WorldView-2 Images Research Articles

Testing the value of freely available Landsat 8 Operational Land Imager (OLI) and OLI pan-sharpened imagery in discriminating commercial forest species

ABSTRACT The adoption of remotely sensed data in forest applications has grown significantly. Whereas high spatial resolution sensors have been successful in mapping and monitoring commercial forests, their cost, accessibility, and spatial coverage remain a critical challenge. Hence, it is was necessary to investigate the value of new and improved freely available sensors in forest species mapping using the Partial Least Square-Discriminant Analysis (PLS-DA). This study evaluated the performance of new freely available and improved raw and pan-sharpened Landsat 8 Operational Land Imager (OLI) imagery in discriminating seven key plantation forest species in KwaZulu-Natal, South Africa. Accuracies achieved using the Landsat (OLI) imagery were benchmarked against the WorldView-2 imagery. Results show that raw and pan-sharpened bands successfully delineated commercial forest species, with overall classification accuracies of 79% and 77.8%, respectively. Although these accuracies were lower than the 86.5% achieved from the higher resolution Worldview-2 image data, our findings demonstrate that the Landsat 8 OLI’s lower spatial resolution (30 m) generated a plausible performance in discriminating forest species. Hence, Landsat 8 OLI could be useful in providing existing and historical preliminary forestry assessment due to its free availability, wide spatial coverage as well as its rich archive dating back to the 1970s.

Remote Sensing Image Registration Based on Deep Learning Regression Model

We propose a novel remote sensing image registration method based on the deep learning regression network. Different from the traditional methods of feature extraction and feature matching, we pair the image blocks from sensed and reference images, and then directly learn the displacement parameters of the four corners of the sensed image block relative to the reference image. In addition, we develop the dual deep learning network with weight sharing to fully extract the registration pair image features. The proposed method is tested on different period Landsat-7 and WorldView-3 images and compared with scale-invariant feature transform (SIFT), fast and rotated brief (ORB), and other deep learning methods. The proposed method outperforms all the comparing methods.

The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

Object-based crop classification in Hetao plain using random forest

Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively small size. Second, importance scores for each feature variable are derived by using the RF model. Third, by treating the importance scores as weighting factors, a weighted Euclidean distance criterion is designed and used for sample creation to enlarge training set. Fourth, RF is re-trained by using the enlarged training set, and then it is employed for final classification. To validate the proposed strategy, a Worldview-2 image covering a part of Hetao plain is experimented. Results indicate that the new method yields the best overall accuracy, which equals 90.52%.

Hyperspectral and multispectral data fusion using fast discrete curvelet transform for urban surface material characterization

The objective of the present study is to analyze the quality of hyperspectral data fusion using low spatial hyperspectral (LSH) Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) 8 m data and high spatial multispectral (HSM) WorldView-3 image at 1.24 m remote sensing images with spectral unmixing technique. The resultant HSH data shows new prospects for urban surface material characterization with spectrally distinct classes. The spatial resolution of LSH is enhanced by injecting the high-frequency details from the corresponding HSM bands in fast discrete curvelet transform domain. The image fusion-based products’ quality has been analyzed by endmembers extraction and fractional maps generated using Piecewise Convex Multiple-Model Endmember Detection (PCOMMEND) method. Experimental results showed that the fusion has improved the spatial as well as spectral separability to extract the endmembers, particularly for the urban surface materials like the combination of water and asphalt, and bare soil and roof tiles.

Monitoring Urban Green Infrastructure Changes and Impact on Habitat Connectivity Using High-Resolution Satellite Data

In recent decades, the City of Stockholm, Sweden, has grown substantially and is now the largest city in Scandinavia. Recent urban growth is placing pressure on green areas within and around the city. In order to protect biodiversity and ecosystem services, green infrastructure is part of Stockholm municipal planning. This research quantifies land-cover change in the City of Stockholm between 2003 and 2018 and examines what impact urban growth has had on its green infrastructure. Two 2018 WorldView-2 images and three 2003 QuickBird-2 images were used to produce classifications of 11 land-cover types using object-based image analysis and a support vector machine algorithm with spectral, geometric and texture features. The classification accuracies reached over 90% and the results were used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2003 and 2018, including the generation of land-cover change statistics in relation to administrative boundaries and green infrastructure. For components of the green infrastructure, i.e., habitat networks for selected sensitive species, habitat network analysis for the European crested tit (Lophophanes cristatus) and common toad (Bufo bufo) was performed. Between 2003 and 2018, urban areas increased by approximately 4% while green areas decreased by 2% in comparison with their 2003 areal amounts. The most significant urban growth occurred through expansion of the transport network, paved surfaces and construction areas which increased by 12%, mainly at the expense of grassland and coniferous forest. Examination of urban growth within the green infrastructure indicated that most land area was lost in dispersal zones (28 ha) while the highest percent change was within habitat for species of conservation concern (14%). The habitat network analysis revealed that overall connectivity decreased slightly through patch fragmentation and areal loss mainly caused by road expansion on the outskirts of the city. The habitat network analysis also revealed which habitat areas are well-connected and which are most vulnerable. These results can assist policymakers and planners in their efforts to ensure sustainable urban development including sustaining biodiversity in the City of Stockholm.

SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS

Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.

Land Cover Classification in Mangrove Ecosystems Based on VHR Satellite Data and Machine Learning—An Upscaling Approach

Mangrove forests grow in the inter-tidal areas along coastlines, rivers, and tidal lands. They are highly productive ecosystems and provide numerous ecological and economic goods and services for humans. In order to develop programs for applying guided conservation and enhancing ecosystem management, accurate and regularly updated maps on their distribution, extent, and species composition are needed. Recent advances in remote sensing techniques have made it possible to gather the required information about mangrove ecosystems. Since costs are a limiting factor in generating land cover maps, the latest remote sensing techniques are advantageous. In this study, we investigated the potential of combining Sentinel-2 and Worldview-2 data to classify eight land cover classes in a mangrove ecosystem in Iran with an area of 768 km2. The upscaling approach comprises (i) extraction of reflectance values from Worldview-2 images, (ii) segmentation based on spectral and spatial features, and (iii) wall-to-wall prediction of the land cover based on Sentinel-2 images. We used an upscaling approach to minimize the costs of commercial satellite images for collecting reference data and to focus on freely available satellite data for mapping land cover classes of mangrove ecosystems. The approach resulted in a 65.5% overall accuracy and a kappa coefficient of 0.63, and it produced the highest accuracies for deep water and closed mangrove canopy cover. Mapping accuracies improved with this approach, resulting in medium overall accuracy even though the user’s accuracy of some classes, such as tidal zone and shallow water, was low. Conservation and sustainable management in these ecosystems can be improved in the future.

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.

Multi-objective optimization for worldview image segmentation funded on the entropies of Tsallis and Rényi

Image analysis usually refers to processing of images with the objective of finding objects presented in the image. The extraction and the analysis of image data is a fundamental step for image segmentation, in this work a new method allowing the evolution of the threshold satellite image defined and based on the optimization multi-objective for segmentation of Worldview images and funded on the Tsallis and the Renyi entropies. The Objective is the reclassification of all unclassified pixels by the previous method in 2017. The improved analysis and the optimized method multi-objective thresholding are proposed. First, respectively for each Worldview image selected, the optimal thresholds for all the criteria used in this study is find. Finally, by using the evaluation criteria corresponding to the Levine and Nazif criteria and the criteria of the mean square error, in order to challenge the performance of this method to that previously developed in 2017. The results obtained by this approach were very satisfactory and the efficacy of this method confirmed. This method overcomes the difficulties of the method previously developed in 2017 and obtained results that are more precise. Therefore, the new method based on multi-objective optimization contribute significantly to performance.

SAVMA 法を利用した WorldView-2 画像による阿寒湖の沈水植物分布の推定

SAVMA 法を利用した WorldView-2 画像による阿寒湖の沈水植物分布の推定

ANALYSIS ON THE EFFECT OF SPATIAL AND SPECTRAL RESOLUTION OF DIFFERENT REMOTE SENSING DATA IN SUGARCANE CROP YIELD STUDY

Abstract. Sugarcane is a perennial crop that contributes to nearly 80% of the global sugar-based products. Therefore, sugarcane growers and food companies are seeking ways to address the concerns related to sugarcane crop yield and health. In this study, a spatial and spectral analysis on the peak growth stage of the sugarcane fields in Bundaberg, Queensland, Australia is performed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) derived from high-resolution WorldView-2 (WV2) images and multispectral Unmanned Aerial Vehicle (UAV) images. Two topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index, a common crop parameter for the assessment of crop yield and health stages; 2) the impact of spatial resolution on the systematic difference in the abovementioned two Vegetation Indices (VIs). The statistical correlation analysis between the WV2 and UAV images produced correlation coefficients of 0.68 and 0.71 for NDVI and NDRE, respectively. In addition, an overall comparison of the WV2 and UAV-derived VIs indicated that the UAV images produced a better accuracy than the WV2 images because UAV can effectively distinguish various status of vegetation owing to its high spatial resolution. The results illustrated a strong positive correlation between NDVI and NDRE, each derived from the WV2 and UAV images, and the correlation coefficients were 0.81 and 0.90, respectively, i.e. the correlation between NDVI and NDRE is higher in the UAV images than the WV2 images.

SEMANTIC SEGMENTATION OF BRAZILIAN SAVANNA VEGETATION USING HIGH SPATIAL RESOLUTION SATELLITE DATA AND U-NET

Abstract. Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.

Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.

Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis

The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2, which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts.

Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R= −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

Parcel-level mapping of crops in a smallholder agricultural area: A case of central China using single-temporal VHSR imagery

Timely and accurate monitoring of crop patterns in smallholder agricultural areas is essential for guiding local crop yield estimates, agricultural subsidy allocations, and food security policy formulation. The household-operated land management model leads to a highly fragmented and heterogeneous agricultural landscape; therefore, fine crop mapping in smallholder agricultural areas remains challenging. By using very high spatial resolution (VHSR) images, this study aimed to explore parcel-level crop mapping methods using the case of a typical smallholder agricultural area in Wuhan, China. Object-based image analysis techniques as well as random forest (RF) and support vector machine (SVM) classifiers were used to classify WorldView-2 (WV2) images into eight crop-level agricultural land use categories. Several classification models were built using the combination of two classifiers and different image features, including spectral, geometrical, and textural features. The results showed that the classification model using the RF classifier and all 27 selected features had the highest accuracy, with an overall accuracy of 80.04% and a kappa value of 0.78; specifically, the user’s and producer’s accuracies of rice, cotton, lotus, bare paddy field and bare upland field exceeded 80%. We found that the performance of the RF and SVM classifiers was generally comparable, although as the input features increased, the accuracy of the RF was slightly higher than that of the SVM. The use of spatial features, such as the gray level cooccurrence matrix (GLCM) standard deviation, GLCM correlation, and area of image objects, could help improve the accuracy of parcel-level crop mapping. Our research confirmed the practical value of single-temporal VHRS images and RF classifiers in mapping parcel-level crops in complex agricultural areas. This framework provides a methodological reference for accurately monitoring crop distribution in smallholder agriculture areas to support the development of local precision agriculture.

Deep-learning based high-resolution mapping shows woody vegetation densification in greater Maasai Mara ecosystem

The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small-holder fencing. Here, we present the first comprehensive vegetation map of GMME at high (10-m) spatial resolution. The map consists of nine key vegetation cover types (VCTs), which were derived in a two-step process integrating data from high-resolution WorldView-3 images (1.2-m) and Sentinel-2 images using a deep-learning workflow. We evaluate the role of anthropogenic, topographic, and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS-derived browning/greening rates in the preceding 17 years at 250-m resolution. Results show that most VCTs showed a preceding greening trend in the protected land. In contrast, the semi- and unprotected land showed a general preceding greening trend in the woody-dominated cover types, while they exhibited browning trends in grass-dominated cover types. These results suggest that woody vegetation densification may be happening across much of the GMME, alongside vegetation declines within the non-woody covers in the semi- and unprotected lands. Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure. Increasing woody densification across the entire GMME in the future would replace high-quality grass cover and pose a risk to the maintenance of the region's rich savanna megafauna, thus pointing to a need for further investigation using alternative data sources. The increasing availability of high-resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change.

Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India

Landslide is a complex natural hazard that sometimes causes disaster resulting in loss of life, assets and infrastructure, especially in the Himalayas. Recent studies suggest that for effective mitigation and resilience through proper planning and policymaking, it is equally important to justify and select a suitable scientific technique that most appropriately addresses the salient causes of a landslide in any area. The principal objective of this study is to carry out a comparative assessment between two contemporary statistical techniques, i.e., the statistical information value (SIV) and index of entropy (IOE), to find out the effectiveness of the two said methods in landslide susceptibility mapping in Bhanupali-Beri region. During the analysis, the higher-resolution satellite images, i.e., World view-2 image of 2017 and Landsat-8 OLI image of 2018, have been used for delineation of various triggering parameters used for landslide susceptibility. The contemporary GIS technique integrated with the remote sensing applications was distinct in preparing the prominent landslide conditioning factor layers such as slope, slope aspect, thrust and fault proximity, geomorphology, landuse–landcover, stream power index, topographic wetness index, geology, roads proximity, lineament density and past landslide inventory. The final assessment was performed using GIS software through raster re-sampling, and the values derived for each conditioning factors were combined using defined SIV and IOE equations. The study area was categorized into five distinct landslide susceptible zones (very low, low, moderate, high and very high) using the Jenk’s Natural Breaks algorithm. Index of entropy model has given better results compared to SIV. The utmost vital factors triggering landslide (estimated for entropy values) in the area are landuse–landcover with barren land and sparse vegetation followed by TWI, lineament density, geomorphology, and slope.

Assessment and Quantification of the Accuracy of Low- and High-Resolution Remote Sensing Data for Shoreline Monitoring

Τhe accuracy of low-resolution remote sensing data for monitoring shoreline evolution is the main issue that researchers have been trying to overcome in recent decades. The drawback of the Landsat satellite archive is its spatial resolution, which is appropriate only for low-scale mapping. The present study investigates the potentialities and limitations of remote sensing data and GIS techniques in shoreline evolution modeling, with a focus on two major aspects: (a) assessing and quantifying the accuracy of low- and high-resolution remote sensing data for shoreline mapping; and (b) calculating the divergence in the forecasting of coastline evolution based on low- and high-resolution datasets. Shorelines derived from diachronic Landsat images are compared with the corresponding shorelines derived from high-spatial-resolution airphotos or Worldview-2 images. The accuracy of each dataset is assessed, and the possibility of forecasting shoreline evolution is investigated. Two sandy beaches, named Kalamaki and Karnari, which are located in Northwestern Peloponnese, Greece, are used as test sites. It is proved that the shorelines derived from the Landsat data present a displacement error of between 6 and 11 m. The specific data are not suitable for the shoreline forecasting procedure and should not be used in related studies, as they yield less accurate results for the two study areas in comparison with the high-resolution data.