A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

This review paper evaluates the potential of hyperspectral remote sensing for assessing species diversity in homogeneous (non-tropical) and heterogeneous (tropical) forest, an increasingly urgent task. Existing studies of species distribution patterns using hyperspectral remote sensing have used different techniques to discriminate different species, in which the wavelet transforms, derivative analysis and red edge positions are themost important of them. The wavelet transform is used based on its effectiveness and determined as the most powerful technique to identify species. Furthermore, estimations of relationships between spectral values and species distributions using chemical composition of foliage, tree phenology, selection of signature training sites based on field measured canopy composition, selection of the best wavelet coefficient and waveband regions may be useful to identify different plant species. This paper presents a summary on the feasibility, operational applications and possible strategies of hyperspectral remote sensing in forestry, especially in assessing its biodiversity. The paper also reviews the processing and analysis of techniques for hyperspectral data in discriminating different forest tree species

Influence of tree species complexity on discrimination performance of vegetation indices

Performance of different vegetation indices (VIs) in combination with single- and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%

Tracking elderly Alzheimer's patient using real time location system

Alzheimer Disease (AD) has major implications on patient safety and care. The elderly Alzheimer’s patient encounters risk of losing their memory capabilities and are unable to live a normal life accordingly. The short memory may lead them to wander aimlessly and danger. Hence, the Alzheimer’s patients need to be monitored closely to ensure their safety. In this paper, an assistive technology tool called Alzheimer’s Real Time Location System (ARTLS) was developed. The system tracks all the patients instantaneously in real time and helps in analyzing patient spatial movement for enhancing their care management. As a general result, ARTLS relieves the caregiver’s burden and enhances patient’s safety by close monitoring of the wandering movements of the patients in real time

Impact of discrete wavelet transform on discriminating airborne hyperspectral tropical rainforest tree species

Discriminating tropical rainforest tree species is still a challenging task due to a variety of species with high spectral similarity and due to very limited studies conducted in this area. We are investigating the effect of discrete wavelet transform (DWT) on enhancing discrimination of tropical rainforest tree species. For this purpose, airborne imaging spectrometer for applications (AISA) airborne hyperspectral data obtained from Malaysian’s rainforest area are used; six tree species were selected from the study area. For comparison purposes, the performance of DWT is compared with the original reflectance, first, and second derivative spectra by using five different spectral measure techniques. An overall discrimination accuracy of ∼74% is obtained with DWT using Euclidean distance, which outperforms the original reflectance and first and second derivatives by ∼16.6 , 11.9, and 22.1%, respectively. The results suggest a significant impact of the DWT approach on improving tropical rainforest tree species discrimination

Hyperspectral discrimination of tree species with different classifications using single- and multiple-endmember.

Discrimination of tree species with different ages is performed in three classifications using hyperspectral data. The first classification is between Broadleaves and pines; the second classification is between Broadleaves, Corsican Pines, and Scots Pines, and the third classification is between six tree species including different ages of Corsican and Scots Pines. These three classifications are performed by having single- and multiple-endmember and considering five different spectral measure techniques (SMTs) in combination with reflectance spectra (ReflS), first and second derivative spectra. The result shows that using single-endmember, derivative spectra are not useful for a more challenging classification. This is further emphasized in multiple-endmember classification, where all SMTs perform better in ReflS rather than derivative in all classifications. Furthermore, using derivative spectra, discrimination accuracy become more dependent on the type of SMTs, especially in single-endmember. By employing multiple-endmember, the within-species variation is significantly reduced, thereby, the remaining challenge in discriminating tree species with different ages is only due to the between-species similarity. Overall, discrimination accuracies around 92.4, 76.8, and 71.5% are obtained using original reflectance and multiple-endmember for the first, second, and third classification, which is around 14.3, 17, and 8.3% higher than what were obtained in single-endmember classifications, respectively. Also, amongst the five SMTs, Euclidean distance (in both single- and multiple-endmember) and Jeffreys–Matusita distance (in single-endmember and derivative spectra) provided the highest discrimination accuracies in different classifications. Furthermore, when discrimination become more challenging from the first to second and third classification, the performance difference between different SMTs is increased from 1.4 to 3.8 and 7.3%, respectively. The study shows high potential of multiple-endmember to be employed in remote sensing applications in the future for improving tree species discrimination accuracy