Trees used in agroforestry practices, such as windbreaks, provide a variety of ecosystem benefits and are recognized globally as an important land use. However, efforts to inventory and monitor agroforestry land use have been sporadic, short-lived, or focused on small spatial extents. There are a variety of satellite-derived datasets that provide information about tree cover over broad spatial ...

We present an application of our framework for 3-D object-centered change detection1–4 to combined satellite and aerial imagery . In this framework, geometry is compared to geometry, allowing us to compare image sets with different acquisition conditions and even different sensors. By working in this framework, we do not encounter the restrictions and short-comings of conventional image-based c...

This paper presents a practical method of epipolar resampling of high-resolution satellite imagery. Satellite imagery imaged with a linear array CCD sensor has quite different geometric characteristics from aerial photographs, therefore the conventional method of epipolar resampling is not applicable to it. On the other hand, epipolar resampling method based on rigorous orientation model with h...

The tidal wetlands within the Long Island Sound estuary serve a critical role in maintaining the health of the Sound. Over the past two centuries, there has been significant disturbance and loss of tidal wetlands along the Sound due primarily to anthropogenic activities. Researchers at the University of Connecticut and Wesleyan University are continuing on the second year of a two year project ...

Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown t...

Spectral similarity and spatial adjacency between various kinds of objects, shadow and occluded areas behind high rise objects as well as complex relationships lead to object recognition difficulties and ambiguities in complex urban areas. Using new multiangular satellite imagery, higher levels of analysis and developing a context aware system may improve object recognition results in these sit...

Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most ...

Thermal infrared (TIR) remote sensing of landsurface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstra...

Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing ...