International Journal of Remote Sensing

Casal, Gema; Monteys, Xavier; Hedley, John; Harris, Paul; Cahalane, Conor; McCarthy, Tim

doi: 10.1080/01431161.2018.1533660pmid: N/A

Bathymetry estimated from optical satellite imagery has been increasingly implemented as an alternative to traditional bathymetric survey techniques. The availability of new sensors such as Sentinel-2 with improved spatial and temporal resolution, in comparison with previous optical sensors, offers innovative capabilities for bathymetry derivation. This study presents an assessment of the fit between satellite data and the underlying models in the most widely used empirical algorithms: the linear band model and the log-transformed band ratio model using Sentinel-2A data. Both models were tested in two study areas of the Irish coast with different morphological and environmental conditions. Results showed that the linear band model fitted better than the log-transformed band ratio model providing coefficient of determination values, R2, between 0.83 and 0.88 (0 m–10 m) for the five images considered in the study. The closest fit was found in the depth range 2 m–6 m. Atmospheric correction, bottom type influence, and water column conditions proved to be key factors in the bathymetric derivation using these satellite datasets.

Wang, Chaojun; Zhao, Hongrui

doi: 10.1080/01431161.2018.1533661pmid: N/A

Remotely sensed time-series data have provided valuable information and sound foundations for ecological sustainability studies. Ecosystem sustainability has been viewed as a dynamic process that requires an ecosystem to deal with climate change and anthropogenic disturbances. Following this school of thought, ecosystem sustainability can be portrayed in terms of order and disorder using spatio-temporal analysis of entropy-related indices of Normalized Difference Vegetation Index (NDVI) time-series. Information theory and entropy-related measures have provided insights for complex systems analysis and have high relevance in ecology; however, less attention has been focused on temporal evolution and dynamics. The overall aim of this study is to propose an index called ‘temporal information entropy’ (Ht), and it is an entropy-related index able to describe the degree of order and regularity within a time-series of observations. We then assess Ht’s ability to measure the ecosystem sustainability of Yanhe watershed based on MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI time-series. Our results indicate that temporal information entropy of ecological time-series data may be used as a natural indicator with respect to sustainability, and in some degree, it helps us to get a better understanding of ecosystem dynamics from a physical-based standpoint.

Tang, Dongmei; Fan, Hui; Yang, Kun; Zhang, Yao

doi: 10.1080/01431161.2018.1533662pmid: N/A

The China–Laos border area is one of the world’s biodiversity hotspots and has undergone unprecedented social and economic shifts related to extensive land conversion to cash plantations in recent decades. However, spatially and temporally detailed information on land conversion and forest disturbance does not exist in this area. The aim of this study is to map and analyse spatiotemporal changes in forest disturbance from 1991 to 2016 along the China–Laos border using annual Landsat time series images. Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr), based on a temporal segmentation algorithm, was used with the Atmospherically Resistant Vegetation Index (ARVI) as a disturbance index in this study. The results show that the overall accuracy of forest disturbance is 89.72% ± 0.67% and that the estimated forest disturbance area between 1991 and 2016 reaches 4366.14 km2 ± 887.17 km2 (at the 95% confidence interval). This accounts for 16.73% of the total area of forest cover in 1991, which is based on the error-adjusted estimator of area. The trend in the forest disturbance area increased from 1991 to 1995 and then continued downward. The forest disturbance area across the China–Laos border is closely related to global rubber prices as well as the policies and economies of the two countries and cooperation between China and Laos. Compared to Laos, the percentage of disturbed forest area is higher within China, except for some individual years (e.g., 1998–1999, 2004–2005, 2009 and 2016). The average annual disturbed forest area is 98.44 km2 (0.76%) within China and 69.49 km2 (0.53%) within Laos. Large disturbed patches are much more common within China than within Laos. This study highlights the merit of using dense Landsat time series for mapping the human-induced processes of forest disturbance in tropical areas, and the role of economic globalization and regional geopolitics in cross-border forest management.

Pan, Xiaoju; Wong, George T. F.; Ho, Tung-Yuan; Tai, Jen-Hua

doi: 10.1080/01431161.2018.1538583pmid: N/A

The effects of the diel (involving a 24 hour period) variations in the surface concentrations of chlorophyll a (C) on the use of once-daily remotely sensed C as the diel average were assessed from the diel records in the derived depth-weighted C (Cd) that should be detected by remote sensing and the in situ surface C at two time-series stations in the North Pacific: the SEATS (SouthEast Asian Time-series Study) station in the northern South China Sea and the ALOHA (A Long-Term Oligotrophic Habitat Assessment) station in the North Pacific subtropical gyre. In situ surface C varied by a factor of about 2.0 and 1.3 over a diel cycle, and by ±20% and ±9% over the diel average at the SEATS and ALOHA stations, respectively. As the overpass-times of the different satellites were not identical, Cd was satellite-dependent. While the Cd corresponding to MODerate resolution Imaging Spectroradiometer on Aqua (MODIS-Aqua) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) overpass-times agreed to ±10%, the Cd corresponding to MEdium Resolution Imaging Spectrometer (MERIS) overpass-time could differ from the other two by −22% to +28% at the SEATS station and −1% to +12% at the ALOHA station. In addition, Cd corresponding to the overpass-times of the three satellites deviated from the observed diel average in situ surface C by −19% to +32% at the SEATS station and by −6% to +13% at the ALOHA station. These results indicate that, as a result of diel variations, neither a one-time remotely-sensed nor a one-time observed in situ surface C can represent the diel average in situ surface C accurately. Furthermore, diel variations are an inherent source of uncertainty when data from multiple satellites are pooled for use. The magnitudes of these discrepancies can be comparable to the commonly claimed uncertainties in remotely sensed C and thus should be taken into consideration in its interpretation and use.

Jakovljević, Gordana; Govedarica, Miro; Álvarez-Taboada, Flor

doi: 10.1080/01431161.2018.1538584pmid: N/A

Surface water maps are essential for many environmental applications. Waterbody delineation from satellite images remains a challenging task due to sensor limitations, the presence of clouds, the low albedo surfaces in urban areas, topographic, and atmospheric conditions. In this paper, a model based on the Supported Vector Machine (SVM) classifier was adopted for waterbody extraction from Sentinel-2, Landsat 8 Operational Land Imager (OLI) and RapidEye satellite images. As well, the accuracy of two other sources (OpenStreetMapping (OSM) and Military Geographic Institute (MGI)) was tested. The free images from Sentinel-2 and Landsat 8 OLI were more accurate (Kappa (KHAT):0.89, 0.88) data sources than commercial RapidEye images (KHAT: 0.79). Regarding the performance between Sentinel-2 and Landsat 8 OLI, Sentinel-2 obtained the most accurate results (overall accuracy 94.49 vs. 94.17, commission error 1.34 vs. 1.87). Due to the variable spatial resolution of OSM and MGI data, it was not possible to detect small waterbodies with these sources, and therefore high values of omission error and a strong underestimation of the area of surface water were obtained. This study demonstrates the suitability of free images for mapping and monitoring of surface waterbodies, including small water bodies.

Sun, Yue; Zhang, Hua; Shi, Wenzhong

doi: 10.1080/01431161.2018.1538585pmid: N/A

This paper presents a spatio-temporal fusion method for remote sensing images by using a linear injection model and local neighbourhood information. In this method, the linear injection model is first introduced to generate an initial fused image, the spatial details are extracted from the fine-resolution image at the base date, and are weighted by a proper injection gains. Then, the spatial details and the relative spectral information from the coarse-resolution images are blended to generate the fusion result. To further enhance its robustness to the noise, the local neighbourhood information, derived from the fine-resolution image and the fused result simultaneously, is introduced to refine the initial fused image to obtain a more accurate prediction result. The algorithm can effectively capture phenology change or land-cover-type change with minimum input data. Simulated data and two types of real satellite images with seasonal changes and land-cover-type changes are employed to test the performance of the proposed method. Compared with a spatial and temporal adaptive reflectance fusion model (STARFM) and a flexible spatio-temporal fusion algorithm (FSDAF), results show that the proposed approach improves the accuracy of fused images in phenology change area and effectively captures land-cover-type reflectance changes.

Xue, Li; Yang, Shuwen; Li, Yikun; Ma, Jijing

doi: 10.1080/01431161.2018.1538586pmid: N/A

Most existing shadow detection models and algorithms require extensive calculations and have difficulties effectively removing features, such as water bodies, some dark objects and bluish ground objects. In this paper, we propose a high-resolution automatic shadow extraction algorithm based on the process of histogram fitting. First, the histogram of the whole image is fitted by fourth and fifth-degree polynomials according to the histogram difference of the near-infrared bands of different shadow areas in the remotely sensed image. Second, the shadow area is preliminarily extracted based on the relationships between the shadow features of the remote sensing image and the intersections of the fourth- and fifth-degree polynomials. Then, the normalized difference water index (NDWI) is applied to extract the water bodies. Finally, to obtain the shaded area, the scanning line seed filling algorithm is applied to remove the water bodies falsely detected as shadows in the preliminary shading extraction. The proposed algorithm is evaluated by using the various high-resolution images including GaoFen-1 (GF-1), GaoFen-2 (GF-2), QuickBird2, and ZiYuan-3 (ZY-3), as well as an elaborate comparison to histogram threshold segmentation algorithms such as Component 3 (C3) algorithm, multi-elements extraction algorithm multi-band detection algorithm, and spectral correlation algorithm based on spectral features. The results of experiment showed that the proposed algorithm could extract the shadows of various images, achieve satisfied results, and completely remove water bodies.

Rupasinghe, Prabha Amali; Simic Milas, Anita; Arend, Kristin; Simonson, Martin Albert; Mayer, Christine; Mackey, Scudder

doi: 10.1080/01431161.2018.1539267pmid: N/A

Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.

Wang, Wenqing; Liu, Han; Liang, Lili; Liu, Qing; Xie, Guo

doi: 10.1080/01431161.2018.1539269pmid: N/A

Combining the spectral information of a low-resolution multispectral (LRMS) image and the spatial information of a high-resolution panchromatic (HRP) image to generate a high-resolution multispectral (HRMS) image has become an important and interesting issue. Local dissimilarities between the LRMS image and the HRP image affect the performance of the pan-sharpening technique. This paper presents a model-based pan-sharpening method with global and nonlocal spatial similarity regularisers to reduce the effects of the local dissimilarities. The degraded model relating the LRMS image to the unknown HRMS image is employed as the data-fitting term to keep spectral fidelity. Two spatial similarity constraints are utilized to further enhance the spatial resolution of the unknown HRMS image. The first regularisation term is under the assumption that the high-pass component of each HRMS band has the similar geometry structure with the adjusted high-pass component of the HRP image. A modulation matrix is constructed to reduce the contrast differences. Moreover, nonlocal self-similarity characteristic of the high-pass component extracted from each HRMS band is considered as another regulariser, which is an effective structural prior to improve the local spatial quality of the HRMS image. The weights of nonlocal similarity model are learned from the high-pass component of available HRP image. Experiments conducted on QuickBird and IKONOS data validate that the proposed pan-sharpening method can achieve better performance compared with several traditional and state-of-the-art pan-sharpening algorithms in terms of quantitative evaluation and visual analysis.

Deng, Yingbin; Wu, Changshan; Zhang, Xin; Jia, Xiuping

doi: 10.1080/01431161.2018.1539270pmid: N/A

Spectral mixture analysis (SMA) has been widely applied for estimating fractional land-cover types from remote sensing pixels. SMA typically assumes each spectral band has equal contribution to the unmixing results, which has attracted debates on whether a different weight should be given to each band. Subsequently, a number of weighted SMA (WSMA) approaches have been developed and applied to different research fields. The necessity and applicability of WSMA, however, have not been adequately addressed, especially when applied to urban environments. This paper, therefore, aims to answer two research questions, including 1) whether significantly different results would be generated through applying a WSMA, and 2) which WSMA approach performs better in an urban environment. Specifically, five existing schemes: Shannon Entropy-weighted method (Entropy), reflected energy fixed-weighted vector (REFWV), InStability Index-based weighting method (ISIb), combined weighting vector (WV), and within-class variance (VW), and five potential schemes: between-class variance (VB), total-class variance (VT), inversed Optimum Index Factor (IOIF), mean (Mean), and standard deviation (SD), were employed to construct WSMAs. We tested each weighting scheme 100 times with different endmember classes’ spectra. Performance of each WSMA was evaluated using the mean absolute error (MAE). Paired-samples t-test was applied to indicate if there is a significant difference between the mean of MAEs. Results illustrated that only REFWV, ISIb, and WV in All samples (samples included vegetation, high albedo impervious surface area, and low albedo impervious surface area) outperformed the unweighted scheme significantly. Other weighting schemes, such as IOIF, VB, VT, and SD illustrated unstable performance in different study areas. The rest of weighting schemes weakened the performance compared to the unweighted scheme. We concluded that REFWV, ISIb, and WV in All samples can be applied in analysing urban environments with three-endmember (vegetation – high albedo impervious surface area – low albedo impervious surface area) model to improve the performance of SMA. The construction of future weighting schemes would be better to consider the class variance.