Peatlands cover ~3% of the globe and are key ecosystems for climate regulation. To better understand the potential
effects of climate change in peatlands, a major challenge is to determine the complex relationship between hydrology,
microtopography, vegetation patterns, and gas exchange. Here we study the spectral and spatial relationship of
microtopographic features (e.g. hollows and hummocks) and near-surface water through narrow-band spectral indices
derived from hyperspectral imagery. We used a very high resolution digital elevation model (2.5 cm horizontal, 2.2 cm
vertical resolution) derived from an UAV based Structure from Motion photogrammetry to map hollows and hummocks
in the peatland area. We also created a 2 cm spatial resolution orthophoto mosaic to enhance the visual identification of
these hollows and hummocks. Furthermore, we collected SWIR airborne hyperspectral (880-2450 nm) imagery at 1 m
pixel resolution over four time periods, from April to June 2016 (phenological gradient: vegetation greening). Our results
revealed an increase in the water indices values (NDWI1640 and NDWI2130) and a decrease in the moisture stress index
(MSI) between April and June. In addition, for the same period the NDWI2130 shows a bimodal distribution indicating
potential to quantitatively assess moisture differences between mosses and vascular plants. Our results, using the digital
surface model to extract NDWI2130 values, showed significant differences between hollows and hummocks for each
time period, with higher moisture values for hollows (i.e. moss dominated). However, for June, the water index for
hummocks approximated the values found in hollows. Our study shows the advantages of using fine spatial and spectral
scales to detect temporal trends in near surface water in a peatland.
KEYWORDS: Databases, Data modeling, Remote sensing, Field spectroscopy, Data storage, Hyperspectral imaging, Calibration, Airborne remote sensing, Data acquisition, Data integration
In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.
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