/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

Polarimetric SAR interferometry

PA—Precision Agriculture: Remote-Sensing and Mapping of Weeds in Crops

Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Despite the established importance of mangroves to the coastal enviro...

Satellite remote sensing of mangrove forests: Recent advances and future opportunities:

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel-and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995-2005. For single date thematic maps of 10 LULC classes, quantitative and visual analyses showed no significant accuracy difference between the two methods. The object-based method produced thematic maps with more uniform and meaningful LULC objects, but it suffered from absorption of small rare classes into larger objects and the incapability of spatial parameters (e.g. object shape) to contribute to class discrimination. Despite the similar map accuracies produced by the two methods, temporal change maps produced using post-classification comparison (PCC) and analysed using intensive visual analysis of errors of omission and commission revealed that the object-based maps depicted change more accurately than maximum likelihood classification (MLC)-derived change maps.

Comparison of pixel-and object-based classification in land cover change mapping

Sugar maple decline assessment based on spectral and textural analysis of multispectral aerial videography

https://www.glel.carleton.ca/PEOPLE/PROFILES/JonPasher/Pasher%20et%20al%202007.pdf

Modelling and mapping potential hooded warbler (Wilsonia citrina) habitat using remotely sensed imagery

The potential for forest change monitoring in the state of Karnataka, India using Landsat imagery was evaluated. Imagery from 1986 and 2003 was analyzed using two change detection techniques: (1) image differencing of the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the Kauth-Thomas greenness index (KT-G), and (2) post-classification comparison (PCC). As field validation data did not exist for 1986, extensive visual assessment was conducted to locate and identify errors of commission and omission in the change maps. The image difference vegetation maps did not display obvious errors of omission, but the NDVI difference performed better than KT-G and PC2 differences in terms of errors of commission. It was therefore classified into a deforestation/reforestation map and evaluated against the PCC forest change map. PCC was able to more accurately detect changes over the 17-year period. Analysis of the literature and the forest change maps showed that deforestation was primarily a result of submergence by reservoirs created in hydroelectric developments, whereas reforestation was mainly due to significant increases in forest plantations, as a result of various social forestry projects.

https://www.glel.carleton.ca/PDF/geoPub/gc06.pdf

Comparison of Techniques for Forest Change Mapping Using Landsat Data in Karnataka, India

Forest canopy height is an important parameter that is strongly correlated with timber volume, biomass, carbon stocks and ecological values such as habitat quality. This parameter is critical to the terrestrial carbon cycle and biomass models and in forest inventory. Remote sensing technologies such as interferometric synthetic aperture radar (InSAR) and polarimetric InSAR (PolInSAR) provide a means to extract canopy height remotely and over large areas. Single-pass X-HH/C-HH InSAR and single-pass L-PolInSAR derived canopy height over Lodgepole pine forest vegetation in Edson, Alberta, Canada are compared to LiDAR derived canopy height. Canopy height of the X-HH and C-HH given by the scattering phase center height (

https://earth.esa.int/workshops/polinsar2009/participants/470/paper_470_p4_5tigh.pdf

Comparison of x/c-hh insar and l-polinsar for canopy height estimation in a lodgepole pine forest

Increased rates of permafrost degradation and erosion along Arctic coastlines are anticipated, as a result of changing climatic conditions. To characterize and quantify rates of change, baselines in terms of the land cover types present and the physical location of the land-water interface, need to be established. Earth observation data provides a logistical alternative to conventional mapping approaches, while Synthetic Aperture Radar (SAR) systems are particularly well suited to this application since images can be acquired through cloud cover and in the low lighting conditions observed during Arctic winters. The focus of this study will be to assess the potential of dual pol TerraSAR-X and quad-pol Radarsat-2 imagery for land cover mapping, using complimentary datasets acquired over two study areas in the southern Beaufort Sea, Northwest Territories, Canada. Novel polarimetric SAR processing techniques, including the extraction of Kennaugh matrix elements, have been used to generate inputs to the Maximum Likelihood Classifier. Results show good potential for general land cover mapping, and that overall accuracy increases when backscatter coefficients from both sensors are utilized (81.4% for eight classes), compared to TerraSAR-X HH/VV (68.9%) or Radarsat-2 HH/HV/VV (73.6%) alone. Similar accuracies were achieved using Kennaugh matrix elements, but slight decreases were observed when backscatter coefficients and Kennaugh matrix elements were combined.

Classification of Arctic Coastal land covers with polarimetric SAR data

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