Estimation Of Grassland Biomass Research Articles

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R2 of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time.

Crucial roles of the optimal time-scale of water condition on grassland biomass estimation on Qinghai–Tibet Plateau

The effect of the time-scale of water conditions on vegetation productivity has been widely studied by the academic community. However, the relationship between the time-scale of water conditions and the vegetation growth rhythm and the effect of this relationship on vegetation biomass estimation have rarely been discussed. Here, we analyzed the occurrence times of major phenological events on alpine grasslands using the widely distributed “site-dominant species” dataset and set a series of time-scales for accumulated precipitation and standardized precipitation evapotranspiration index based on phenological information. Then, we combined large-scale aboveground/belowground biomass datasets to evaluate the role of the optimal time-scale for water conditions in aboveground/belowground biomass estimation. The results showed that (1) the optimal time-scale for water conditions with the greatest effects on aboveground biomass was on the month before the end of flowering or the onset of fruit maturity. The optimal time-scale for water condition effects on belowground biomass was earlier and longer than that for the aboveground biomass. The optimal time-scales for accumulated precipitation and standardized precipitation evapotranspiration index effects on belowground biomass were at five months before the end of flowering or the beginning of fruit ripening and the three months before the first flowering, respectively. (2) The aboveground and belowground biomass were underestimated by 11 % and 9 %, respectively, when the water conditions at the optimal time-scales were ignored. (3) The interannual variability in aboveground/belowground biomass was more effectively captured by considering the optimal time-scales of water conditions, especially in water-restricted areas. Overall, this study indicated that terrestrial carbon cycle models should incorporate information on the lag-effects of the water conditions in previous periods. In the future, increasing the number of belowground biomass observations and conducting monthly belowground biomass monitoring sooner will be key to revealing the mechanisms of the belowground biomass response to climate change.

UAV Hyperspectral Characterization of Vegetation Using Entropy-Based Active Sampling for Partial Least Square Regression Models

Optimization of agricultural practices is key for facing the challenges of modern agri-food systems, which are expected to satisfy a growing demand of food production in a landscape characterized by a reduction in cultivable lands and an increasing awareness of sustainability issues. In this work, an operational methodology for characterization of vegetation biomass and nitrogen content based on close-range hyperspectral remote sensing is introduced. It is based on an unsupervised active learning technique suitable for the calibration of a partial least square regression. The proposed technique relies on an innovative usage of Shannon’s entropy and allows for the set-up of an incremental monitoring framework from scratch aiming at minimizing field sampling activities. Experimental results concerning the estimation of grassland biomass and nitrogen content returned RMSE values of 2.05 t/ha and 4.68 kg/ha, respectively. They are comparable with the literature, mostly relying on supervised frameworks and confirmed the suitability of the proposed methodology with operational environments.

Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment

Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.

基于机器学习算法的草地地上生物量估测——以祁连山草地为例

PDF HTML阅读 XML下载 导出引用 引用提醒 基于机器学习算法的草地地上生物量估测——以祁连山草地为例 DOI: 10.5846/stxb202203180669 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 第二次青藏高原综合科学考察研究（2019QZKK0302）；中国科学院战略性先导科技专项（A类）项目（XDA23100201） Estimation of grassland biomass using machine learning methods: A case study of grassland in Qilian Mountains Author: Affiliation: Fund Project: the Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2019QZKK0302), the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (grant no. XDA23100201) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:草地地上生物量（Aboveground Biomass，AGB）是指导畜牧业生产管理的重要指标，是草畜平衡综合分析的基础。目前，有关祁连山草地AGB反演的研究较少，且多源数据间的尺度差异问题并未得到很好的解决。为了解祁连山草地AGB的空间分布状况，利用Sentinel-2多光谱数据、无人机（Unmanned Aerial Vehicle，UAV）数据以及2021年植被生长期实测草地AGB数据实现了空天地一体化监测，通过决策树回归（Decision Tree Regression，DTR）、随机森林回归（Random Forest Regression，RFR）、梯度提升决策回归树（Gradient Boosting Regression Tree，GBRT）以及极致梯度提升（eXtreme Gradient Boosting，XGBoost）共4种算法反演草地AGB的适用性分析，利用最优模型反演了祁连山草地的AGB空间分布状况。结果表明：研究区内多种植被指数所表现出的特性有所差异。祁连山地区AGB在空间分布上呈现出由西北向东南递增的趋势，平均AGB为925.43kg/hm2。6种植被指数与实测AGB之间均表现为显著正相关，适合作为祁连山草地AGB遥感反演的指标；XGBoost模型较其它模型具有最高的R2值（0.78）和精度（74.75%）、最低的均方根误差（RMSE，99.74 kg/hm2）和平均绝对误差（MAE，71.60 kg/hm2），模型反演效果最好；UAV数据能够提供更加详细的空间细节特征，减小Sentinel-2数据和实地采样数据间的尺度差异；因此，基于6种植被指数与祁连山草地AGB间的相关性，构建XGBoost模型反演研究区草地AGB空间分布状况是具有实践意义的。研究结果将为指导祁连山草地畜牧业的发展和维护草地生态系统的平衡提供一定的参考价值与数据支撑。 Abstract:Aboveground biomass (AGB) is an important indicator to guide the management of livestock industry, and it is the basis of comprehensive analysis of the balance between grassland and livestock. To date, only few studies have studied the spatial distribution of grassland AGB in Qilian Mountains, and the scale differences of multi-sources data have not been well solved. Therefore, in order to understand the spatial distribution of AGB in Qilian Mountains, we used the space-air-ground integrated method based on Sentinel-2 multispectral data, Unmanned Aerial Vehicle (UAV) data and the measured AGB data during the growth period of vegetation in 2021. In addition, we analyzed the applicability of Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT) and eXtreme Gradient Boosting (XGBoost) algorithms for AGB inversion. Finally, we mapped the spatial distribution of AGB in the study area using the optimal model among all the models. The results verified that the effectiveness of vegetation indices varied in study area. Generally, the results indicated that the spatial distribution had an increasing trend from northwest to southeast, with an average AGB density of 925.43 kg/hm2. A significantly positive correlation was found between 6 vegetation indices and measured AGB, and both of the indices was suitable for inversion of grassland AGB in the Qilian Mountains. Moreover, compared with other models, the performance of XGBoost model was the best, with the highest R2 of 0.78 and accuracy of 74.75%, the lowest Root Mean Squared Error (RMSE) of 99.74 kg/hm2and Mean Absolute Deviation (MAE) of 71.60 kg/hm2. In addition, UAV data provided spatial characteristics in detail, which reduced the scale difference between Sentinel-2 and the measured data. Therefore, on the basis of the correlation between 6 vegetation indices and AGB, it is of practical significance to construct the XGBoost model to invert the spatial distribution of AGB in grassland of Qilian Mountains. The results can provide a reference value and data support for guiding the development of livestock industry and maintaining the balance of grassland ecosystem. 参考文献 相似文献 引证文献

Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes

Grassland biomass is an essential part of the regional carbon cycle. Rapid and accurate estimation of grassland biomass is a hot topic in research on grassland ecosystems. This study was based on field-measured biomass data and satellite remote sensing data from the Moderate resolution imaging spectroradiometer (MODIS). A generalized linear model (GLM) was used to analyze the aboveground biomass (AGB), dynamic changes, and relevance of climatic factors of the typical/desert steppe in Inner Mongolia during the growing seasons from May 2009 to October 2015. The results showed that: (1) The logarithmic function model with the ratio vegetation index (RVI) as the independent variable worked best for the typical steppe area in Inner Mongolia, while the power function model with the normalized differential vegetation index (NDVI) as the independent variable worked best for the desert steppe area. The R2 values at a spatial resolution of 250 m were higher than those at a spatial resolution 500 m. (2) From 2009 to 2015, the highest values of AGB in the typical steppe and desert steppe of Inner Mongolia both appeared in 2012, and were 41.9 Tg and 7.0 Tg, respectively. The lowest values were 30.7 Tg and 5.8 Tg, respectively, in 2009. (3) The overall spatial distribution of AGB decreased from northeast to southwest. It also changed considerably over time. From May to August, AGB at the same longitude increased from south to north with seasonal variations; from August to October, it increased from north to south. (4) A variation partitioning analysis showed that in both the typical steppe and desert steppe, the combined effect of precipitation and temperature contributed the most to the aboveground biomass. The individual effect of temperature contributed more than precipitation in the typical steppe, while the individual effect of precipitation contributed more in the desert steppe. Thus, the hydrothermal dynamic hypothesis was used to explain this pattern. This study provides support for grassland husbandry management and carbon storage assessment in Inner Mongolia.

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.

Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment

Remote sensing based grassland carrying capacity assessments are not commonly applied in rangeland management. Possible reasons for this include non-equilibrium thinking in rangeland management, and the costliness of existing remotely sensed biomass estimation that carrying capacity assessments require. Here, we present a less demanding approach for grassland biomass estimation using the MODIS Net Primary Production (NPP) product and demonstrate its use in carrying capacity assessment over the mountain grasslands of Azerbaijan. Based on publicly available estimates of the fraction of total NPP partitioned to aboveground NPP (fANPP) we calculate the aboveground biomass produced from 2005 to 2014. Validation of the predicted aboveground biomass with independent field biomass data collected in 2007 and 2008 confirmed the accuracy of the aboveground biomass product and hence we considered it appropriate for further use in the carrying capacity assessment. A first assessment approach, which allowed for consumption of 65% of aboveground biomass, resulted in an average carrying capacity of 12.6 sheep per ha. A second more realistic approach, which further restricted grazing on slopes steeper than 10%, resulted in a stocking density of 6.20 sheep per ha and a carrying capacity of 3.93 million sheep. Our analysis reveals overgrazing of the mountain grasslands because the current livestock population which consists of at least 8 million sheep, 0.5 million goats and an unknown number of cattle exceeds the predicted carrying capacity of 3.93 million sheep. We consider that the geographically explicit advice on sustainable stocking densities is particularly attractive to regulate grazing intensity in geographically varied terrain such as the mountain grasslands of Azerbaijan. We further conclude that the approach, given its generic nature and the free availability of most input data, could be replicated elsewhere. Hence, we advise considering its use where traditional carrying capacity assessments are difficult to implement.

Evaluation of 3D point cloud-based models for the prediction of grassland biomass

Farmers, as well as agronomists, are intrigued by efficient quantification of grassland biomass at field-scale. Canopy surface height (CSH) based non-destructive grassland biomass estimation over a larger area has important advantages compared to destructive methods. 3D point clouds generated from remote sensing (RS) data offer a systematic methodology to derive CSH and estimate grassland biomass. This study evaluated 3D point clouds derived from a terrestrial laser scanner (TLS) and an unmanned aerial vehicle (UAV)-borne structure from motion (SFM) approach for grassland biomass estimation over three grasslands with different composition and management practice in northern Hesse, Germany. TLS data, UAV-borne images and reference biomass data were collected two days before each harvest from the selected grasslands in 2017. Three levels of linear empirical models (i.e. sampling date-specific, grassland-specific and global) were developed to estimate grasslands fresh and dry biomass using CSH derived from TLS and SFM 3D point clouds. The aforementioned three level models resulted in an average nRMSE of 17.2%, 25.3%, and 28.7% respectively for the grassland dry biomass estimation based on CSH from TLS data. Similarly, models based on CSH from SFM data estimated dry biomass with somewhat higher average nRMSE of 19.5%, 27.1%, and, 36.2% respectively. In general, models with 3D point clouds from UAV-borne SFM was slightly outperformed by models with TLS point cloud data. This study also identified that the utilisation of UAV-borne SFM developed digital terrain model as a reference layer to derive CSH could enhance the performance of the models with SFM data. Furthermore, the performance of the biomass estimation models was affected by both species richness and sward heterogeneity of the grasslands. Overall, these results disclosed the potential of 3D point cloud derived from RS for estimating field-scale grassland biomass.

Biomass estimation of desert steppe based on spectral indices along a precipitation gradient

Accurate estimation of grassland biomass in desert steppe is crucial for various studies such as grassland dynamic monitoring, scientific management, and environmental degradation. Observational tests were conducted in the Ordos desert steppe in July and August from 2014 to 2016 to measure the grassland biomass and grassland canopy spectral data along a precipitation gradient, to analyze the spectral reflectance characteristics and the correlation between different spectral indices and grassland biomass in the range of 350–1830 nm, and to establish and verify a model for estimating grassland biomass of desert steppe. The results showed that the spectral reflectance of the Ordos desert steppe is similar to the spectral characteristics of typical green vegetation. The spectral reflectance values of control and of lightly, medium-, and heavily degraded plots successively increased in the visible light band at 350–696 nm and decreased in the near-infrared band at 792–1349 nm. The sensitive band of the normalized difference spectrum indices after optimization was 705 and 710. The coefficient of determination of normalized difference spectral indices (705 and 710) and grassland biomass was R2 = 0.735. These values were significantly correlated at the p < 0.01 level. The R2 values of the fitted power function model and the verification power function model were 0.764 and 0.746, respectively. The root mean square error was 41.59 g/m2. The relative error was 28.37%, and the constructed power function model was the best model. Therefore, the optimized spectral indices can be used to effectively estimate the grassland biomass of desert steppe.

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Accurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during 2001–2005. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37±180.84Tg (i.e., 532.02±99.71g/m2) during 2001–2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo>DATsrc>MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

Training reduced subjectivity of comparative yield method of estimation of grassland biomass

Measuring grassland biomass using objective, quick and accurate methods is important for practical purposes in agriculture and ecology. Most existing techniques are either expensive or require advanced technical skills making them unaffordable and impractical for farmers in developing countries. The comparative yield method is a quick technique that involves the estimation of grassland biomass from visual scores. Despite its potential for use by farmers with minimal technical skills, it has been criticized for being subjective, thus limiting its use for research purposes only. We hypothesized that training and establishment of agreed-upon ground rules can reduce this subjectivity. We compared data collected by 36 farmers in northern Ethiopia, who participated under three instruction groups. Group 1 was involved both in the selection of reference plots and calibration plots and agreed-on ground rules. Group 2 was shown the reference plots and orientated about the ground rules. Group 3 was neither shown reference plots nor orientated on ground rules. Levene's test for equality of variance was performed among the estimations made by individuals. Mean values of scores and biomass measurements varied among groups. The variability of records was higher in groups 3 and 2 than in group 1, indicating the need for training and agreed-upon ground rules to achieve objectivity. We conclude that with minimal training and agreement on ground rules, the comparative yield method can be used by farmers and ecologists in developing countries to estimate grassland biomass with better precision and accuracy.

Are soil‐adjusted vegetation indices better than soil‐unadjusted vegetation indices for above‐ground green biomass estimation in arid and semi‐arid grasslands?

AbstractAccurate estimation of grassland biomass has been a central focus due to its importance in ecosystem processes and carbon cycles. This study aimed to examine whether the performance of soil‐adjusted vegetation indices for estimating above‐ground green biomass was better than that of soil‐unadjusted vegetation indices in arid and semi‐arid grasslands. Above‐ground green biomass in desert steppe of Inner Mongolia and corresponding moderate‐resolution imaging spectroradiometer (MODIS) surface reflectance 8‐day composite MOD09Q1 data were collected during late September of 2013. Results showed that soil‐adjusted SAVI (soil‐adjusted vegetation index), MSAVI (modified soil‐adjusted vegetation index), OSAVI (optimized soil‐adjusted vegetation index), TSAVI (transformed soil‐adjusted vegetation index), ATSAVI (adjusted transformed soil‐adjusted vegetation index) and PVI (perpendicular vegetation index) did not improve estimation accuracy over soil‐unadjusted simple ratio (SR) and normalized difference vegetation index (NDVI), due to low green vegetation cover (&lt;30%) in the study area. Our results suggest that these soil‐adjusted vegetation indices may be not suitable for describing green vegetation information in arid and semi‐arid grasslands with low green vegetation cover (&lt;30%).

Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m−2; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert.

Application of fused data to grassland biomass estimation

Application of fused data to grassland biomass estimation

Ground based digital imagery for grassland biomass estimation

Aboveground biomass was estimated on the shortgrass prairie of eastern Colorado using ground based conventional (RGB) digital camera imagery. The accuracy and efficiency of image‐based estimates were compared with clipped biomass measurements. Field measurements of aboveground biomass were obtained on three grazing treatments and three sample dates (phenological status). Grazing treatments did not significantly affect (p>0.10) estimates of clipped green biomass taken from digital images. However, plant phenology, green biomass estimates from images, and the interaction of plant phenology and green biomass estimates from images significantly affected clipped green biomass measurements (p≤0.04). Analysed images provided fair estimates of total clipped green biomass (R2 = 0.55) and clipped green biomass without cactus (R2 = 0.73) when plant phenological status was included in the models. When plant phenology was removed from the models, the variability explained by green biomass estimates from images declined to 25% for clipped green biomass, and 32% for clipped green biomass without cactus. Thus, results showed that plant phenological status was the most important variable in the prediction of green vegetation biomass. Results indicated that the usefulness of RGB digital camera imagery for green biomass estimation is limited for the shortgrass prairie.