Abstract:The retrieval of nutrient concentration in sugarcane through hyperspectral remote sensing is widely known to be affected by canopy architecture. The goal of this research was to develop an estimation model that could explain the nitrogen variations in sugarcane with combined cultivars. Reflectance spectra were measured over the sugarcane canopy using a field spectroradiometer. The models were calibrated by a vegetation index and multiple linear regression. The original reflectance was transformed into a First-Derivative Spectrum (FDS) and two absorption features. The results indicated that the sensitive spectral wavelengths for quantifying nitrogen content existed mainly in the visible, red edge and far near-infrared regions of the electromagnetic spectrum. Normalized Differential Index (NDI) based on FDS (750/700) and Ratio Spectral Index (RVI) based on FDS (724/700) The strong correlation between measured and estimated nitrogen concentration indicated that the methods proposed in this study could be used for the reliable diagnosis of nitrogen quantity in sugarcane. Finally, the success of the field spectroscopy used for estimating the nutrient quality of sugarcane allowed an additional experiment using the polar orbiting hyperspectral data for the timely determination of crop nutrient status in rangelands without any requirement of prior cultivar information.

Abstract:This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC) using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR) and Support Vector Regression (SVR) were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS) and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF) kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE) value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management.

Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.

Papaya is an economically important plant in Thailand for domestic consumption and export. However, papaya is extremely susceptible to disease caused by the papaya ring spot virus. Although transgenic papaya has been developed, commercial cultivation of transgenic plants in Thailand is still illegal. One concern is cross-pollination to conventional varieties. In this study, windborne-pollen dispersion of papaya (Carica papaya L.) was investigated using geographic information systems (GIS) and remotely sensed data. Pollen traps were placed around a papaya plot in eight geographic directions, with radiuses varying from 5 to 900 m from the plot. Pollen counts were made for 12 different dates, and data were input into a GIS database. The distribution of pollen and its relation to land use were analyzed using land use data obtained from Quickbird imagery acquired during 2007. Comparative analyses of pollen dispersal, wind direction, and speed were made using data collected from a micro-climatic station set up at a papaya plot. The furthest distance from the plot that pollen was found was at 0.9 km, a distance at which only 1 pollen grain was found. The number of pollen grains carried by wind decreased as distance increased. The direction of dispersal was not in accordance with wind direction data. Most pollen grains were found in agricultural areas and bare land. The total number of pollen grains found in exposed areas was considerably higher than the total found in areas sheltered by dense tree lines.

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