The urban thermal environment significantly influences regional climate change, urban resilience, and sustainability. Although traditional remote sensing technologies are common tools for urban thermal environments observation, it has limitations including low spatial resolution, long revisit cycles, and cloud cover. In responding to these limitations, a low-altitude dual-source remote sensing platform (LADSRSP) was researched and developed in this study through a series of tests. First, a thermal infrared sensor and a hyperspectral sensor were simultaneously equipped on a multi-rotor drone via a customized three-axis gimbal in LADSRSP. Furthermore, LADSRSP was optimized based on the stability test results, and was then confirmed to have the ability of collecting data with high spatial resolution and quality through the data acquisition tests. Last, the performance of LADSRSP was assessed by data integrity, classification accuracy, and temperature analysis. The image classification results demonstrate high accuracy with an overall classification accuracy of 97.92% and a Kappa coefficient of 0.97 from collected data quality analysis tests. The temperature values of each urban underlying surface were then accurately extracted to facilitate further statistical analysis. Overall, the LADSRSP was proved to be a feasible, efficient, and accurate tool for urban thermal environment research.

Currently, unmanned aerial vehicle hyperspectral images (UAV-HSIs) lack quick, objective, and comprehensive image quality assessment methods. Therefore, a multi-feature-based fuzzy comprehensive evaluation method was proposed in this letter to comprehensively evaluate UAV-HSI quality. To characterize the hyperspectral quality comprehensively, we selected four radiometric features, three spatial features, and two spectral features to construct an indicator set. After analyzing the statistical distribution of the above features of the 23 UAV-HSIs, a fuzzy evaluation threshold table and membership functions were established. To determine the optimal feature weights, the weights obtained using the three methods were tested on a test set of UAV-HSIs constructed with different degrees of noise and blur. The test results showed that the combined weight based on a combination of subjective and objective weight is more robust. The experiment for comprehensive quality assessment of different distortion types and flight heights was done with UAV-HSIs. The results indicated that the comprehensive quality score was in good agreement with the subjective assessment and the objective fact. This comprehensive quality evaluation method can be effectively used for blurred, noisy, overexposed, and different-height UAV-HSIs.

Accurate mapping of urban land cover is still a fundamental challenge in remote sensing communities due to the great spectral variability of urban environments. This study presents an application of multiple criteria spectral mixture analysis (MCSMA) approach to map vegetation, impervious surfaces, and soil (V-I-S) components in a highly urbanized city of Chengdu, China, using the Landsat-8 Operational Land Imager (OLI) surface reflectance product. Unlike its counterparts which rely on single indicator in the mapping process, MCSMA uses multiple indicators to better address the problem of spectral variability. Our results showed that MCSMA produced accurate V-I-S maps that well matched the actual distributions. The vegetation map presented higher accuracies than impervious surfaces and soil maps in root mean square error, mean absolute error and systematic error. Results of this study demonstrate the potential of MCSMA in accurate urban land cover mapping.

Remote environmental monitoring systems require effective energy management to allow reliable long-term operation without frequent maintenance to replace or recharge batteries. To design and analyze relevant energy management strategies, we have developed Simulink-based models of a recently constructed monitoring device to evaluate its potential performance. The model uses long-term solar energy data from two locations, Chamela, Mexico, and Fairview, Canada, to estimate the energy harvesting capabilities of the device. Using the simulator, we have developed and evaluated a fuzzy energy management strategy that determines how the device should operate to match the solar energy profile in each location. Solar energy in Chamela, Mexico is abundant and consistent so an energy harvesting remote monitoring device could have a high activity level without risking device failure. Fairview, Canada, has limited solar resources in the winter but plenty in the summer; a device dependent upon this energy source must adapt its activity level to match energy availability or risk running out of energy. While the simulated device in Mexico outperforms the one in Canada, both succeed in matching the available environmental resources and largely avoid energy related device failure. In the future, their performance can be improved by optimizing the designed strategies and further improving the details of the simulation.

While indoor wireless sensor network (WSN) research has recently flourished for monitoring civil and industrial infrastructure, considerably less attention has been given to the development of reliable outdoor WSNs capable of long-term operation in challenging remote locations. We present wireless sensor network link performance results from the first year of monitoring micro-meteorological conditions alongside the 802.15.4 link received signal strength indicator (RSSI) within an old growth stand of deciduous boreal Aspen forest (Populus tremuloides) in Northern Alberta, Canada. Thirty-six weather proof nodes were equipped with meteorological sensors and distributed across one hectare in the forest understory to assess the application of WSNs for observing high resolution changes in seasonal ecosystem productivity and forest phenology. We describe here the density distribution of node RSSI using Gaussian kernel density estimates in relation to node antenna-receiver orientation and vegetation seasonality. RSSI across the network displays a lognormal distribution with an increasing bimodal tendency with path length through the forest stand. Spatial variability in RSSI is discussed with respect to forest structure. A strong temporal relationship between RSSI variability and plant canopy development is observed with a 20dBm or 100 fold difference in mean network radio signal power from spring leaf presence to fall leaf absence. The meteorological and biophysical factors associated with this trend are explored using multiple regression and relative factor importance analysis. Our results indicate that in addition to meteorological data, spectral vegetation density metrics are useful in assisting deployment planning and network performance diagnostics when using wireless sensor networks for remote forestry applications. The longevity and performance of this outdoor WSN can be seen as a new standard for harsh network-climate tolerance in northern boreal environments.

In the present paper, we evaluate the effect of cast shadows in the classification of three successional stages of tropical forest in Mexico using hyperspectral and multiangular Chris/Proba images. A simple algorithm based on the cosine of the angle of solar incidence on the terrain is applied to correct the effect of topography on Chris/Proba's reflectances. Previous to the correction of topographic effects, Chris/Proba images were atmospherically corrected in BEAM software. Vegetation maps of the study site were generated using non-parametric decision trees, defining four mam classes: late, intermediate and early stages of succession within a tropical dry forest, and riparian forests. By the comparison of resultant vegetation maps before and after the correction of topography in Chris/Proba's spectral data, we found that late stage of succession and riparian forests are overestimated in non-corrected images while intermediate and early stages of succession are underestimated. Errors in classification are more important in extreme Chris/Proba's angles of observation. Thus, the radiometric correction of topography is necessary for an accurate classification of succession in tropical dry forests.

Seasonally Dry Tropical Forest (SDTF) ecosystems are of great interest to earth observation sciences due to their strong influence over land-atmosphere exchange of carbon and water in semi-arid environments. Current space and airborne earth observation remote sensing platforms provide coarse temporal resolution and low signal to noise ratios in SDTF canopy phenology time series. In order to obtain hyper-temporal phenology information we present here a ground-based optical wireless sensor network (WSN) to monitor in-situ the seasonality of the forest canopy in a tropical dry forest reserve located in the Sao Francisco river basin in southeastern Brazil. In 2007 a network of hemispherical photosynthetically active radiation (PAR) sensors was deployed in the understory of a mature secondary SDTF to capture intercepted light at sub-hourly intervals and wirelessly transmit data to a central data aggregator. The resulting time series provides detailed information on the vegetative phenology, spatial variation of intercepted PAR (FIPAR), and growing season parameters.

The emergence of new environmental monitoring tools utilizing Wireless Sensor Networks (WSNs) represents new opportunities to understand locally driven ecological processes in tropical environments at larger spatial and temporal scales, while at the same time opens new opportunities for eScience research. In this paper we present results from the outcome of two WSNs deployments aimed to evaluate this new technology in tropical environments. Leaf temperature and Photosynthetically Active Radiation (PAR) were measured at high temporal resolution in Panama and Brazil, respectively. Our results indicate that WSN technologies can be used effectively enough to measure important micro-meteorological variables that are sensitive to climate change and land use/cover change. Some of the temperatures recorded during our experiments in Panama were significantly high than those suggested as critical environmental thresholds for tropical environments. Our PAR results from Brazil showcase the value of this technology to evaluate the role of light patterns on ecosystem succession as a result of regional land use/cover change process. Our experiments indicate the WSN can serve as a key element in future understandings of ecosystem process driven by eScience in the years to come.

Leaf water content as a percentage of dry mass (LWCD), also termed fuel moisture content, is a critical variable for fire risk modeling. To date, the relationship between spectral reflectance and LWCDis poorly understood due to the mixed effect of leaf water and dry mass on spectral response. Few studies have reported an adequate estimation of LWCDfrom leaf reflectance spectra, in particular for a variety of species. In this study, we propose an effective approach, using continuous wavelet analysis, to determine LWCDfrom reflectance spectra for 47 tropical forest species in Panama. The proposed method provides a new way to select a small number of wavelet features (wavelet coefficients) that are sensitive to changes in LWCD. Wavelet features derived using this new method are compared with common spectral indices in terms of their relationship with LWCD. Results demonstrate that LWCDis poorly correlated to the water indices but strongly correlated to the derived wavelet features. Accurate estimates of LWCDacross a range of species are achieved using any of those wavelet features and can be slightly improved using a combination of them.

In this paper, the optimizations of three fundamental components of image understanding: segmentation/annotation, 3D sensing (stereo) and 3D fitting, are posed and integrated within a Bayesian framework. This approach benefits from recent advances in statistical learning which have resulted in greatly improved flexibility and robustness. The first two components produce annotation (region labeling) and depth maps for the input images, while the third module integrates and resolves the inconsistencies between region labels and depth maps to fit most likely 3D models. To illustrate the application of these ideas, we have focused on the difficult problem of fitting individual tree models to tree stands which is a major challenge for vision-based forestry inventory systems.

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks.

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every endmember can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping.