Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

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Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Forests ◽

10.3390/f11010011 ◽

2019 ◽

Vol 11 (1) ◽

pp. 11

Author(s):

Pablito M. López-Serrano ◽

José Luis Cárdenas Domínguez ◽

José Javier Corral-Rivas ◽

Enrique Jiménez ◽

Carlos A. López-Sánchez ◽

...

Keyword(s):

Machine Learning ◽

Aboveground Biomass ◽

Goodness Of Fit ◽

Accurate Estimation ◽

Support Vector ◽

Landsat 8 ◽

Sensing Applications ◽

Learning Techniques ◽

Physical Variables ◽

Selection Of

An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0216 ◽

2020 ◽

Vol 50 (4) ◽

pp. 390-398

Author(s):

Chao Li ◽

Mingyang Li ◽

Yingchang Li

Keyword(s):

Remote Sensing ◽

Aboveground Biomass ◽

Vegetation Type ◽

Coniferous Forest ◽

Mixed Forest ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Dummy Variable ◽

Crown Density ◽

Forest Aboveground Biomass

Optical remote sensing data are widely used in estimation of forest aboveground biomass (AGB), and the accuracy of AGB estimations has drawn wide attention. A method to improve the accuracy of remote sensing-based AGB models was developed by combining Landsat 8’s Operational Land Imager (OLI) and forest crown density (FCD). Remote sensing-based AGB models with and without an FCD dummy variable were developed using linear regression based on vegetation type (coniferous forest, broadleaf forest, mixed forest, and total vegetation). The differences between the models with and without an FCD dummy variable were analysed and compared. The models involving stratification of vegetation types provided more accurate estimations than the models of total vegetation. The models with an FCD dummy variable performed better than the models without an FCD dummy variable for each vegetation type. In each FCD class, the models with an FCD dummy variable provided more accurate estimations than the models without an FCD dummy variable, and the over- and underestimation problems associated with the models without an FCD dummy variable in thin and dense stands were significantly alleviated by the models with an FCD dummy variable. Therefore, introducing FCD into remote sensing-based AGB models has great potential to improve AGB estimation.

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Comparative Analysis of Seasonal Landsat 8 Images for Forest Aboveground Biomass Estimation in a Subtropical Forest

Forests ◽

10.3390/f11010045 ◽

2019 ◽

Vol 11 (1) ◽

pp. 45 ◽

Cited By ~ 2

Author(s):

Chao Li ◽

Mingyang Li ◽

Jie Liu ◽

Yingchang Li ◽

Qianshi Dai

Keyword(s):

Aboveground Biomass ◽

Regional Scale ◽

Coniferous Forest ◽

Mixed Forest ◽

Subtropical Forest ◽

Acquisition Time ◽

Biomass Estimation ◽

Landsat 8 ◽

Forest Types ◽

Forest Aboveground Biomass

To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has, therefore, been widely used for regional scale AGB estimation; however, most studies have been based solely on peak season images without performance comparison of other seasons; this may ultimately affect the accuracy of AGB estimation. To explore the effects of utilizing various seasonal images for AGB estimation, we analyzed seasonal images collected using Landsat 8 OLI for a subtropical forest in northern Hunan, China. We then performed stepwise regression to estimate AGB of different forest types (coniferous forest, broadleaf forest, mixed forest and total vegetation). The model performances using seasonal images of different forest types were then compared. The results showed that textural information played an important role in AGB estimation of each forest type. Stratification based on forest types resulted in better AGB estimation model performances than those of total vegetation. The most accurate AGB estimations were achieved using the autumn (October) image, and the least accurate AGB estimations were achieved using the peak season (August) image. In addition, the uncertainties associated with the peak season image were largest in terms of AGB values < 25 Mg/ha and >75 Mg/ha, and the quality of the AGB map depicting the peak season was poorer than the maps depicting other seasons. This study suggests that the acquisition time of forest images can affect AGB estimations in subtropical forest. Therefore, future research should consider and incorporate seasonal time-series images to improve AGB estimation.

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Linking the spatiotemporal variation of litterfall to standing vegetation biomass in Brazilian savannas

Journal of Plant Ecology ◽

10.1093/jpe/rtaa039 ◽

2020 ◽

Vol 13 (5) ◽

pp. 517-524

Author(s):

Alan N Costa ◽

Jeane R Souza ◽

Karyne M Alves ◽

Anderson Penna-Oliveira ◽

Geisciele Paula-Silva ◽

...

Keyword(s):

Aboveground Biomass ◽

Vegetation Structure ◽

Species Abundance ◽

Basal Area ◽

Global Scale ◽

Woody Vegetation ◽

Litter Production ◽

Vegetation Types ◽

Cylindrical Volume ◽

Landscape Scales

Abstract Aims Litterfall at a global scale is affected by climate, edaphic features and vegetation structure, with litter production increasing from grasslands to forests following the rise in standing biomass. However, at landscape scales, the same relationship between litter production and vegetation structure has rarely been studied and comparisons of litterfall patterns between adjacent, structurally distinct communities are lacking. Here, we use a standardized methodology to describe the structural differences among four savanna physiognomies and analyze their relationship with changes in litterfall across the Cerrado. Methods We evaluated the woody vegetation structure and composition in 48 sites, equally distributed across four physiognomies and monitored the monthly litter production from April 2014 to March 2015. Important Findings Results showed that the density, basal area, cylindrical volume and aboveground biomass of woody vegetation differ among physiognomies, increasing consistently from cerrado ralo, cerrado típico, cerrado denso and cerradão. Indeed, we found a strong and positive relationship between aboveground biomass and annual litter production, with litter yield increasing from 0.9 to 8.4 Mg ha−1 across different physiognomies, following the increment in vegetation structure. Monthly production was seasonal and similar among vegetation types, increasing during the dry season. Leaves comprised the dominant fraction (approx. 85%) and litterfall seasonality primarily resulted from the concentration of leaf shedding during dry months. However, the temporal pattern of litterfall throughout the year showed a gradual reduction in the seasonality from open to closed vegetation types, likely following the decrease of deciduous species abundance in the plant community. Our results showed that changes in vegetation structure may affect spatial and temporal litterfall patterns in different physiognomies, which co-occur across the Cerrado landscape, with potential implications for the overall functioning of this ecosystem. Moreover, these findings highlight the use of standardized methods as essential to correctly compare litterfall patterns among different environments.

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Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images

Remote Sensing ◽

10.3390/rs10071144 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1144 ◽

Cited By ~ 13

Author(s):

Wimala van Iersel ◽

Menno Straatsma ◽

Hans Middelkoop ◽

Elisabeth Addink

Keyword(s):

Land Cover ◽

Large Scale ◽

Accurate Solution ◽

Vegetation Types ◽

Time Step ◽

Floodplain Vegetation ◽

Herbaceous Vegetation ◽

Single Time ◽

Uav Images

The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.

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Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽

10.3390/s21134442 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4442

Author(s):

Zijie Niu ◽

Juntao Deng ◽

Xu Zhang ◽

Jun Zhang ◽

Shijia Pan ◽

...

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Semantic Segmentation ◽

Dynamic Monitoring ◽

Support Vector ◽

Distribution Maps ◽

Time Operation ◽

Aerial Vehicle ◽

Uav Images ◽

Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

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Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Remote Sensing ◽

10.3390/rs13040581 ◽

2021 ◽

Vol 13 (4) ◽

pp. 581 ◽

Cited By ~ 2

Author(s):

Yuanyuan Fu ◽

Guijun Yang ◽

Xiaoyu Song ◽

Zhenhong Li ◽

Xingang Xu ◽

...

Keyword(s):

Winter Wheat ◽

Least Squares ◽

Aboveground Biomass ◽

Regression Models ◽

Digital Images ◽

Predictive Performance ◽

Estimation Accuracy ◽

Support Vector ◽

Biomass Estimation ◽

High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Remote Sensing ◽

10.3390/rs13152962 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2962

Author(s):

Jingyi Wang ◽

Huaqiang Du ◽

Xuejian Li ◽

Fangjie Mao ◽

Meng Zhang ◽

...

Keyword(s):

Aboveground Biomass ◽

Geographically Weighted Regression ◽

Local Variation ◽

Zhejiang Province ◽

Weighted Regression ◽

Landsat 8 ◽

Bamboo Forest ◽

Prediction Ability ◽

Gwr Model ◽

Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

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