gave better results, root mean squared deviation (RMSD) of 1.09 m for tree heights, than the normal CHM (RMSD = 1.46 m). We also observed that LiDAR, spectral and textural combined model with 119 variables performed best for AGB prediction using both machine learning algorithms. However, RF regression performed better with an R 2 of 0.95, RMSE of 35.15 Mg ha-1 and RMSErel of 17.25 % compared to SVM regression with an R 2 of 0.40, RMSE of 48.29 Mg ha-1 and RMSErel of 23.70 %. RF was also used for extracting an optimal number of predictor variables based on their importance. Next, 20 most important variables were used for generation of the forest AGB spatial distribution map. The output estimates were validated using 15 independent sample plots data, results for which were satisfactory (R 2 = 0.72, RMSE = 47.71 Mg ha-1 , RMSErel = 23.41 %). Moreover, the uncertainty of AGB estimation was found to be within the range between 0 to 34 Mg ha-1 using Monte Carlo simulation. The result also shows that multi-sensor parameters such as near infra-red, red, red-edge (spectral bands), variance, contrast, dissimilarity, homogeneity, second angular momentum, mean (texture measure), bincentiles, relative height points count including other height metrics and percentile heights (LiDAR metrics) have strong relationship with the in-situ biomass. It is concluded that the combination of multi-sensor/source data using RF regression demonstrates to be a reliable algorithm for accurate estimation of tropical forest AGB. Based on the results of this study, it suggests that the estimation of biomass should be done using multi-sensor data coupled with field measurements with sufficient sample plots for improving accuracy.

A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey's height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m 2 /ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m 3 for canopy bulk density, 0.068 (0.133) kg/m 2 for available canopy fuel, and 0.33 (0.39) m 2 /m 2 for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis.

Proceedings of the AAAI Conference on Artificial Intelligence

Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost intensive and have been shown to be subjective. They can lead to substantial overestimation of the carbon stock and ultimately distrust in forest financing. The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising, but needs to be of high quality in order to replace the current forest stock protocols for certifications. In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we show that a deep learning-based end-to-end model using individual tree detection from low cost RGB-only drone imagery is accurately estimating f...

Remote Sensing

The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertica...

Described as a 'collaboratory', the Hub, founded by Professor Cecilia Åsberg, a leading scholar in gender studies, cultural studies of science and technology, and environmental studies at Linköping, is a space for research in the new humanities and more-than-human humanities, and work in the symbiotic arts and sciences. Its aim is to forge new alliances among researchers and practitioners/practices of posthumanities enabling thinking on "how to co-exist, work and think better together in a troubled world". It brings together philosophy, arts and sciences and academic activism, all informed by a "far-reaching societal commitment to democracy and co-existential ethics in multispecies practice".