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Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production. peerReviewed

This study proposed the development plan of a novel aerial-to-ground remote sensing (AGRS) system for surveying the land scenes of interest. Specifically, the AGRS system is composed by integrating an unmanned aerial vehicle (UAV) imaging system and a mobile mapping system (MMS), onboard whose platform a control station is also added. The UAV-MMS-collaboration can be classified into two modes - loosely and tightly, respectively related to two efficacy levels of the AGRS - fine-scale mapping in general and target investigating in special cases. The latter scenario can be illustrated by the tasks of fast-responses to the time-critical events, e.g., seeking the accessible roads into disaster areas. These all pose challenging issues. To ensure the premise for AGRS development, a field test was carried out in prior to examine the collaborative effect between its two RS-functional modules. Two typical topics were explored, i.e., self-indicated orthorectification of the UAV images and landcover classification based on information fusion. The final positive results have basically validated the feasibility of the development of the AGRS system.

This paper focuses on the use of multispectral measurements to classify remotely sensed radiance and reflectance information into three tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and birch (Betula pubescens Ehrh., Betula pendula Roth), using a Support Vector Machine (SVM) algorithm. The features used for the classifier are radiometric involving different viewing angles, but without textural information. At-sensor radiance (ASR) signals used here were obtained using a four-band Leica ADS40-SH52 airborne sensor. The experiments were carried out in a forest area at Hyytiala, in southern Finland (61°50' N, 24°20' E), which has been widely used for similar purposes, so that detailed tree-level information has been reported previously. The flight was carried out on August 23, 2008. ADS40 ASR measurements can be converted to ground reflectance signatures in two viewing directions using atmosphere and BRDF modeling implemented in Leica XPro 4.2 software. Taking into account the assumptions entailed in the radiometric model, the classification performance of the ground reflectance is evaluated only for the pixel values under sunlit conditions and is compared with the performance of the ASR data. The sunlit and shaded parts of the tree crown were extracted based on the use of LiDAR data for crown shape modeling. The classification results for the real multispectral measurements are compared with the earlier results obtained with simulated Leica ADS40 at-sensor radiance response values which were based on the ground-level high-resolution ground reflectance factor measurements using a single viewing direction. The simulated classification accuracy was 75-79% with the original four bands, while it was up to 85-88%, using the simulated fifth channel. It was found here that the classification accuracy using comparable real ADS40-SH52 four-band data and one viewing angle was 75-79% and increased to 78-82% with two viewing angles. The results show that the best-case classification accuracy with real data can reach 88% if trees are modeled as objects with sunlit and shaded areas, and multiple measurements are available for every tree. The results suggest that ground reflectance estimation with normalization of anisotropic reflectance behavior leads to similar classification performance to ASR data, but can in some cases improve the generalization properties of training data.