Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

Fassnacht, Fabian Ewald; Härtig, Florian; Latifi, Hooman; Berger, Christian; Hernández, Jaime; Corvalán, Patricio; Koch, Bárbara

doi:10.1016/j.rse.2014.07.028

Cited by 342 publications

(256 citation statements)

References 78 publications

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“…feature importance order), and it is less sensitive to overfitting and in parameter selection [45][46][47]. In biomass estimation, RF has shown competitive accuracy among other estimation methods applied in forestry [43,48] and in agricultural [32,[49][50][51] applications. Only some studies have used RF in crop parameter estimations.…”

Section: Introductionmentioning

confidence: 99%

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

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Abstract:The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

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Section: Introductionmentioning

confidence: 99%

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

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“…With the ability of providing vegetation height information, data from ALS and SAR sensors have been used as auxiliary information for biomass estimation in all major forest ecosystems [11]. Literature reviews have attempted to assess the impact of different sensors, statistical modelling methods, inventory sample sizes, and inventory plot sizes in different forest types [9,11].…”

Section: Introductionmentioning

confidence: 99%

“…Literature reviews have attempted to assess the impact of different sensors, statistical modelling methods, inventory sample sizes, and inventory plot sizes in different forest types [9,11]. Results from these studies seem to be conclusive on two issues: (1) Use of ALS-sensors gives the best results compared to all other sensors for modelling biomass in terms of root mean square error (RMSE); and (2) that RMSE, as an expression of model precision, varies with forest type.…”

Section: Introductionmentioning

confidence: 99%

“…Following this approach, a relationship between biomass calculated from field measurements on inventory plots and remotely sensed data is modelled using statistical methods such as regression analysis, nearest neighbours, neural-networks, or ensemble learning (e.g., [11,19,20]). The models are subsequently used to predict biomass for population elements of the same size as the inventory plots.…”

Section: Introductionmentioning

confidence: 99%

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Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

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Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m 2 , sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m 2 to achieve the same precision as an inventory supported by ALS data. OPEN ACCESSRemote Sens. 2015, 7 9866

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“…The Friedman test is a nonparametric test method to detect differences between several related groups. These analysis methods have been used in many studies including both geography and remote sensing [61][62][63].…”

Section: One-way Anova and Friedman Testmentioning

confidence: 99%

Identifying the Lambertian Property of Ground Surfaces in the Thermal Infrared Region via Field Experiments

Qin

Yang

et al. 2017

Remote Sensing

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Abstract:Lambertian surfaces represent an important assumption when constructing thermal radiance transfer equations for remote sensing observations of ground surface temperatures. We identify the properties of ground surfaces in thermal infrared regions as Lambertian surfaces via field experiments. Because Lambertian surfaces present homogeneous thermal emissions levels in hemispheric directions for a specific ground surface under specific kinetic temperatures and emissions, we conducted a series of field experiments to illustrate the properties of such ground surfaces. Four typical ground surfaces were selected for the experiments to observe thermal emissions: bare soil, grass, water, and concrete. Radiance thermometers were used to observe ground emissions from seven directions: 30 • , 45 • , 60 • , 90 • , 120 • , 135 • , and 150 • . Solar zenith angles were considered for the observation of ground emissions. Experiments were conducted in five different regions of China (Beijing, Nanjing, Xilinguole, Yongzhou, and Jiangmen) during both daytime and nighttime. To determine whether different observation angles have significantly different effects on radiance, statistical analyses (ANOVA and Friedman test) were conducted. Post hoc multiple comparison tests and pairwise multiple comparisons were also conducted to examine the various pairings of observation angles and to measure the radiance differences. Roughly half of the radiance groups of all observed sites were tested via an ANOVA, and the remaining groups with unequal variances were subjected to the Friedman test. The results indicate that statistically significant differences in the radiance levels occurred among the seven angles for almost all of the sites (39 of the 40 groups). The results of our experiments indicate that the selected ground surfaces, especially the grass and the bare soil, may not behave with Lambertian properties in the thermal infrared region. This is probably attributed to the roughness of the selected surface, because we found that roughness is an important factor affecting the observed magnitude of thermal emission from different directions of the ground surface under study. Therefore, whether or not a terrestrial surface can be assumed to be a Lambertian surface should be based on their geometric structure. When the surface is relatively smooth, we can say that it is close to the Lambertian property in thermal emission.

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Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass

Cited by 342 publications

References 78 publications

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Identifying the Lambertian Property of Ground Surfaces in the Thermal Infrared Region via Field Experiments

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