QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Assessing across-scale optical diversity and productivity relationships in grasslands of the Italian alps

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation

Remote Sensing as a Precision Farming Tool in the Nile Valley, Egypt

Detecting stress in plants resulting from different stressors including nitrogen deficiency, salinity, moisture, contamination and diseases, is crucial in crop production. In the Nile Valley, crop production is hindered perhaps more fundamentally by issues of water supply and salinity. Predicting stress in crops by conventional methods is tedious, laborious and costly and is perhaps unreliable in providing a spatial context of stress patterns. Accurate and quick monitoring techniques for crop status to detect stress in crops at early growth stages are needed to maximize crop productivity. In this context, remotely sensed data may provide a useful tool in precision farming. This research aims to evaluate the role of in situ hyperspectral and high spatial resolution satellite remote sensing data to detect stress in wheat and maize crops and assess whether moisture induced stress can be distinguished from salinity induced stress spectrally. A series of five greenhouse based experiments on wheat and maize were undertaken subjecting both crops to a range of salinity and moisture stress levels. Spectroradiometry measurements were collected at different growth stages of each crop to assess the relationship between crop biophysical and biochemical properties and reflectance measurements from plant canopies. Additionally, high spatial resolution satellite images including two QuickBird, one ASTER and two SPOT HRV were acquired in south-west Alexandria, Egypt to assess the potential of high spectral and spatial resolution satellite imagery to detect stress in wheat and maize at local and regional scales. Two field work visits were conducted in Egypt to collect ground reference data and coupled with Hyperion imagery acquisition, during winter and summer seasons of 2007 in March (8-30: wheat) and July (12-17: maize). Despite efforts, Hyperion imagery was not acquired due to factors out with the control of this research. Strong significant correlations between crop properties and different vegetation indices derived from both ground based and satellite platforms were observed. RDVI showed a sensitive index to different wheat properties (r > 0.90 with different biophysical properties). In maize, GNDVIbr and Cgreen had strong significant correlations with maize biophysical properties (r > 0.80). PCA showed the possibility to distinguish between moisture and salinity induced stress at the grain filling stages. The results further showed that a combined approach of high (2-5 m) and moderate (15-20) spatial resolution satellite imagery can provide a better mechanistic interpretation of the distribution and sources of stress, despite the typical small size of fields (20-50 m scale). QuickBird imagery successfully detects stress within field and local scales, whereas SPOT HRV imagery is useful in detecting stress at a regional scale, and therefore, can be a robust tool in identifying issues of crop management at a regional scale. Due to the limited spectral capabilities of high spatial resolution images, distinguishing different sources of stress is not directly possible, and therefore, hyperspectral satellite imagery (e.g. Hyperion or HyspIRI) is required to distinguish between moisture and salinity induced stress. It is evident from the results that remotely sensed data acquired by both in situ hyperspectral and high spatial resolution satellite remote sensing can be used as a useful tool in precision farming in the Nile Valley, Egypt. A combined approach of using reliable high spatial and spectral satellite remote sensing data could provide better insight about stress at local and regional scales. Using this technique as a precision farming and management tool will lead to improved crop productivity by limiting stress and consequently provide a valuable tool in combating issues of food supply at a time of rapid population growth

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe

A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl \u3e 40 mg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI

Evaluation of the UAV-Based Multispectral Imagery and Its Application for Crop Intra-Field Nitrogen Monitoring and Yield Prediction in Ontario

Unmanned Aerial Vehicle (UAV) has the capability of acquiring high spatial and temporal resolution images. This new technology fills the data gap between satellite and ground survey in agriculture. In addition, UAV-based crop monitoring and methods are new challenge of remote sensing application in agriculture. First, in my thesis the potential of UAV-based imagery was investigated to monitor spatial and temporal variation of crop status in comparison with RapidEye. The correlation between red-edge indices and LAI and biomass are higher for UAV-based imagery than that of RapidEye. Secondly, the nitrogen weight and yield in wheat was predicted using the UAV-based imagery. The intra-field nitrogen prediction model performs well at wheat early growth stage. Additionally, the best data collection time for yield prediction is at the end of booting stage. The results demonstrate the UAV-based data could be an alternative effective and affordable approach for farmers on intra-field management