Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Puletti, Nicola; Camarretta, Nicolò; Corona, Piermaria

doi:10.5721/eujrs20164909

Cited by 22 publications

(10 citation statements)

References 30 publications

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“…The applicability of dimensionality reduction was investigated in order to determine whether dimensionally reduced Hyperion data can be used for tree species classification and if it is possible, if it is feasible to classify tree species using selected Hyperion data corresponding to Sentinel-2 band information. To reduce the dimensionality of Hyperion data, a stepwise discriminant analysis procedure based on Wilk's lambda test statistic, which has been used for Hyperion data preprocessing by several research groups [8,15,72], was performed.…”

Section: Data Dimensionality Reductionmentioning

confidence: 99%

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Lim

Kim

Jin

2019

IJGI

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Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

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Section: Data Dimensionality Reductionmentioning

confidence: 99%

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Lim

Kim

Jin

2019

IJGI

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“…Hyperion is a Hyperspectral instrument on the Earth Observation 1 (EO-1) spacecraft launched on 21 November 2000 [ 59 ], with 7.5 km coverage [ 60 ]. It has a total of 242 bands from 357 to 2577 nm with a spectral resolution of 10 nm and a spatial resolution of 30 m [ 61 , 62 ]. In this study, one Hyperion image (ID: EO1H1190382015042110PF) of 11 February 2015 was captured from the USGS.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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“…Evaluation of significant abrupt land cover changes (e.g., forest cuttings) with remote sensing data is usually performed using bitemporal mapping approaches, i.e., by comparing, pixel by pixel, the classification results obtained from two coregistered images taken at different times. Examples of traditional algorithms used to solve this issue are maximum likelihood, support vector machine classification, classification and regression trees, and random forests [5]. The main operational drawback with such approaches is that when two standalone image classification processes are carried out, errors Land 2019, 8, 58 2 of 11 from both are summed, resulting in cumulative errors that reduce the change detection accuracy [6].…”

Section: Introductionmentioning

confidence: 99%

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Puletti

Bascietto²

2019

Land

Self Cite

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Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 "forest" points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km 2 in Tuscany, central Italy. Author Contributions: Conceptualization and Methodology N.P.; Formal Analysis N.P. and M.B.; Writing-Original Draft Preparation, Review and Editing, N.P. and M.B. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflicts of interest.

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Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Cited by 22 publications

References 30 publications

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

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