Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Remote Sensing ◽

10.3390/rs13081595 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1595

Author(s):

Chunhua Li ◽

Lizhi Zhou ◽

Wenbin Xu

Keyword(s):

Aboveground Biomass ◽

Carbon Accounting ◽

Dynamic Monitoring ◽

Gradient Boosting ◽

Biomass Estimation ◽

Floodplain Wetlands ◽

Red Edge ◽

Extreme Gradient Boosting ◽

Ensemble Algorithms ◽

Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

Remote Sensing ◽

10.3390/rs13173488 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3488

Author(s):

Keren Goldberg ◽

Ittai Herrmann ◽

Uri Hochberg ◽

Offer Rozenstein

Keyword(s):

Time Series ◽

Mean Shift ◽

Vegetation Indices ◽

Gradient Boosting ◽

Support Vector ◽

Object Based ◽

Accuracy Measure ◽

Extreme Gradient Boosting ◽

Accuracy Measures ◽

Sentinel 2

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

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Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data

Remote Sensing ◽

10.3390/rs10111848 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1848 ◽

Cited By ~ 7

Author(s):

Santa Pandit ◽

Satoshi Tsuyuki ◽

Timothy Dube

Keyword(s):

Aboveground Biomass ◽

Vegetation Index ◽

Vegetation Indices ◽

Buffer Zone ◽

Landsat 8 ◽

Community Forest ◽

Pooled Data ◽

Spectral Bands ◽

Simple Ratio

Knowledge of forest productivity status is an important indicator of the amount of biomass accumulated and the role of terrestrial ecosystems in the carbon cycle. However, accurate and up-to-date information on forest biomass and forest succession remain rudimentary within natural forests. This study sought to understand and establish the potential of a new-generation sensor in estimating aboveground biomass (AGB) stored in the natural forest, also known as ‘community forest’ or buffer zone community forest (BZCF), in the Parsa National Park, Nepal. The utility of the 30-m resolution Landsat 8 Operational Land Imager (OLI) and in situ data was tested using two statistical approaches, namely multiple linear regression (MLR) and random forest (RF). The analysis was done based on four computational procedures. These included spectral bands, vegetation indices and pooled dataset (spectral bands + vegetation indices), and model selected important variables. AGB estimation based on pooled data showed that the RF algorithm produced better results when compared to the use of the MLR model. For instance, the RF model estimated AGB with an R2 value of 0.87 and a root mean square error of 20.50 t ha−1, as well as an R2 value of 0.95 and a RMSE of 13.3 t ha−1 when using selected important variables. Comparatively, the MLR using pooled data produced an R2 value of 0.56 and RMSE value of 37.01 t ha−1. The RF model selected Optimized Soil Adjusted Vegetation index (OSAVI), Simple ratio (SR), Modified simple ratio (MSR), and Normalized difference Vegetation index (NDVI) as the most important variables for estimating AGB, whereas MLR selected band 5 and SR. These findings demonstrate the relevance of the relatively new Landsat 8 sensor in the estimation of AGB in community buffer zones.

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Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Forests ◽

10.3390/f13010104 ◽

2022 ◽

Vol 13 (1) ◽

pp. 104

Author(s):

Fardin Moradi ◽

Ali Asghar Darvishsefat ◽

Manizheh Rajab Pourrahmati ◽

Azade Deljouei ◽

Stelian Alexandru Borz

Keyword(s):

Aboveground Biomass ◽

Nearest Neighbor ◽

Vegetation Indices ◽

Field Measurements ◽

K Nearest Neighbor ◽

Remotely Sensed Data ◽

Northern Iran ◽

Hyrcanian Forests ◽

Sentinel 2

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

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Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images

Transactions of the ASABE ◽

10.13031/trans.14305 ◽

2021 ◽

Vol 64 (6) ◽

pp. 2089-2101

Author(s):

Razieh Barzin ◽

Hamid Kamangir ◽

Ganesh C. Bora

Keyword(s):

Machine Learning ◽

Random Forest ◽

Vegetation Indices ◽

Leaf Nitrogen ◽

Gradient Boosting ◽

N Content ◽

Spectral Bands ◽

Red Edge ◽

Leaf N Content ◽

Leaf N

HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.

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EXPLORING THE POTENTIAL OF HIGH-RESOLUTION PLANETSCOPE IMAGERY FOR PASTURE BIOMASS ESTIMATION IN AN INTEGRATED CROP–LIVESTOCK SYSTEM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w12-2020-419-2020 ◽

2020 ◽

Vol XLII-3/W12-2020 ◽

pp. 419-424

Author(s):

A. A. Dos Reis ◽

B. C. Silva ◽

J. P. S. Werner ◽

Y. F. Silva ◽

J. V. Rocha ◽

...

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Predictor Variables ◽

Biomass Estimation ◽

Remotely Sensed Data ◽

Preliminary Study ◽

Temporal And Spatial ◽

Simple Ratio ◽

New Generation ◽

Livestock System

Abstract. Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May–August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g m−2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g m−2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.

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UAV-Based High-Throughput Approach for Fast Growing Cunninghamia lanceolata (Lamb.) Cultivar Screening by Machine Learning

Forests ◽

10.3390/f10090815 ◽

2019 ◽

Vol 10 (9) ◽

pp. 815 ◽

Cited By ~ 2

Author(s):

Xiaodan Zou ◽

Anjie Liang ◽

Bizhi Wu ◽

Jun Su ◽

Renhua Zheng ◽

...

Keyword(s):

Machine Learning ◽

High Throughput ◽

Random Forests ◽

Hyperspectral Image ◽

Vegetation Indices ◽

Tree Height ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting ◽

Crown Density

Obtaining accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using light detection and ranging (LiDAR) and three-dimensional (3-D) modeling. As an alternative, we provide a novel high-throughput strategy for predicting the biomass of forests in the field by vegetation indices. This study proposes an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density, which combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, which is commonly known as Chinese fir, in Fujian, China, images were collected while using a hyperspectral camera. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. The tree height and DBH of 2880 samples were manually measured and clustered into three groups—“Fast”, “median”, and “normal” growth groups—and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier that was generated by random forests had the best prediction accuracy when compared to other algorithms (75%). This framework can be applied to other tree species to make management and business decisions.

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UAV-Based High-Throughput Approach for Fast Growing Cunninghamia lanceolata (Lamb.) Cultivar Screening by Machine Learning

10.20944/preprints201907.0158.v1 ◽

2019 ◽

Author(s):

Xiaodan Zou ◽

Anjie Liang ◽

Bizhi Wu ◽

Jun Su ◽

Renhua Zheng ◽

...

Keyword(s):

Machine Learning ◽

Random Forests ◽

Hyperspectral Image ◽

Vegetation Indices ◽

Tree Height ◽

Third Party ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting ◽

Crown Density

Accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using LiDAR and 3-D modeling. We propose an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density which that combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, commonly known as Chinese fir, in Fujian, China, images were collected using a hyperspectral camera and orthorectified in HiSpectral Stitcher. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. Tree height and DBH of 2880 samples were measured manually and clustering into three groups: “fast growth,” “median,” growth and “normal” growth group, and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier generated by random forests had the best prediction accuracy compare to other algorisms (75%). This framework can be applied to other tree species to make management and business decisions.

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Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm

International Journal of Forestry Research ◽

10.1155/2020/4216160 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

The Dung Nguyen ◽

Martin Kappas

Keyword(s):

Random Forest ◽

Aboveground Biomass ◽

Nature Reserve ◽

Vegetation Indices ◽

Forest Biomass ◽

Model Performance ◽

Texture Features ◽

Predictor Variables ◽

Ecological Indicator ◽

Spectral Bands

Forest biomass is an important ecological indicator for the sustainable management of forests. The aim of this study was to estimate forest aboveground biomass (AGB) by integrating SPOT-6 data with field-based measurements using the random forest (RF) algorithm. In total, 52 remote sensing variables, including spectral bands, vegetation indices, topography data, and textures, were extracted from SPOT-6 images to predict the forest AGB of Xuan Lien Nature Reserve, Vietnam. To determine the optimal predictor variables for AGB estimation, 10 different RF models were built. To evaluate these models, 10-fold cross-validation was applied. We found that a combination of spectral and vegetation indices and topography variables offer the highest prediction results (Radj2 = 0.74 and RMSE = 61.24 Mg ha−1). Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm.

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Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

10.2196/preprints.17235 ◽

2019 ◽

Author(s):

Kasper Van Mens ◽

Joran Lokkerbol ◽

Richard Janssen ◽

Robert de Lange ◽

Bea Tiemens

Keyword(s):

Machine Learning ◽

Treatment Outcome ◽

Mental Health Treatment ◽

Mental Healthcare ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Trade Off ◽

Trade Offs ◽

Outcome Monitoring ◽

Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

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