Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

Tillage erosion as an important driver of in‐field biomass patterns in an intensively used hummocky landscape

Tillage erosion causes substantial soil redistribution that can exceed water erosion especially in hummocky landscapes under highly mechanized large field agriculture. Consequently, truncated soil profiles can be found on hill shoulders and top slopes, whereas colluvial material is accumulated at footslopes, in depressions, and along downslope field borders. We tested the hypothesis that soil erosion substantially affects in-field patterns of the enhanced vegetation index (EVI) of different crop types on landscape scale. The interrelation between the EVI (RAPIDEYE satellite data; 5 m spatial resolution) as a proxy for crop biomass and modeled total soil erosion (tillage and water erosion modeled using SPEROS-C) was analyzed for the Quillow catchment (size: 196 km2) in Northeast Germany in a wet versus normal year for four crop types (winter wheat, maize, winter rapeseed, winter barley). Our findings clearly indicate that eroded areas had the lowest EVI values, while the highest EVI values were found in depositional areas. The differences in the EVI between erosional and depositional sites are more pronounced in the analyzed normal year. The net effect of total erosion on the EVI compared to areas without pronounced erosion or deposition ranged from −10.2% for maize in the normal year to +3.7% for winter barley in the wet year. Tillage erosion has been identified as an important driver of soil degradation affecting in-field crop biomass patterns in a hummocky ground moraine landscape. While soil erosion estimates are to be made, more attention should be given toward tillage erosion.ISSN:1085-3278ISSN:1099-145

Energy Production from Forest Biomass: An Overview

As long as care is taken regarding stand and forest sustainability, forest biomass is an interesting alternative to fossil fuels because of its historical use as an energy source, its relative abundance and availability worldwide, and the fact that it is carbon-neutral. This study encompasses the revision of the state of the sources of forest biomass for energy and their estimation, the impacts on forests of biomass removal, the current demand and use of forest biomass for energy, and the most used energy conversion technologies. Forests can provide large amounts of biomass that can be used for energy. However, as the resources are limited, the increasing demand for biomass brings about management challenges. Stand structure is determinant for the amount of residues produced. Biomass can be estimated with high accuracy using both forest inventory and remote sensing. Yet, remote sensing enables biomass estimation and monitoring in shorter time periods. Different bioenergy uses and conversion technologies are characterized by different efficiencies, which should be a factor to consider in the choice of the best suited technology. Carefully analyzing the different options in terms of available conversion technologies, end-uses, costs, environmental benefits, and alternative energy vectors is of utmost importance

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Study of winter wheat growth and development in Oklahoma using a hierarchical Bayesian approach

In this dissertation, we discussed the underlying mechanisms behind growth and yield patterns in Oklahoma wheat and simultaneously introduced a unique approach to data analysis in crop science. First, the relationships between wheat yield, yield components, and weather variables were explored to understand source-sink balance. The analysis was performed using a Bayesian hierarchical model. Bayesian analysis quantifies uncertainties around the parameter values which helps to realize confidence in the results. Environmental factors were found to explain more yield variability than genotypic factors and wheat yield was found to be limited by both source and sink in Oklahoma. Second, wheat growth patterns in Oklahoma were investigated using a repeated measures dataset on leaf area index and biomass using a dynamic ordinary differential equation (ODE) modeling approach within a Bayesian hierarchical framework. Dynamic crop models are often complex with many parameters which limit their scope. In this dissertation, we have proposed a simple dynamic ODE model with few parameters. The proposed dynamic ODE model was also compared to a traditional data analysis method (linear mixed model) for repeated measures data. Results showed that neither model outperformed the other in terms of prediction. However, the dynamic ODE model offered an advantage of biologically meaningful parameters that was not apparent with the linear models. Third, the dynamic ODE model was extended to include a water balance component in order to investigate how changes in soil moisture throughout the growing season impacts wheat yield production. It was established that the water balance component is most important to make yield predictions under water limiting conditions. Further research is required to overcome the limitations of the water balance model

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) to estimate LAI and biomass with combined OSVIs and RPPs (the product of OSVIs and RPPs (COSVI-RPPs)), (3) to use multiple stepwise regression (MSR) and partial least squares regression (PLSR) to test and compare the estimations of LAI and biomass in winter wheat, respectively. The results showed that LAI and biomass were highly correlated with several OSVIs (the enhanced vegetation index (EVI) and modified triangular vegetation index 2 (MTVI2)) and RPPs (the radar vegetation index (RVI) and double-bounce eigenvalue relative difference (DERD)). The product of MTVI2 and DERD (R2 = 0.67 and RMSE = 0.68, p < 0.01) and that of MTVI2 and RVI (R2 = 0. 68 and RMSE = 0.65, p < 0.01) were strongly related to LAI, and the product of the optimized soil adjusted vegetation index (OSAVI) and DERD (R2 = 0.79 and RMSE = 148.65 g/m2, p < 0.01) and that of EVI and RVI (R2 = 0. 80 and RMSE = 146.33 g/m2, p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data