WorldView-2 Images Research Articles

Comparison of machine learning algorithms for mangrove species identification in Malad creek, Mumbai using WorldView-2 and Google Earth images

Comparison of machine learning algorithms for mangrove species identification in Malad creek, Mumbai using WorldView-2 and Google Earth images

Improving Mountain Snow and Land Cover Mapping Using Very-High-Resolution (VHR) Optical Satellite Images and Random Forest Machine Learning Models

Very-high-resolution (VHR) optical imaging satellites can offer precise, accurate, and direct measurements of snow-covered areas (SCA) with sub-meter to meter-scale resolution in regions of complex land cover and terrain. We explore the potential of Maxar WorldView-2 and WorldView-3 in-track stereo images (WV) for land and snow cover mapping at two sites in the Western U.S. with different snow regimes, topographies, vegetation, and underlying geology. We trained random forest models using combinations of multispectral bands and normalized difference indices (i.e., NDVI) to produce land cover maps for priority feature classes (snow, shaded snow, vegetation, water, and exposed ground). We then created snow-covered area products from these maps and compared them with coarser resolution satellite fractional snow-covered area (fSCA) products from Landsat (~30 m) and MODIS (~500 m). Our models generated accurate classifications, even with limited combinations of available multispectral bands. Models trained on a single image demonstrated limited model transfer, with best results found for in-region transfers. Coarser-resolution Landsat and MODSCAG fSCA products identified many more pixels as completely snow-covered (100% fSCA) than WV fSCA. However, while MODSCAG fSCA products also identified many more completely snow-free pixels (0% fSCA) than WV fSCA, Landsat fSCA products only slightly underestimated the number of completely snow-free pixels. Overall, our results demonstrate that strategic image observations with VHR satellites such as WorldView-2 and WorldView-3 can complement the existing operational snow data products to map the evolution of seasonal snow cover.

Investigating accuracies of WorldView-2, Sentinel-2, and SPOT-6 in discriminating morphologically similar savanna woody plant species during a dry season

Accurate assessment of woody species diversity using remote sensing can assist ecologists by providing timely information for ecosystem management. The increasing availability of remotely sensed data necessitates the investigation of accuracies of different sensors in classifying plant species, especially during the dry season when foliage amount is low. WorldView-2, SPOT-6, and Sentinel-2 images were compared in detecting woody species (n = 27) and three coexisting land cover types in a savanna environment during a dry period. Random Forest (RF) and Support Vector Machine (SVM) classifiers were applied to each imagery to make a strong case for the comparison. The overall classification accuracies ranged between 52% and 65% for all images, with the WorldView-2 image performing the best followed by Sentinel-2 and SPOT-6 images. These accuracy rankings were similar for both the RF and SVM classifiers, with the former faring better. Pairwise comparison of the images using McNemar’s test showed significant differences between images in their ability to correctly identify woody species. Analysis of band importance revealed better contributions to the classifications of infrared bands for all images. Overall, the findings showed the potential of optical imagery in classifying and monitoring woody species hotspots in savanna environments even during a low photosynthesis season.

Spectral-Spatial Interaction Network for Multispectral Image and Panchromatic Image Fusion

Recently, with the rapid development of deep learning (DL), an increasing number of DL-based methods are applied in pansharpening. Benefiting from the powerful feature extraction capability of deep learning, DL-based methods have achieved state-of-the-art performance in pansharpening. However, most DL-based methods simply fuse multi-spectral (MS) images and panchromatic (PAN) images by concatenating, which can not make full use of the spectral information and spatial information of MS and PAN images, respectively. To address this issue, we propose a spectral-spatial interaction Network (SSIN) for pansharpening. Different from previous works, we extract the features of PAN and MS, respectively, and then interact them repetitively to incorporate spectral and spatial information progressively. In order to enhance the spectral-spatial information fusion, we further propose spectral-spatial attention (SSA) module to yield a more effective spatial-spectral information transfer in the network. Extensive experiments on QuickBird, WorldView-4, and WorldView-2 images demonstrate that our SSIN significantly outperforms other methods in terms of both objective assessment and visual quality.

Multispectral panoptic segmentation: Exploring the beach setting with worldview-3 imagery

Panoptic segmentation is a recent and powerful task that tackles individual object recognition (“things”) and multiple backgrounds (“stuff”) simultaneously. Remote sensing studies with panoptic segmentation are still restricted and recent, with great application perspectives. In this sense, we propose the first multispectral panoptic segmentation study, considering the “thing” and “stuff” classes in the beach scenario and evaluating different sets of spectral bands. Our methodology included developing a dataset with 3800 (3200 for training, 300 for validation, and 300 for testing) with 128 × 128 spatial dimensions and eight spectral bands considering fourteen classes (6 “thing” and 8 “stuff” classes). We used WorldView-3 images from Praia do Futuro, Fortaleza, and pan-sharpening to improve spatial resolution. Five different spectral band configurations were considered: (1) all eight bands, (2) R G B + N I R 1 + N I R 2 , (3) R G B + N I R 1 , (4) R G B + N I R 2 , and (5) only RGB. The model training used the Panoptic-FPN architecture with the same hyperparameter settings considering three backbones (ResNeXt-101, ResNet-101, and ResNet-50). The best result considered the ResNeXt-101 with all spectral bands. However, the results from the first four configurations were very similar, and the RGB alone was the only configuration with significantly lower results. We also evaluated 15 semantic segmentation models for a benchmark comparison for the Beach Dataset. We show in visual results that even though the semantic models may be precise, they fail at identifying unique targets, especially in crowded locations such as the beach. The panoptic segmentation allowed a necessary detailing and counting of tourist infrastructures and mapping of other background features, establishing an essential tool for inspecting beach areas. • First panoptic segmentation study in the beach setting. • First panoptic segmentation study comparing spectral band compositions. • First panoptic segmentation study using multispectral WorldView-3 imagery. • Novel beach dataset suitable for instance, semantic and panoptic segmentation.

Spatial distribution patterns of coral reefs in the Abrolhos region (Brazil, South Atlantic ocean)

Managers and scientists benefit from satellite-derived bottom type maps of coral reefs and other highly biodiverse coastal ecosystems. Here, we generated a map of the coral reefs within the Abrolhos National Marine Park (ANMP), one of Brazil’s most iconic marine protected areas, using a very-high-spatial-resolution WorldView-2 (WV02) image and field observations. A particular geomorphology characterizes this region, where reefs grow in columnar pinnacles with different top height and dimensions (1–25 m height and 1–50 m diameter). The WV02 image went through three different processing steps (corrections for atmosphere, sun-glint and water column effects) to retrieve the bottom signal at 478 nm, which was used to produce a bottom map from an object-based image analysis (OBIA) and to evaluate reef distribution through landscape metrics. A bottom map with 64% of overall accuracy was obtained. Reef distribution showed a heterogeneous aggregation pattern of pinnacles to the East of the Abrolhos archipelago as well as the vegetated bottom around the archipelago. The novel map presented herein may guide management strategies of the AMNP, provide a background for connectivity studies, and help address the evolution (i.e., biogeomorphology processes) of the unique shallow Abrolhos reef structures.

Advantages of Nonlinear Intensity Components for Contrast-Based Multispectral Pansharpening

In this study, we investigate whether a nonlinear intensity component can be beneficial for multispectral (MS) pansharpening based on component-substitution (CS). In classical CS methods, the intensity component is a linear combination of the spectral components and lies on a hyperplane in the vector space that contains the MS pixel values. Starting from the hyperspherical color space (HCS) fusion technique, we devise a novel method, in which the intensity component lies on a hyper-ellipsoidal surface instead of on a hyperspherical surface. The proposed method is insensitive to the format of the data, either floating-point spectral radiance values or fixed-point packed digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the intensity component no longer lie on a hypersphere in the vector space of the MS samples, but on a hyperellipsoid. Furthermore, before the fusion is accomplished, the interpolated MS bands are corrected for atmospheric haze, in order to build a multiplicative injection model with approximately de-hazed components. Experiments on GeoEye-1 and WorldView-3 images show consistent advantages over the baseline HCS and a performance slightly superior to those of some of the most advanced methods.

Monitoring Dryland Trees With Remote Sensing. Part A: Beyond CORONA—Historical HEXAGON Satellite Imagery as a New Data Source for Mapping Open-Canopy Woodlands on the Tree Level

Monitoring woody cover by remote sensing is considered a key methodology towards sustainable management of trees in dryland forests. However, while modern very high resolution satellite (VHRS) sensors allow woodland mapping at the individual tree level, the historical perspective is often hindered by lack of appropriate image data. In this first study employing the newly accessible historical HEXAGON KH-9 stereo-panoramic camera images for environmental research, we propose their use for mapping trees in open-canopy conditions. The 2–4 feet resolution panchromatic HEXAGON satellite photographs were taken 1971–1986 within the American reconnaissance programs that are better known to the scientific community for their lower-resolution CORONA images. Our aim is to evaluate the potential of combining historical CORONA and HEXAGON with recent WorldView VHRS imagery for retrospective woodland change mapping on the tree level. We mapped all trees on 30 1-ha test sites in open-canopy argan woodlands in Morocco in the field and from the VHRS imagery for estimating changes of tree density and size between 1967/1972 and 2018. Prior to image interpretation, we used simulations based on unmanned aerial system (UAS) imagery for exemplarily examining the role of illumination, viewing geometry and image resolution on the appearance of trees and their shadows in the historical panchromatic images. We show that understanding these parameters is imperative for correct detection and size-estimation of tree crowns. Our results confirm that tree maps derived solely from VHRS image analysis generally underestimate the number of small trees and trees in clumped-canopy groups. Nevertheless, HEXAGON images compare remarkably well with WorldView images and have much higher tree-mapping potential than CORONA. By classifying the trees in three sizes, we were able to measure tree-cover changes on an ordinal scale. Although we found no clear trend of forest degradation or recovery, our argan forest sites show varying patterns of change, which are further analysed in Part B of our study. We conclude that the HEXAGON stereo-panoramic camera images, of which 670,000 worldwide will soon be available, open exciting opportunities for retrospective monitoring of trees in open-canopy conditions and other woody vegetation patterns back into the 1980s and 1970s.

Modeling Spatial Distribution of Carbon Sequestration, CO2 Absorption, and O2 Production in an Urban Area: Integrating Ground‐Based Data, Remote Sensing Technique, and GWR Model

AbstractThe main purpose of this research is to model the spatial distribution of carbon sequestration, CO2 absorption, and oxygen production by trees within Isfahan city, Iran, in 2020. To quantify carbon sequestration, we accessed a sample group of trees with measured biophysical attributes. First, we calculated the biomass and carbon sequestration of a tree using the allometric and photosynthesis equations. Then, to model the spatial distribution of carbon sequestration, we used geographic weighted regression (GWR) method. In this model, the amount of calculated carbon sequestration was the dependent variable, whereas the difference between vegetation indexes of Excess Green Plant Index minus Excess Red Plant Index (ΔExGR) from the Worldview image was the independent variable. Subsequently, the spatial distribution map of CO2 absorption and oxygen production was generated. The total value of annual carbon sequestration, CO2 absorption, and O2 production was about 7704.22, 28274.502, and 20570.16 tons, respectively. The results showed that there was a strong correlation between the ΔExGR index of the canopy with calculated carbon. Integrating the ΔExGR index from a high‐resolution image with calculated carbon can contribute to developing a fast, accurate, and low‐cost method in estimating carbon sequestration and modeling its spatial distribution in urban areas. In conclusion, the results of this research can be implemented by land‐use planners to integrate urban ecosystem service concept (i.e., carbon sequestration) in planning process toward sustainability of the cities.

Geoinformation analysis of the satellite imagery data in order to assess the changes in radiohydrological conditions over the study territories

The article considers the relevance of the application of a set of terrestrial hydrological, radioecological and modern remote sensing methods in the process of monitoring studies of areas that are difficult to access, waterlogged, largely radionuclide contaminated. The example of the Ukrainian part of the left-bank Pripyat drainage system within the Chornobyl Exclusion Zone shows that remotely obtained data together with the materials integrated into the geographic information systems of the terrestrial measurements provide the adequate monitoring information on the spatio-temporal changes in the hydrological and radioecological situation in general. Among the research methods, the simulation, the methods of hydrometric, balancing, remote sensing studies of water bodies were most used. The main purpose of the research is to perform the retrospective and current analyses of the hydrological situation based on archival and recent satellite images, assess the effectiveness of the water protection complex on the left bank of the Pripyat River, especially after the introduction of hydrotechnical measures to improve its radioecological status. In the research process the LANDSAT, SPOT, IRS, WorldView images were used. In particular, a significant sensitivity of the "surface water – dry land" system to the changes in the environment of this area was established and the mapping of objects of open water surface was performed as well as its spatial and temporal variability was followed. When comparing the results of calculations of the radionuclides carry-over by water (in particular, 90Sr) before and after the implementation of appropriate measures at the water protection complex the notable negative consequences of its implementation and operation during a period from 1986 to 2001 were revealed. It was found that the diverted one-time 90Sr carry-over due to the construction of a "new" left-bank dam (according to the 1999 flood scenario) is "compensated" by 40% through the increase of its carry-over prolonged in time as a result of operation of the polder pumping station (PPS) and the inundation of the contaminated areas, which is caused by the complicated conditions for the discharge of surface and underground runoff caused by the "new" and "old" dams. The ecological advisability and validity of hydraulic measures (clearing of canals, reconstruction of culverts) and the recommended form of the operational complex (without the use of PPS, with natural filtration discharge of the body of the "new" sand dam in the Pripyat River) are confirmed by the results of thematic interpretation of the multiband images. Thus, within the complex, minor flooding and waterlogging of the inter-dam section, as well as areas to the north-east from the "old" dam are established. Prospects for the further research of various hydrological processes and radioecological situation on the left bank of the Pripyat River are seen in the use of high resolution remote surveys, which would be performed over the short periods of time. The informative result in good quality can be obtained on the basis of the use of unmanned aerial vehicles technology.

AUTOMATIQUE TECHNIQUE FOR OBTAINING URBAN/SUBURBAN CHANGE DETECTION INFORMATION FROM HIGH-RESOLUTION SATELLITE IMAGES

Abstract. Multitemporal satellite images can serve for monitoring the same geographical area on the earth and recognize potential changes that have occurred. Actually, high-resolution (HR) satellite images can provide detailed pieces of information about change detection. In the current work automatic change detection is carried out in urban and suburban areas of Crete, Greece. QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel, that have a relative time difference of 13 years, are utilized. The scene is classified into buildings, vegetation, water and ground as soon as multiindex representation has been applied on the images. Afterwards, automatic change detection is feasible by pixel-per-pixel comparison of the classified multi-temporal images.The water index and the vegetation index which are utilized in the current work are compared with vegetation and water detections which appear in the literature. The experimental results indicate the superiority of the particular spectral indices. Moreover, the presented methodology not only specifies if changes have arisen or not but also provides particular information regarding the types of changes. In the future, further experimentations could lead to the optimization of the proposed methodology.

MAPPING EUCALYPTUS SPECIES USING WORLDVIEW 3 AND RANDOM FOREST

Abstract. Recent advances in remote sensing technologies have allowed the development of new innovative methodologies to obtain geospatial information about Eucalyptus genus distribution. This is an important task for forest stakeholders due to the high presence of this genus in forest plantations worldwide. Therefore, the next step in research should focus on exploring remote sensing possibilities to discern between Eucalytpus species. It would be an important step forward in forest management since different Eucalyptus species present different characteristics and properties that imply different management plans and industrial usages. This study accomplish the classification of E. nitens and E. globulus, the most common Eucalyptus species in the Iberian Peninsula. Worldview-3 images and random forest are used in a forest area placed in Galicia (Northwest of Spain). The differentiation of Eucalyptus species resulted in a producer’s accuracy of 84% and a users’ accuracy of 70% for E. nitens, while for E. globulus accuracy metrics did not reach 70%. The most important bands in the classification were the coastal blue and the blue, followed by the red related ones. The resulting unequal accuracy metrics might be caused by an imbalanced presence of both species in the selected study area. Therefore, further studies might be developed in different locations.

AUTOMATIC POLE DETECTION IN AERIAL AND SATELLITE IMAGERY FOR PRECISE IMAGE REGISTRATION WITH SAR GROUND CONTROL POINTS

Abstract. The world-wide absolute geographic positioning accuracy of optical Satellite imagery is mostly about a few pixels of the image resolution. So for example WorldView-3 images have a CE90 of about 4 m. Also the direct georeferencing without ground control information of aerial imagery is in the same range of one to a few metres. These inaccuracies originate predominantly in uncertainties of angular measurements for the sensor attitude. An angular error of only one arc-second at a satellite 750 km above ground results in an absolute error on ground of 3.6 metres. On the other hand radar satellites like TerraSAR-X or TanDEM-X do not measure angles but signal runtimes. So if we identify the same point in an optical image and in a radar image we can solve the problem of inaccurate angle-measurements in the optical sensor models and are able to georeference optical images world wide absolute to below one pixel. In this paper we present a method for identification of point-objects which can be detected in both types of images: the footpoints of poles. If such a footpoint of a pole can be detected simultaneously in both types of images the geoposition of the optical image can be corrected to the accuracy of the point-measurement in the radar image. To achieve a high accuracy also a nearly perfect correction of all errors in signal propagation times of the radar signals has to be conducted. In this paper we describe how the footpoints of poles will be extracted in optical spaceborne or air-borne imagery and how these footpoints are correlated to the potential footpoints of poles detected in the radar imagery.

SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN

Abstract. Remote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of SRGAN which means Super-Resolution using a Generative Adversarial Network (a network structure that uses the loss function considering the perceptual loss and the adversarial loss to improve the spatial resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and breadth of remote sensing images.We adjust the reasonable parameters and network structure for our research by analysing the SRGAN in the network architecture, the perceptual loss and the adversarial loss.A super-resolution model is obtained by training with aerial photogrammetry images whose spatial resolution are 0.1 meter in Shanghai.We find the improved SRGAN has a good performance in in remote sensing image super-resolution by comparing the super-resoved images with real high-resolution images in visual perception, spatial position mapping accuracy and chromaticity spatial information. In addition, it is proved that the trained model is also effective to deal with Worldview-2 and SuperView-1 satellite images whose spatial resolution are 0.5 m. Our research shows that our method which can effectively realize the super-resolution of remote sensing images has great potential in the application of remote sensing technology such as urban mapping and changes monitoring.

ENHANCED SUPER RESOLUTION FOR REMOTE SENSING IMAGERIES

Abstract. Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.

Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery

Feature selection to reduce redundancies for efficient classification is necessary but usually time consuming and challenging. This paper proposed a comprehensive analysis for optimum feature selection and the most efficient classifier for accurate urban area mapping. To this end, 136 multiscale textural features alongside a panchromatic band were initially extracted from WorldView-2, GeoEye-3, and QuickBird satellite images. The wrapper-based and filter-based feature selection were implemented to optimally select the best ten percent of the primary features from the initial feature set. Then, machine leaning algorithms such as artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers were utilized to evaluate the efficiency of these selected features and select the most efficient classifier. The achieved optimum feature set was validated using two other images of WorldView-3 and Pleiades. The experiments revealed that RF, particle swarm optimization (PSO), and neighborhood component analysis (NCA) resulted in the most efficient classifier and wrapper-based and filter-based methods, respectively. While ANN and SVM’s process time depended on the number of input features, RF was significantly resistant to the criterion. Dissimilarity, contrast, and correlation features played the greatest contributing role in the classification performance among the textural features used in this study. These trials showed that the feature number could be reduced optimally to 14 from 137; these optimally selected features, alongside the RF classifier, can produce an F1-measure of about 0.90 for different images from five very high resolution satellite sensors for various urban geographical landscapes. These results successfully achieve our goal of assisting users by eliminating the task of optimal feature selection and classifier, thereby increasing the efficiency of urban land use/cover classification from very high resolution images. This optimal feature selection can also significantly reduce the high computational load of the feature-engineering phase in the machine and deep learning approaches.

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images.

The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy.

Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

ABSTRACT Spatially explicit information on aboveground seagrass carbon stock (AGCseagrass) is required to understand the role of seagrass as a nature-based solution to mitigating and adapting to climate change. Remote sensing provides the most effective and efficient means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with different characteristics. Kemujan Island in Indonesia was selected to represent patchy beds with relatively low seagrass percent cover and low water clarity, whereas Labuan Bajo of Indonesia served an example of continuous beds with a relatively high percent cover and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and water column correction, principal component analysis, and co-occurrence texture analysis, were applied to the atmospherically corrected WorldView-2 images. These datasets were used as inputs in the AGC empirical model using seven regression algorithms, namely, single-band linear regression, band-ratio linear regression, stepwise regression, random forest regression, support vector regression, extreme gradient boosting, and multivariate adaptive regression spline. Seagrass field data collected using photo quadrat technique were used to train the regression model and assess the accuracy of the resulting AGCseagrass maps. The results indicated that WorldView-2 can be used to map AGC in different seagrass meadows with a consistent accuracy. The most accurate AGCseagrass map for Kemujan Island had a root mean square error (RMSE) of 4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2, with aboveground stock range of 0.31–64.38 g C m−2. Model cross-validation revealed that the mapping model can be site-specific or robust depending on the characteristics of the field-derived AGCseagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is a key factor in the future development of a robust AGCseagrass mapping model that is applicable to various seagrass meadows as a stepping stone to accelerating the filling of gaps in the global seagrass dataset.

Mapping α- and β-diversity of mangrove forests with multispectral and hyperspectral images

Mangrove deforestation has rapidly declined by an order of magnitude compared to that reported for the 20th century, but the remaining mangrove ecosystems are still undergoing biodiversity loss and degradation. Labor-intensive ground surveys that are usually used for terrestrial plant biodiversity assessment are difficult to conduct in the mangrove forests. Assessing plant biodiversity from space does offer a novel perspective, but existing solutions are mainly focused on estimating α- or β-diversities (i.e., diversity within communities or diversity among communities) alone. This paper applied a novel holistic biodiversity approach that could partition γ-diversity into α- and β-diversities for plant diversity mapping with operational satellites (WorldView-2, Sentinel-2, and Zhuhai-1) and field plots in the Qinglangang Provincial Nature Reserve, Hainan, China. We compared the resulting outputs of the α-diversity from the holistic method (SDα) to those by the coefficient of variation (CV) and the Rao's Q index, and the contributions of individual spectral features to α- and β-diversity were also measured. Results indicated that α- and β-diversities accounted for ~30% and ~ 70% of the total diversity in the Reserve, respectively. α-diversity derived from the WorldView-2 images showed statistically higher correlations with the observed Shannon's index (R2: 0.20–0.42) compared to that from Sentinel-2 and Zhuhai-1 (R2: 0.03–0.15). β-diversity derived from WorldView-2 images had the highest accuracy (90.00%), followed by that from Sentinel-2 and Zhuhai-1 (83.33% and 73.33%, respectively). Red-edge and near-infrared spectral features were the most informative features for diversity mapping while shortwave infrared (SWIR) features were also valuable for β-diversity mapping. Concurrent mapping of α- and β-diversities of mangrove forests represents the first step toward achieving rapid biodiversity monitoring schemes of mangrove forests over a national or global scale.

Estimation of aboveground mangrove carbon stocks from WorldView-2 imagery based on generic and species-specific allometric equations

The aboveground mangrove carbon stock can be estimated and mapped from remote-sensing data using allometric equations. However, the distinct effects of applying the generic or species-specific allometric equations remain unclear in the mangrove environment. The aims of this study are to (1) describe the relationship between field biomass and image pixels using generic and species-specific allometric equations, (2) estimate and map the mangrove carbon stock at the generic and species-specific levels, and (3) compare the accuracy and distribution of the resulting mangrove carbon stock maps. We tested the performance of these allometric equations using a resampled WorldView-2 image (6 m pixel size). The study site was in Clungup Mangrove Conservation (CMC), Malang, East Java and included natural mangrove forests and well-planted mangroves. We used the simple ratio (SR) vegetation index to estimate and map the aboveground carbon stock (AGC) of the mangroves based on the WorldView-2 image. Overall, the results indicate that the use of a generic allometric equation estimates mangrove AGC more accurately than the species-specific equations. However, we found a low coefficient of determination (R2) for both models: 0.2175 for the generic model and 0.1801 for the species-specific one. This may be due to the mangrove objects’ high variation in internal spectral reflectance at the tested image pixel size. The total estimated mangrove AGC obtained was 3,834.15 tons with a standard error of 0.12 for the generic model and 4,927.90 tons with a standard error of 0.17 for the species-specific model. Our findings substantiate the importance of the selection of allometric equations for mangrove AGC studies based on remote-sensing images.