Single Vegetation Index Research Articles

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Estimation of aboveground biomass of senescence grassland in China's arid region using multi-source data

Aboveground Biomass (AGB) in the grassland senescence period is a key indicator for assessing grassland fire risk and autumnal pasture carrying capacity. Despite the advancement of remote sensing in rapid monitoring of AGB on a regional scale, accurately monitoring AGB during the senescence period in vast arid areas remains a major challenge. Using remote sensing, environmental data, and 356 samples of grassland senescence period AGB data, this study utilizes the Gram-Schmidt Pan Sharpening (GS) method, multivariate selection methods, and machine learning algorithms (RF, SVM, and BP_ANN) to construct a model for AGB during senescence grassland, and applies the optimal model to analyze spatio-temporal pattern changes in AGB from 2000 to 2021 in arid regions. The results indicate that the GS method effectively enhances the correlation between measured AGB and vegetation indices, reducing model error to some extent; The accuracy of grassland AGB inversion models based on a single vegetation index is low (0.03 ≤ |R| ≤ 0.63), while the RF model constructed with multiple variables selected by the Boruta algorithm is the optimal model for estimating AGB in arid regions during the senescence period (R2 = 0.71, RMSE = 519.74 kg/ha); In the span of 22 years, the annual average AGB in the senescence period of arid regions was 1413.85 kg/ha, with regions of higher AGB primarily located in the northeast and southwest of the study area. The area experiencing an increase in AGB during the senescence period (79.97 %) was significantly larger than that with decreased AGB (20.03 %).

The Degradability of Microplastics May Not Necessarily Equate to Environmental Friendliness: A Case Study of Cucumber Seedlings with Disturbed Photosynthesis

In the environment, degradable plastics are decomposed into biodegradable microplastics (Bio-MPs), but there is limited study on the impact on plant growth and development. Therefore, this study aimed to investigate biodegradable polylactic acid microplastics (PLA-MPs) and nonbiodegradable polystyrene microplastics (PS-MPs) with different concentrations (0.02%, 0.2%, and 2% w/w) to explore their short-term toxic effects on cucumbers. The results of this study showed that PLA-MPs significantly (p < 0.05) reduced the aboveground and belowground biomass of cucumber seedlings compared to the control. At the level of 2% MPs, the chlorophyll content and PRI vegetation index of cucumber plants decreased significantly, anthocyanin content increased, and the photosynthetic system was disturbed. Likewise, the antioxidant defensive system of cucumber was affected after exposure to MPs stress, especially under 2% levels. The hyperspectral image is a novel technique which analyzed the chlorophyll content and absorption under MPs treatment; there was still a high correlation between chlorophyll content, anthocyanin content, and MCARI vegetation index, so a single vegetation index could be used for rapid detection of plant physiological status. Our study suggests that Bio-MPs have potential ecological toxicity that could affect the growth of cucumber seedlings through deactivation of the PSII reaction center. Therefore, biodegradable plastics do not seem to be the optimal solution, and there is an urgent need for long-term monitoring and evaluation of the biological toxicity of biodegradable MPs.

Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies

Timely monitoring of crop production using a remote sensing-based approach offers promise toward enhancing food security. Statistical models developed using satellite data typically employ a single vegetation index from a single sensor for yield estimation. With the increasing availability of satellite datasets, there is now an opportunity to investigate the potential of available vegetation indices from different sensors in estimating yields. The key objective of this study was to develop a best-performing satellite-based yield model for the Canadian Prairies for wheat, barley, and canola, trained using municipality-level data from 2009 to 2019. We tested the statistical performance of models built using (a) indices from different sensors (Landsat and Sentinel-2), (b) indices sensitive to different yield properties, and (c) single versus multiple vegetation indices. Results showed Landsat-NDWI as the best performing single-index across all indices and sensors for each crop. Sentinel-2-EVI performed best for wheat and canola and Sentinel-2-SR for barley; but these models were built using only 4 years of data. We found that best-performing single-index models recorded similar predictive accuracy as multi-index models during model validation. The results from this work suggest that satellite-based yield estimation can be improved by selecting the right index related to different crop properties.

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Walnut Acreage Extraction and Growth Monitoring Based on the NDVI Time Series and Google Earth Engine

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years.

Using a Vegetation Index as a Proxy for Reliability in Surface Reflectance Time Series Reconstruction (RTSR)

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of 0.01 to 0.03 in terms of surface reflectance.

Inversion models of aboveground grassland biomass in Xinjiang based on multisource data.

Grassland biomass monitoring is essential for assessing grassland health and carbon cycling. However, monitoring grassland biomass in drylands based on satellite remote sensing is challenging.Statistical regression models and machine learning have been used for the construction of grassland biomass models, but the predictive power for different grassland types is unclear. Additionally, the selection of the most appropriate variables to construct a biomass inversion model for different grassland types must be explored. Therefore,1201 ground-truthed data points collected from 2014-2021,including 15 Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices,geographic location and topographic data,and meteorological factors and vegetation biophysical indicators were screened for key variables using principal component analysis (PCA). The accuracy of multiple linear regression models, exponential regression models, power function models, support vector machine (SVM) models, random forest (RF) models, and neural network models was evaluated for the inversion of three types of grassland biomass. The results were as follows: (1) The biomass inversion accuracy of single vegetation indices was low, and the optimal vegetation indices were the soil-adjusted vegetation index (SAVI) (R2 = 0.255), normalized difference vegetation index (NDVI) (R2 = 0.372) and optimized soil-adjusted vegetation index (OSAVI) (R2 = 0.285). (2)Grassland above-ground biomass (AGB) was affected by various factors such as geographic location,topography, and meteorological factors, and the inverse models using a single environmental variable had large errors. (3) The main variables used to model biomass in the three types of grasslands were different. SAVI, aspect, slope, and precipitation (Prec.) were selected for desert grasslands; NDVI,shortwave infrared 2 (SWI2), longitude, mean temperature, and annual precipitation were selected for steppe;and OSAVI, phytochrome ratio (PPR), longitude, precipitation, and temperature were selected for meadows. (4) The non-parametric meadow biomass model was superior to the statistical regression model. (5) The RF model was the best model for the inversion of grassland biomass in Xinjiang, and this model had the highest accuracy for grassland biomass inversion (R2 = 0.656, root mean square error (RMSE) = 815.6 kg/ha),followed by meadow (R2 = 0.610, RMSE = 547.9 kg/ha) and desert grassland (R2 = 0.441, RMSE = 353.6 kg/ha).

Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning

Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monitoring and data collection were conducted during a cyanobacterial bloom period. Finally, 155 sets of valid data were obtained. The imaging data were obtained using a multispectral UAV, water samples were collected from the lake, and the chl-a concentration was obtained in the laboratory. Then, the images were preprocessed to extract the information from different wavebands. The univariate regression of each vegetation index and the regression using band information were used for comparative analysis. Four machine learning algorithms were used to build the model: support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and convolutional neural network (CNN). The results showed that the effect of estimating the chl-a concentration via multiple regression using vegetation indices was generally better than that via regression with a single vegetation index and original band information. The CNN model obtained the best results (R2 = 0.7917, RMSE = 8.7660, and MRE = 0.2461). This study showed the reliability of using multiple regression based on vegetation indices to estimate the chl-a of surface water.

Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data

Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees.

Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity

As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R2 = 0.67, RMSE = 2.24 g C·m−2·d−1); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R2 = 0.87, RMSE = 1.40 g C·m−2·d−1). Plant traits effectively enhance all prediction models’ accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction.

Prediction of Buckwheat Maturity in UAV-RGB Images Based on Recursive Feature Elimination Cross-Validation: A Case Study in Jinzhong, Northern China

Buckwheat is an important minor grain crop with medicinal and edible functions. The accurate judgment of buckwheat maturity is beneficial to reduce harvest losses and improve yield. With the rapid development of unmanned aerial vehicle (UAV) technology, it has been widely used to predict the maturity of agricultural products. This paper proposed a method using recursive feature elimination cross-validation (RFECV) combined with multiple regression models to predict the maturity of buckwheat in UAV-RGB images. The images were captured in the buckwheat experimental field of Shanxi Agricultural University in Jinzhong, Northern China, from September to October in 2021. The variety was sweet buckwheat of "Jinqiao No. 1". In order to deeply mine the feature vectors that highly correlated with the prediction of buckwheat maturity, 22 dimensional features with 5 vegetation indexes, 9 color features, and 8 texture features of buckwheat were selected initially. The RFECV method was adopted to obtain the optimal feature vector dimensions and combinations with six regression models of decision tree regression, linear regression, random forest regression, AdaBoost regression, gradient lifting regression, and extreme random tree regression. The coefficient of determination (R2) and root mean square error (RMSE) were used to analyze the different combinations of the six regression models with different feature spaces. The experimental results show that the single vegetation index performed poorly in the prediction of buckwheat maturity; the prediction result of feature space "5" combined with the gradient lifting regression model performed the best; and the R2 and RMSE were 0.981 and 1.70 respectively. The research results can provide an important theoretical basis for the prediction of the regional maturity of crops.

Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries

Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the accuracy on the test set were 0.8355, 0.7611, and 0.7940, respectively. This method could be applied to remote sensing classification of fruit trees in a large area, and could provide effective technical means for monitoring fruit tree planting areas with medium and high resolution remote sensing imageries.

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

Rice nitrogen status detection using commercial-scale imagery

Determining the mid-season nitrogen status of rice is important for precision application of fertilizer to optimize productivity. While there has been much research aimed at developing remote-sensing-based models to predict the nitrogen status of rice, this has been predominantly limited to scientific small plot trials, relying on experts performing radiometric calibrations, encompassing limited cultivars, seasons and locations, and uniform management practices. As such, there has been little testing of models at commercial scale, against the range of conditions encountered across entire growing regions. To fill this gap, this work brings together four years of data, from both experimental replicated plot trials (38 datasets with 1734 observations) and commercial farms (12 datasets with 106 observations). Using commercial scale imagery acquired from airplanes, a number of nitrogen uptake modeling methodologies were evaluated. Universal single vegetation index based linear regression models had prediction root mean squared error (RMSE) of more than 45 kg/ha when tested at the 12 commercial sites. Machine learning models using multiple remote sensing features were able to improve predictions somewhat (RMSE > 30 kg/ha). Practically useful accuracies were achieved after using three local field samples to calibrate models to each field image. The prediction RMSE using this methodology was 22.9 kg/ha, or 19.4%. This approach enables provision of optimal variable-rate mid-season rice fertilizer prescriptions to growers, while motivating continued research towards development of methods that reduce requirement of local sampling.

Identifying and understanding alternative states of dryland landscape: A hierarchical analysis of time series of fractional vegetation-soil nexuses in China’s Hexi Corridor

Understanding and early identification of state transitions in dryland systems’ landscape pattern are challenging but critical for conservation planning and sustainable management strategies. Currently the alternative states of drylands have been identified based on dynamics of single vegetation index (i.e., normalized difference vegetation index), with the interactions between vegetation and soil components often being neglected. Here, we proposed a framework for identifying the alternative states in relation to land degradation based on the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this framework, we initially generated 16-day fractional green vegetation endmember (green vegetation [GV]) and soil-related endmembers (sand [SL], saline [SA], dark surface [DA]) time series using a standardized linear spectral mixture analysis model in the Hexi Corridor during 2001–2017. A discrete wavelet transform based trends and turning points detection (DWT-TTD) algorithm was proposed to detect trends, turning point types, and timing of these time series records. These metrics were subsequently used to identify eight state transition pathways for each endmember and synthetic degradation processes at the appropriate 5–10 year scale. We found that the unmixed fractional endmembers exhibited a consistent agreement with both field abundance values and estimated fractions from high-spatial-resolution Google Earth images. Based on the DWT-TTD algorithm derived alternative pathways, we found that four positive pathways of GV and DA fractions can be observed in the vegetated areas were integrated with corresponding negative pathways of SA and SL fractions. These pathways resulted in remarkably increasing GV (+4551.81 km2) and DA (+9749.26 km2) and significantly declining SA (−1452.70 km2) and SL (−9555.57 km2) from 2001 to 2017. These processes were linked to increased precipitation and temperature and reduced drought degree. Also, the timing of the turning points (around 2007 and 2013) was largely coincidental with the timing of ecological management, suggesting that ecological governance played a crucial role in the state shifts. However, because of drought trends in the barren regions, land desertification exhibited an increasing occurrence in extent (+2.5% of total pixels) during the study period, which may increase in the future, as indicated by the large pixel proportion of initially positive reversal pathway for SL (9.31%). Compared to state-of-the-art approaches, the DWT-TTD algorithm, as a multi-resolution analysis method, has the potential to detect hierarchical temporal changes and disentangle the cross-scale inherent shifts of vegetation-soil systems. The framework can help devise sustainable management practices that maintain land degradation neutrality, especially when integrated with land use cover change analysis.

Stand Delineation of Pinus sylvestris L. Plantations Suffering Decline Processes Based on Biophysical Tree Crown Variables: A Necessary Tool for Adaptive Silviculture

Many planted Pinus forests are severely affected by defoliation and mortality processes caused by pests and droughts. The mapping of forest tree crown variables (e.g., leaf area index and pigments) is particularly useful in stand delineation for the management of declining forests. This work explores the potential of integrating multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data for stand delineation based on selected tree crown variables in Pinus sylvestris plantations in southern Spain. Needle pigments (chlorophyll and carotenes) and leaf area index (LAI) were quantified. Eight vegetation indices and ALS-derived metrics were produced, and seven predictors were selected to estimate and map tree crown variables using a Random Forest method and Gini index. Chlorophylls a and b (Chla and Chlb) were significantly higher in the non-defoliated and moderately defoliated trees than in severely defoliated trees (F = 14.02, p < 0.001 for Chla; F = 13.09, p < 0.001 for Chlb). A similar response was observed for carotenoids (Car) (F = 14.13, p < 0.001). The LAI also showed significant differences among the defoliation levels (F = 26.5, p < 0.001). The model for the chlorophyll a pigment used two vegetation indices, Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI); three WV-2 band metrics, and three ALS metrics. The model built to describe the tree Chlb content used similar variables. The defoliation classification model was established with a single vegetation index, Green Normalized Difference Vegetation Index (GNDVI); two metrics of the blue band, and two ALS metrics. The pigment contents models provided R2 values of 0.87 (Chla, RMSE = 12.98%), 0.74 (Chlb, RMSE = 10.39%), and 0.88 (Car, RMSE = 10.05%). The cross-validated confusion matrix achieved a high overall classification accuracy (84.05%) and Kappa index (0.76). Defoliation and Chla showed the validation values for segmentations and, therefore, in the generation of the stand delineation. A total of 104 stands were delineated, ranging from 6.96 to 54.62 ha (average stand area = 16.26 ha). The distribution map of the predicted severity values in the P. sylvestris plantations showed a mosaic of severity patterns at the stand and individual tree scales. Overall, the findings of this work underscore the potential of WV-2 and ALS data integration for the assessment of stand delineation based on tree health status. The derived cartography is a relevant tool for developing adaptive silvicultural practices to reduce Pinus sylvestris mortality in planted forests at risk due to climate change.

Single bands leaf reflectance prediction based on fuel moisture content for forestry applications

Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparaíso forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with a RMSE lower than 0.022, and a average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively.

Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning

Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran?

ABSTRACTSatellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R2. Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R2 = 0.47 and R2 = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.