The potential for using remote sensing to quantify stress in and predict yield of sugarcane (Saccharum spp. hybrid)

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010

PRISMA and Sentinel-2 spectral response to the nutrient composition of grains

Micronutrient malnutrition is a global challenge affecting &gt;2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.</p

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

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species Eucalyptus forests

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia

Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400–720 nm and 560–710 nm) regions, and positively correlated (r &gt; 0.50, r &gt; 0.60) with red and NIR (720–900 nm) regions. These sensitive regions are used to formulate the new (SR777/759, SR768/750) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period

Detecting Plant Functional Traits of Grassland Vegetation Using Spectral Reflectance Measurements

Changes in climate and an intensified agricultural use threaten grassland ecosystems in many places. To allow an efficient conservation of grassland vegetation communities, ecologists monitor variations in their plant functional traits (FTs). FTs are morphological, physiological or phenological properties of plants, which are measured at the individual plant level. However, manual measurements of FTs are costly as well as time-consuming and often require destructive sampling techniques. Grassland ecologists and agronomists are thus seeking for novel methods to monitor and map grassland FTs. Remote sensing (RS) may provide a solution to the mentioned problems and allows to collect spatially contiguous and multitemporal information on FTs. To test the performance of RS systems for detecting FTs, the Rengen Grassland Experiment in Germany was selected as study site. Due to more than 70 years of constant fertilization along a gradient from limed only to fully fertilized (treated with lime, nitrogen, phosphorus and potassium), five different plant communities have developed, which differ in their FTs. The spectral reflectance of these plant communities was collected for a period of three years using an ASD Field Spec 3 (FS3) spectroradiometer. Furthermore, 23 different FTs were measured using manual sampling methods. Firstly, it was investigated if and how the five grassland communities can be distinguished using 15 different remotely sensed vegetation indices (VIs). It was found that the performance of single VIs for differentiating the studied plant canopies fluctuates over time. Consequently, it was not possible to distinguish the communities with high accuracy throughout all phases of their phenological development using one VI. To solve this problem, a multi-VI approach using the random forests algorithm is proposed, which automatically selects the ideal sets of VIs for distinguishing grasslands. This technique allows a stable and accurate classification of grassland communities for the entire growing season. Secondly, it was studied how well the FTs of the different grassland communities can be estimated based on FS3 data. Using partial least squares regression (PLSR) it was possible to create one single model for estimating one FT of all studied grassland canopies at all phenological stages based on the spectral reflectance. Among the 23 investigated FTs, nine were modelled with R squared in validation (R2val) larger than 0.6, four with R2val larger than 0.4 and 10 with R2val lower than 0.4. It is concluded that RS allows a cost-efficient, time-saving and non-destructive monitoring of many FTs for a range of plant communities. Thirdly, the potential of different RS systems for detecting FTs was assessed. Based on spectral reflectance data recorded with a full-range FS 3, the bands of two hyperspectral and three multispectral RS sensors were simulated. Using PLSR and hyperspectral RS, 13 FTs were modeled with R2val larger than 0.4 using FS 3, 11 using EnMAP and ten using ASD HandHeld 2 data. Based on multispectral information, R2val larger than 0.4 were reached with Sentinel-2 for nine, Landsat 7 for four and RapidEye for none of the 23 FTs. These results show that hyperspectral RS systems outperform multispectral systems in detecting the FTs of grassland vegetation. It is concluded that hyperspectral RS systems have the potential to collect spatio-temporal information on grassland FTs. Such information may support grassland scientists in adapting the management to changes in climate and land-use intensity and to secure a sustainable agricultural production

Assessment of foliar nitrogen as an indicator of vegetation stress using remote sensing : the case study of Waterberg region, Limpopo Province

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques.Environmental SciencesM. Sc. (Environmental Management

Remote sensing of vegetation characteristics and spatial analysis of pyric herbivory in a tallgrass prairie

Doctor of PhilosophyDepartment of GeographyDouglas GoodinQuantitative remote sensing provides an effective way of estimating and mapping vegetation characteristics over an extensive area. The spatially explicit distribution of canopy vegetative properties from remote sensing imagery can be further used for studies of spatial patterns and processes in grassland systems. My research focused on remote sensing of grassland vegetation characteristics and its applications to spatial analysis of grassland dynamics involving interactions between pyric herbivory and vegetation heterogeneity. In remote sensing of vegetation characteristics, (1) I estimated the foliar pigments and nutritional elements at the leaf level using hyperspectral data. The foliar pigments, chlorophylls and carotenoids, were retrieved by inverting the physical radiative transfer model, PROSPECT. The nutritional elements were modeled empirically using partial least squares (PLS) regression. Correlations were found between the leaf pigments and nutritional elements. This provided insight into the use of pigment-related vegetation indices as a proxy of the plant nutritional quality. (2) At the canopy level, I assessed the use of the broadband vegetation indices, normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI), in detecting vegetation quantity (LAI) and quality (leaf and canopy chlorophyll concentrations). The relationships between vegetation indices and vegetation characteristics were examined in the physical model, PROSAIL, and validated by a field dataset collected from a tallgrass prairie. NDVI showed high correlations with LAI and canopy chlorophylls. GRVI performed even better than NDVI in estimating LAI. A new index GNVI (green-red normalized vegetation index) that combined NDVI and GRVI was proposed to extract leaf chlorophyll concentration. These findings showed the potential of using broadband vegetation indices from multispectral remote sensors to monitor vegetation quantity and quality over a wide spatial extent. In the spatial analysis, I examined interactions between pyric herbivory and grassland heterogeneity at multiple scales from the remote sensing imagery. (3) At a coarse, watershed level, I evaluated effects of fire and large herbivores on the spatial distribution of canopy nitrogen. It was found that the interactive effects of fire and ungulate grazing were present in the watersheds burnt in spring, where a high level of ungulate grazing reduced vegetation density, but promoted canopy heterogeneity. Two grazer species, bison and cattle, were compared. Differences in the vegetation canopy between sites with bison and cattle were observed, which may be related to differences in the grazing intensity, forage behavior and habitat selection between the two grazer species. (4) At a fine, patch level (30 m), bison forage pattern was examined associated with canopy nitrogen heterogeneity. Bison preference for patches with high canopy nitrogen was evident in May. Later in June – September, bison tended to avoid sites with high canopy nitrogen. Vegetation heterogeneity showed significant influences on bison habitat selection in June. Bison preferred sites with low variance in canopy nitrogen, where the patch types were highly aggregated and equitably proportioned

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

An investigation into the detection of sugarcane African stalk borer (Eldana saccharina Walker (Lepidoptera : Pyralidae)) using hyperspectral data (spectroradiometry).

Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2009.The South African Sugarcane production is one of the world’s leading sugarcane (Saccharum spp. Hybrid) producers. However, in recent years Eldana saccharina Walker has been the most destructive pest in South African sugarcane production, causing great crop loses per annum and is the most important factor limiting sugarcane productivity. The pest has been monitored using a traditional visual approach whereby a representative sample of stalks is taken from a field and split longitudinally to assess damage and count the number of E. saccharina larvae and pupae. However, this approach is time-consuming, labour intensive and sometimes biased as only easily accessible areas are often surveyed. In order to investigate a more economical but equally effective survey methodology, this study aimed to determine the potential of using hyperspectral remote sensing (spectroradiometry) for identifying sugarcane attacked by E. saccharina. A hand-held spectroradiometer ASD Field Spec® 3 was used to collect leaf spectral measurements of sugarcane plants from a potted-plant trial taking place under shade house conditions at the South African Sugarcane Research Institute (SASRI). In this trial, nitrogen (N) and silicon (Si) fertilizers were applied at known levels to sugarcane varieties. Varieties were either resistant or intermediate resistant or susceptible to E. saccharina attack. In addition, watering regimes and artificial infestation of E. saccharina were carefully controlled. Results illustrated that severe E. saccharina infestation increased spectral reflectance throughout the whole spectrum range (400 – 2500 nm) and caused a red-edge shift to the shorter wavelength. Eldana saccharina stalk damage was also linearly related to modified normalized difference vegetation index (mNDVI) using R2025 and R2200 (R2 = 0.69). It was concluded that hyperspectral data has a potential for use in monitoring E. saccharina in sugarcane rapidly and non-destructively under controlled conditions. A followup study is recommended in field conditions and using airborne and/or spaceborne hyperspectral sensors