Recent morphological changes (2009-2022) on the floodplain of the Guadalporcún River, province of Cádiz (Southern Spain)

Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation

The Windows screen-capture tools was used to get the Google Earth (GE) images. Compared with the original remote sensing images, although the image quality was reduced and the spectral information was lacking, it has been able to meet the needs of this study. A method for estimating fractional vegetation coverage (FVC) using GE images based on K-Means algorithm was proposed. Firstly, GE image was preliminarily classified by using K-Means algorithm. Secondly, by visual interpretation, the initial classification results were further clustered into 4 types according to the number and brightness feature of land surface types in the image, low brightness (shadow), medium low brightness (high density vegetation), medium high brightness (sparse vegetation), high brightness (bare ground), the FVC of each category was determined by its characteristics and composition. Finally, weighted by the proportion of pixels in the image, took the weighted sum of the FVC of all categories as the FVC of the image. In addition, the field survey data were used to verify the FVC estimated by the proposed method, the results showed that: the precision of estimated vegetation coverage could reach 80% ~ 90%.

The Huron River consists of alternating bedrock reaches and alluvial reaches. Analysis of historical aerial photography from 1950-2015 reveals six major channel avulsion events in the 8-km study area. These avulsions occurred in the alluvial reaches but were strongly influenced by the properties of the upstream bedrock reach (“inherited characteristics”). The bedrock reaches aligned with the azimuth of joint sets in the underlying bedrock. One inherited characteristic in the alluvial reach downstream is that the avulsion channels diverged only slightly from the orientation of the upstream bedrock channel (range 2 ° - 38 °, mean and standard deviation 12.1 ° ± 13.7 °). A second inherited characteristic is that avulsion channels were initiated from short distances downstream after exiting the upstream bedrock channel reach (range 62 - 266 m, mean and standard deviation 143.7 ± 71.0 m), which is a fraction of the meander wavelength (1.2 km). Field evidence shows that some avulsion channel sites were re-occupied episodically. In addition, two properties were necessary for channel avulsions: 1) avulsion events were triggered by channel-forming hydrologic events (5-year recurrence interval flows), but not every channel-forming hydrologic event resulted in an avulsion, and 2) channel sinuosity (P) increased to 1.72 - 1.77 prior to an avulsion then decreased to 1.65 - 1.70 following an avulsion, suggesting that P ≥ 1.72 is the “critical sinuosity” or triggering value for avulsions on the Huron River. In summary, for this river consisting of alternating bedrock and alluvial reaches, the bedrock reaches impose certain parameters on downstream alluvial reaches (including sediment supply, channel direction and avulsion channel position downstream after exiting a bedrock reach) while adjustments in sinuosity and sediment storage occur in the alluvial reaches.

ABSTRACTRapidEye satellite images with high spatial resolution, affordable prices and having Red-Edge band have high potential for time series issues, especially in vegetation studies. Despite these beneficial properties, RapidEye images with 5 m spatial resolution are not sufficiently useful for some applications. According to this problem, enhancing the spatial resolution of RapidEye images can significantly improve the results of the subsequent processes on these images. Fusion of high spatial resolution with high spectral resolution images is known as an effective way to enhance the quality of multispectral remotely sensed images. Unfortunately, the lack of panchromatic band with high spatial resolution has been faced the procedure of improving the spatial resolution of RapidEye images with major problems. In this paper, we have proposed using the free Google Earth (GE) images which have high spatial resolution and high-coverage of land surface to enhance the spatial information of RapidEye images. A simulated panchromatic image has been generated by three band GE image and with three different methods: Mean, principal component analysis (PCA) and weighted average of GE image bands. In the last method, the weights are extracted from the spectral response curve of the satellite which captured the GE image. The simulated panchromatic image has been utilized for pansharpening of RapidEye image in five well-known methods: Brovey, Gram-Schmidt (GS), intensity-hue-saturation (IHS), Pansharp1 and Pansharp2. The most important point is finding the GE image with lowest lag time with RapidEye image. By satisfying this condition, the experiments illuminated that the proposed method can effectively enhance the spatial quality of RapidEye image. Also, this study presented that Pansharp2 method, which used simulated panchromatic image generated by the spectral response curve information, has revealed the best results of RapidEye image pansharpening.

Duy, D.V.; Tanaka, H.; Mitobe, Y.; Anh, N.Q.D., and Viet, N.T., 2018. Sand spit elongation and sediment balance at Cua Lo Inlet in Central Vietnam. In: Almar, R.; Almeida, L.P.; Trung Viet, N., and Sall, M. (eds.), Tropical Coastal and Estuarine Dynamics. Journal of Coastal Research, Special Issue No. 81, pp. 32–39. Coconut Creek (Florida), ISSN 0749-0208.Erosion is significant on the downdrift beach of Cua Lo Inlet, which is located in Central Vietnam. The elongation of Cua Lo Inlet's updrift sand spit together with severe erosion of the downdrift bank is raising numerous social problems such as flood safety and land loss. In this study, a series of Landsat and Google Earth images were used to analyze and investigate the inlet recent morphological changes and evolution. Since 1970 the inlet has constantly migrated towards the sediment transport pathway, and its traveling distance has reached 1700 m. In order to quantitatively investigate such characteristics, the tip coordinates and the area of the sand spit updrift of the inlet were calculated. In particular, the area of the sand spit has increased remarkably, and the increasing rate is almost constant. Using this value, the rate of sand volume transported along the updrift sand spit by the waves was estimated and it is in good agreement with the longshore sediment transport rate of the surrounding beach.

Monitoring and mapping complex urban features (e.g. roads and buildings) from remotely sensed data multispectral and hyperspectral has gained enormous research interest. Accurate ground truth allows for high quality assessment of classified images and to verify the produced map. Ground truth can be acquired from: field using the handheld Global Positioning System (GPS) device and from Images with high resolution extracted from Google Earth in additional to field. Ground truth or training samples could be achieved from VHR satellite images such as QuickBird, Ikonos, Geoeye-1 and Wordview images. Archived images are costly for researchers in developing countries. Images from GE with high spatial resolution are free for public and can be used directly producing large scale maps, in producing LULC mapping and training samples. Google Earth (GE) provides free access to high resolution satellite imagery, but is the quality good enough to map urban areas. Costal of the Red sea, Marsa Alam could be mapped using GE images. The main objective of this research is exploring the accuracy assessment of producing large scale maps from free Google Earth imagery and to collect ground truth or training samples in limited geographical extend. This research will be performed on Marsa Alam city or located on the western shore of the Red Sea, Red sea Governorate, Egypt. Marsa Alam is located 274 km south of Hurghada. The proposed methodology involves image collection taken into consideration the resolution of collected photographs which depend on the height of view. After that, image rectification using suitable rectification methods with different number and distributions of GCPs and CPs. Database and Geographic information systems (GIS) layers were created by on-screen vectorization based on the requirement of large scale maps. Attribute data have been collected from the field. The obtained results show that the planmetric accuracy of the produced map from Google Earth Images met map scale 10 000 according to (National Map Accuracy Standards).The collect ground truth or training samples from GE images and field help in accuracy assessment of classification process.

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information

Migrating rivers, consequent paleochannels: The unlikely partners and hotspots of flooding

Despite many studies on land degradation in the Highlands of Northern Ethiopia, quantitative information regarding long-term changes in land use/cover (LUC) is rare. Hence, this study aims to investigate the LUC changes in the Geba catchment (5142 km2), Northern Ethiopia, over 80 years (1935–2014). Aerial photographs (APs) of the 1930s and Google Earth (GE) images (2014) were used. The point-count technique was utilized by overlaying a grid on APs and GE images. The occurrence of cropland, forest, grassland, shrubland, bare land, built-up areas and water body was counted to compute their fractions. A multivariate adaptive regression spline was applied to identify the explanatory factors of LUC and to create fractional maps of LUC. The results indicate significant changes of most types, except for forest and cropland. In the 1930s, shrubland (48%) was dominant, followed by cropland (39%). The fraction of cropland in 2014 (42%) remained approximately the same as in the 1930s, while shrubland significantly dropped to 37%. Forests shrank further from a meagre 6.3% in the 1930s to 2.3% in 2014. High overall accuracies (93% and 83%) and strong Kappa coefficients (89% and 72%) for point counts and fractional maps respectively indicate the validity of the techniques used for LUC mapping.

Land cover change is a major environmental concern in the northwestern highlands of Ethiopia. This study detected land cover transitions over the past 30 years in the Chemoga basin (total area = 118,359 ha). Land cover maps were generated via the supervised classification of Landsat images with the help of the Google Earth (GE) images. A total of 218 unchanged land features sampled from GE images were used as the training datasets. Classification accuracy was evaluated by comparing classified images with 165 field observations during the 2017 field visit. The overall accuracy was 85.4% and the kappa statistic was 0.81, implying that the land classification was satisfactory. Agricultural land is the dominant land cover in the study basin, and increased in extent by 2,337 ha from 1987 to 2017. The second and third most dominant land cover types, grassland and woodland, decreased by 1.9% and 3.6%, respectively, over the past 30 years. The increase in agricultural lands was mostly due to the conversion of grasslands and woodlands, although some agricultural lands changed to Eucalyptus plantations and human settlements. The results revealed that the expansion of built-up space and agricultural lands was the major driver of fragmentation of the landscape, and degradation of natural resources in the Chemoga basin, Ethiopia.

We propose and demonstrate a fast, robust method for using satellite images to localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite images has large storage and computation costs and is unable to run in real time. In this work, we collect Google Earth (GE) images for a desired flight path offline and an autoencoder is trained to compress these images to a low-dimensional vector representation while retaining the key features. This trained autoencoder is used to compress a real UAV image, which is then compared to the precollected, nearby, autoencoded GE images using an inner-product kernel. This results in a distribution of weights over the corresponding GE image poses and is used to generate a single localization and associated covariance to represent uncertainty. Our localization is computed in 1% of the time of the current standard and is able to achieve a comparable RMSE of less than 3 m in our experiments, where we robustly matched UAV images from six runs spanning the lighting conditions of a single day to the same map of satellite images.

Recent morphological changes in the Mekong and Bassac river channels, Mekong delta: The marked impact of river-bed mining and implications for delta destabilisation

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.

Scientific results and lessons learned from an integrated crewed Mars exploration simulation at the Rio Tinto Mars analogue site