New Hyperspectral Procedure to Discriminate Intertidal Macroalgae

The recent development and miniaturization of hyperspectral sensors embedded in drones has allowed the acquisition of hyperspectral images with high spectral and spatial resolution. The characteristics of both the embedded sensors and drones (viewing angle, flying altitude, resolution) create opportunities to consider the use of hyperspectral imagery to map and monitor macroalgae communities. In general, the overflight of the areas to be mapped is conconmittently associated accompanied with measurements carried out in the field to acquire the spectra of previously identified objects. An alternative to these simultaneous acquisitions is to use a hyperspectral library made up of pure spectra of the different species in place, that would spare field acquisition of spectra during each flight. However, the use of such a technique requires developed appropriate procedure for testing the level of species classification that can be achieved, as well as the reproducibility of the classification over time. This study presents a novel classification approach based on the use of reflectance spectra of macroalgae acquired in controlled conditions. This overall approach developed is based on both the use of the spectral angle mapper (SAM) algorithm applied on first derivative hyperspectral data. The efficiency of this approach has been tested on a hyperspectral library composed of 16 macroalgae species, and its temporal reproducibility has been tested on a monthly survey of the spectral response of different macro-algae species. In addition, the classification results obtained with this new approach were also compared to the results obtained through the use of the most recent and robust procedure published. The classification obtained shows that the developed approach allows to perfectly discriminate the different phyla, whatever the period. At the species level, the classification approach is less effective when the individuals studied belong to phylogenetically close species (i.e., Fucus spiralis and Fucus serratus).

Rice growth phase analysis in Pidie regency, Indonesia using multitemporal Sentinel-2 image data: a spectral angle mapper approach

Remote sensing data provides fast and relatively accurate information to retrieve the plant growth phase using spectral analysis. Spectral analysis of plants is the critical point of identifying the stages of rice growth using Sentinel-2 data. Sentinel-2 satellite images were utilized for this study. This study aims to analyze the growth phase of rice in Pidie regency, Aceh Province, Indonesia, as a sample area of the rice-growing site. The Spectral Angle Mapper (SAM) approach was performed to describe the plant growth stages. The results show variations in the rice growth phase across the study area for 2019, 2020, and 2021 growing seasons from vegetative, generative, wet fallow, and dry fallow. The most extensive vegetative phase is for April 2021 data, counting for 1,278.16 Ha. The most extensive generative phase was identified of June 2020 data, counting for 1,107.55 Ha. For wet fallow, counting for 949,30 Ha is the largest in this category. A total of 1,311.94 Ha of dry fallow is identified in 2019. The different growth phases and the total area for different years indicate variation in starting for the growing season of the sample location. In this paper, multitemporal Sentinel-2 data analyzed with the SAM approach has demonstrated identifying rice-growing season phases. This finding can help predict the total area along the year for a change of the pattern of the rice-growing season in the last three years of the study area.

Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

AVALIAÇÃO DA ACURÁCIA DE ALGORITMOS DE CLASSIFICAÇÃO DE IMAGENS ORBITAIS NA BAÍA DA BABITONGA, NORDESTE DE SANTA CATARINA

O objetivo deste trabalho foi a avaliação da acurácia de algoritmos de classificação do uso e cobertura do solo, quando aplicados a uma imagem orbital de média resolução espacial. Para esse estudo foram utilizadas as bandas espectrais da faixa do visível e infravermelho próximo, do sensor Operational Land Imager – OLI na Baía da Babitonga/SC. Foram propostas nove classes de cobertura do solo, que serviram como controle para testar 11 algoritmos classificadores: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper e Spectral Information Divergence. O classificador Maximum Likelihood foi o que apresentou o melhor desempenho, obtendo um índice Kappa de 0,89 e acurácia global de 95,5%, sendo capaz de distinguir as nove classes de cobertura do solo propostas. Evaluation of the Accuracy of Orbital Image Classification Algorithms in Babitonga Bay, northeast of Santa Catarina A B S T R A C TThe objective of this work was to evaluate the classification algorithms accuracy of the soil use and cover when applied to a spatial mean orbital image. For this study we used the visible and near infrared spectral bands of the Operational Land Imager - OLI sensor in Babitonga Bay / SC. Nine classes of soil cover were proposed, which served as control to test 11 classifier algorithms: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper and Spectral Information Divergence. The Maximum Likelihood classifier presented the best performance, obtaining a Kappa index of 0.89 and a global accuracy of 95.5%, being able to distinguish the nine proposed classes of soil cover.Keywords: Algorithms Accuracy, Babitonga Bay, Orbital image, Remote sensing, Soil Use and Cover.

LAND COVER CLASSIFICATION PERFORMANCE OF MULTISPECTRAL RTK UAVs

Unmanned air vehicle (UAV) became an alternative airborne remote sensing technique, due to providing very high resolution and low cost spatial data and short processing time. Particularly, optical UAVs are frequently utilized in various applications such as mapping, agriculture, and forestry. Especially for precise agriculture purposes, the UAVs were equipped with multispectral cameras which enables to classify land cover easily. In this study, the land cover classification potential of DJI Phantom IV Multispectral, one of the most preferred agricultural UAVs in the world, was investigated using spectral angle mapper, minimum distance and maximum likelihood pixel-based classification techniques and object-based classification. In the investigation, a part of Gebze Technical University (GTU) Northern Campus, includes a large variety of land cover classes, was selected as the study area. The UAV aerial photos were achieved from 70 m flight altitude and processed using structure from motion (SfM)-based image matching software Agisoft Metashape. The pixel-based and object-based land cover classification processes were completed with ENVI and eCognition software respectively. 16 independent land cover classes were classified and the results demonstrated that the accuracies are 73.46% in spectral angle mapper, 75.27% in minimum distance and 93.56% in maximum likelihood pixel-based classification techniques and 90.09% in nearest neighbour object-based classification.

Semi-Automatic Extraction and Mapping of Farmlands based on High-Resolution Remote Sensing Images

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

According to the Intergovernmental Panel on Climate Change, the Horn of Africa is getting drier. This research aims at assessing browning and/or greening dynamics and the suitability of Sentinel-2 satellite images to map changes in land cover in a semiarid area. Vegetation dynamics are assessed through a remote sensing approach based on densely vegetated areas in a pilot area of North Horr Sub-County, in northern Kenya, between 2016–2020. Four spectral vegetation indices are calculated from Sentinel-2 images to create annual multi-temporal images. Two different supervised classification methods—Minimum Distance and Spectral Angle Mapper—are then applied in order to identify dense vegetated areas. A general greening is found to have occurred in this period with the exception of the year 2020, with an average annual percentage increase of 19%. Results also highlight a latency between climatic conditions and vegetation growth. This approach is for the first time applied in North Horr Sub-County and supports local decision-making processes for sustainable land management strategies.

Easily Implemented Methods of Radiometric Corrections for Hyperspectral–UAV—Application to Guianese Equatorial Mudbanks Colonized by Pioneer Mangroves

Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations) is a custom, mini-UAV (unmanned aerial vehicle) platform (<20 kg), equipped with a light push broom hyperspectral sensor combined with a navigation module measuring position and orientation. Because of the particularities of UAV surveys (low flight altitude, small spatial scale, and high resolution), dedicated pre-processing methods have to be developed when reconstructing hyperspectral imagery. This article presents light, easy-implementation, in situ methods, using only two Spectralon® and a field spectrometer, allowing performance of an initial calibration of the sensor in order to correct “vignetting effects” and a field standardization to convert digital numbers (DN) collected by the hyperspectral camera to reflectance, taking into account the time-varying illumination conditions. Radiometric corrections are applied to a subset of a dataset collected above mudflats colonized by pioneer mangroves in French Guiana. The efficiency of the radiometric corrections is assessed by comparing spectra from Hyper-DRELIO imagery to in situ spectrometer measurements above the intertidal benthic biofilm and mangroves. The shapes of the spectra were consistent, and the spectral angle mapper (SAM) distance was 0.039 above the benthic biofilm and 0.159 above the mangroves. These preliminary results provide new perspectives for quantifying and mapping the benthic biofilm and mangroves at the scale of the Guianese intertidal mudbanks system, given their importance in the coastal food webs, biogeochemical cycles, and the sediment stabilization.

Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.