Remotely sensed data - available at medium to high resolution across global spatial and temporal scales - are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing,...

Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics such as global carbon modeling, biogeochemical cycling, hydrological modeling, and ecosystem response mod-eling. However, the large amounts of imagery collected by the satellites are largely contaminated by the eeects of atmospheric particles through absorption and scattering of the...

Efficiently querying data collected from Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. In our previous works, we proposed adaptive techniques for learning models (e.g., statistical, nonparametric, etc.) from such data, considering the fact that LCSN data is typically geo-temporally skewed. In this paper, we present a demonstration of EnviroMeter. EnviroMe...

Classification of remotely sensed imagery gives variable and often poor quality results. The causes and nature of these errors have been the subject of extensive research.. There are two different components of accuracy in the context of remote sensing: positional and thematic accuracy. Positional accuracy determines how closely the position of discrete objects shown on a rectified image (map) ...

Boreal peatlands play a major role in carbon and water cycling and other global environmental processes but understanding this role is constrained by inconsistent representation of peatlands on, or omission from, many global land cover maps. The comparison of several widely used global and continental-scale databases on peat-5 land distribution with a detailed map for the St. Petersburg region ...

A generalized model characterizing most remotely sensed data pixel-level fusion techniques is very important for theoretical analysis and applications. This paper focuses on the establishment of a generalized model for most data fusion methods, which is helpful to quantitatively analyze and quickly implement different data fusion techniques. As an example, the PCA fusion method is selected to d...

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors b...

Fractal geometry is increasingly becoming a useful tool for modeling natural phenomenon. As an alternative to Euclidean concepts, fractals allow for a more accurate representation of the nature of complexity in natural boundaries and surfaces. Since they are characterized by self-similarity, an ideal fractal surface is scale-independent; i.e at different scales a fractal surface looks the same....

In an effort to support the development of context-aware applications that use archived sensor data, we introduce the concept of the Context Cube based on techniques of data warehousing and data mining. Our implementation of the Context Cube provides a system that supports a multi-dimensional model of context data and with tools for accessing, interpreting and aggregating that data by using con...

This paper presents a fully automated framework for algal bloom forecasting in inland water by combining remote sensing data series and unsupervised machine learning concepts. In contrast to other methods the specialized literature that usually employ pre-labeled data, proposed approach was designed be autonomous concerning pre-requisites, assuming as input only time of remotely sensed products...