Medium Resolution Satellite Imagery Research Articles

A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery

ABSTRACT Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use and land use change. However, detecting and monitoring scattered trees with an open canopy using medium-resolution satellites is difficult because individual trees often cover a smaller footprint than the satellites’ resolution. Additionally, the variable background land uses and canopy shapes of trees cause a high variability in their spectral signatures. Here we present a globally consistent method to identify trees with canopy diameters greater than 3 m with medium-resolution optical and radar imagery. Biweekly cloud-free, pan-sharpened 10 metres Sentinel-2 optical imagery and Sentinel-1 radar imagery are used to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. Tested across more than 215 thousand Sentinel-1 and Sentinel-2 pixels distributed from – 60 to +60 latitude, the proposed model exceeds 75% user’s and producer’s accuracy identifying trees in hectares with a low to medium density () of tree cover, and 95% user’s and producer’s accuracy in hectares with dense () tree cover. In comparison with common remote-sensing classification methods, the proposed method increases the accuracy of monitoring tree presence in areas with sparse and scattered tree cover () by as much as 20%, and reduces commission and omission error in mountainous and very cloudy regions by nearly half. When applied across large, heterogeneous landscapes, the results demonstrate potential to map trees in high detail and consistent accuracy over diverse landscapes across the globe. This information is important for understanding current land cover and can be used to detect changes in land cover such as agroforestry, buffer zones around biological hotspots, and expansion or encroachment of forests.

Shoreline Evolution Between Al Lith and Ras Mahāsin on the Red Sea Coast, Saudi Arabia Using GIS and DSAS Techniques

A large proportion of the world's population lives in coastal areas. These zones play a vital role in the socioeconomic aspects of coastal communities. The evolution of shoreline along the zones is of great importance to scientists, and engineers as well as coastal management. This research aims to assess and understand the dynamics of shoreline along the Red Sea coast between Al Lith and Ras Mahāsin, using medium-resolution satellite imagery over 34 years (1984–2018) as well as to predict futuristic changes in the shoreline position until 2038. The tasseled cap transformation tools in ArcGIS 10.2 are used to extract shoreline position for seventh intervals time. These shorelines are analyzed by the digital shoreline analysis system in four statistical functions, namely (EPR, LRR, NSM, and LMS). The endpoint rate is used to predict futuristic shoreline positions. The results reveal that the evolutions of shoreline in the form of erosion and accretion patterns, as surface area exceed 12.3 and 0.89 km2, respectively. The evolution rates were classified based on LRR into five classes: [i.e., − 11.84 to − 7.85 (very high erosion), − 7.85 to − 4.17 (moderate erosion), − 4.17 to 0.00 (low erosion), 0.00 to 1.28 (low accretion), and 1.280 to 14.44 (high accretion)]. These changes are attributed to the impact of the extreme wave action and the littoral drifts of sediments by longshore currents. The prediction model reveals that a large portion of the coastal zone is vulnerable to a high rate of shoreline disintegration.

Investigating the Reliability of Irrigated Area Coverages from Fragmented Lands: The case of Tigray, Northern Ethiopia.

Consistent and well-timed information regarding the extent of irrigated areas is very important for the development of the agricultural sector. This study investigated the reliability of traditional estimates of irrigated area coverages from three micro-dam irrigation schemes. High resolution Google Earth (GE) and medium resolution satellite imagery coupled with an intensive ground survey were employed to determine irrigation area coverages. Landsat-8 image pre-processing and classification was carried out in ENVI 5.0 software. In comparison to the Landsat-8 based estimates, the traditional method over-estimated 87% of the total 14 cases considered. The level of discrepancies in scheme area coverages range from a minimum of -20% (underestimated) to a maximum of 224%. Besides this, the traditional estimates were ranging from -1.8 to 238% of GE-based estimates. Whereas, the Landsat-8 based estimates slightly overestimated it by 1.8% -16% of GE-based estimates. On the other hand, the traditional estimates tend to underestimate/ignore crops with smaller area coverages and overestimate crops with relatively larger area coverages. The results hinted that the using medium and low resolution satellite imagery could improve the data collection and reporting from irrigated agriculture in developing countries.

A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

An examination of forest cover change at Angkor, Cambodia, using satellite imagery, interviews and interpretation of historical events

In the three decades since the mid-1970s through to the early 2000s forest cover at the Angkor World Heritage Site, Cambodia has not progressed linearly but in response to different historical events. This research uses multiple methods in comparing satellite image classification with key informant interviews and other forms of data at the landscape scale and at case studies to provide an historical perspective on forest cover change. The earliest date corresponds to the beginning of the civil war in Cambodia through to a time when a number of events had contributed to significant change in forest pattern and extent at Angkor. The change observed is a result of regulatory controls on land use, ongoing subsistence resource use activities and other events and processes, and more broadly reflects a country in transition and a history of conflict relating to resource governance. The research demonstrates that multiple methods combining medium resolution satellite imagery with interview data and processes detectable at specific time periods can be used to understand patterns of forest change and establish links to processes on the ground.

Monitoring Perubahan Pola Alur Sungai Menggunakan Citra Satelit Resolusi Spasial Menengah Berbasis Spectral Classification

The development of remote sensing technology allows humans to acquire and process data remotely and temporally. Changes in the flow of the Progo river from the last few years are significant, this can be caused by natural factors and human factors. The influence of the intensity of flow and the level of sedimentation in the Progo river causes a massive flow pattern change in the Progo river body. The data used in this research is Medium Spatial Resolution Satellite Imagery, Landsat 5 satellite imagery acquired in 1995 and Landsat 8 acquired in 2017. Monitoring of changes in river flow pattern is generally done by using the method of terrestrial or conventional measurement, which takes a long time, for that the use of methods and remote sensing data can be used to save time. The method used is multispectral classification approach with maximum likelihood algorithm. The results of extraction using digital classification method (maximum likelihood) resulted in the appearance of flow pattern quickly and representative, so this method is suitable for the purpose of rapid detection of changes in flow pattern. The results obtained from the extraction of the Progo river flow pattern show an intricate river flow pattern with many river rubbers on the image appearance of 1995, whereas in the image extraction results in 2017 the river banks and turns do not look dominant, it shows that erosion and sedimentation activities continue to occur massively.

Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta, China

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m2). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.

Pushing satellite imagery to new depths: Seascape feature mapping in a tropical shelf

Continental shelves are areas of extreme environmental and economic importance and priority areas for management and planning efforts. However, there is a knowledge gap regarding many tropical shelves, mainly due to high costs associated with surveying large marine areas with traditional techniques. Carbonatic, sediment-starved shelves are distributed across tropical and temperate regions and display characteristics that may allow the application of remote sensing methods, albeit the depth range found in these areas. Thus, this study tested the applicability of Landsat-8 imagery to detect bottom features in a sediment-starved shelf in North-eastern Brazil. The Brazilian Northeast Continental Shelf harbours a variety of geohabitats, including coral and algal reefs, beachrocks, mesophotic reefs, canyons, and paleochannels. Nine scenes were processed following the same steps: radiometric and atmospheric correction, masking, glint removal, water column correction and individual image adjustments. Two scenes were deemed best to portray targeted features. We detected 14 paleochannels in a 9700 km2 area, as well as the shelf break. A pixel-by-pixel analysis using all available bathymetric field data (n = 42,373 pixels) showed that 92.38% of the pixels were accurately classified. Misinterpreted pixels were either due to bordering regions incorrectly identified as paleochannels (3.99%) or paleochannels’ sections undetected in our final mosaic (3.62%). The final mosaic allowed a detailed view of submerged valleys (including previously unmapped channels) distributed up to areas deeper than 100 m using free of charge, medium-resolution satellite imagery. Some of these channels are known fishing grounds and fish spawning aggregation areas, making them priority zones for management and conservation efforts. To the best of our knowledge, this represents the first study applying Landsat imagery to map paleochannels in submerged areas, and most importantly, in areas deeper than 30 m. We believe this research can help focusing future mapping efforts and may advance the general knowledge of the area.

35 Years of Vegetation and Lake Dynamics in the Pechora Catchment, Russian European Arctic

High-latitude regions are a hot spot of global warming, but the scarce availability of observations often limits the investigation of climate change impacts over these regions. However, the utilization of satellite-based remote sensing data offers new possibilities for such investigations. In the present study, vegetation greening, vegetation moisture and lake distribution derived from medium-resolution satellite imagery were analyzed over the Pechora catchment for the last 35 years. Here, we considered the entire Pechora catchment and the Pechora Delta region, located in the northern part of European Russia, and we investigated the vegetation and lake dynamics over different permafrost zones and across the two major biomes, taiga, and tundra. We also evaluated climate data records from meteorological stations and re-analysis data to find relations between these dynamics and climatic behavior. Considering the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI) in the summer, we found a general greening and moistening of the vegetation. While vegetation greenness follows the evolution of summer air temperature with a delay of one year, the vegetation moisture dynamics seems to better concur with annual total precipitation rather than summer precipitation, and also with annual snow water equivalent without lag. Both NDVI and NDMI show a much higher variability across discontinuous permafrost terrain compared to other types. Moreover, the analyses yielded an overall decrease in the area of permanent lakes and a noticeable increase in the area of seasonal lakes. While the first might be related to permafrost thawing, the latter seems to be connected to an increase of annual snow water equivalent. The general consistency between the indices of vegetation greenness and moisture based on satellite imagery and the climate data highlights the efficacy and reliability of combining Landsat satellite data, ERA-Interim reanalysis and meteorological data to monitor temporal dynamics of the land surface in Arctic areas.

Preliminary Study on the use of Sentinel-2A Image for Mapping of Dry Marginal Agricultural Land

The availability of medium resolution satellite imagery (i.e. Sentinel-2A) provides the rapid, low-cost and more accurate mapping. This report presents the use of satellite imagery (Sentinel-2A) for mapping of marginal Agricultural Land in the eastern part of Situbondo Regency. The study area covers three (3) districts, i.e., Arjasa, Jangkar, and Asembagus. This study uses two methods of image classifications (i.e., unsupervised and supervised). Sentinel-2A images for dry seasons of 2018 use for this study. The dry season of this region usually occurs from April to November. Then, 450 ground control point for training areas collected during the fields surveys between June until Octobre 2019. This study also uses multi-band (i.e., 2,3,4,5 and 8A) of the sentinel 2a image. Image treatments use “ Multispect” and SNAP, two open-source image processing software. The procedures include image enhancement, registration, clipping, and classification. The classification consists of pre-processing, processing and post-processing tasks. Then, classification results evaluated by confusion-matrix (overall and kappa accuracy). Furthermore, the thematic maps produce from both unsupervised and supervised classification are then compared to existing thematics maps and statistics data. The unsupervised method use iso-data algorithm and produce five (5) class of land uses, i.e., (1) forestry and plantation; (2) build-up area, (3) irrigated paddy field, (4) non-irrigated rural areas (ladang/tegalan). The unsupervised method did the overall accuracy = 79 % and kappa accuracy = 72%. The supervised methods use maximum-likelihood algorithms and produce six (6) class, i.e., (1) forestry - plantation; (2) urban or build area, (3) irrigated paddy field, (4) non-irrigated rural areas, (5) dry-marginal land and (6) water body. Supervised method provide overall accuracy = 95,8% and kappa accuracy = 93,2%. The result shows the potential use of Sentinel 2A to map dry-marginal agricultural land in the study area.

Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

Accurate crop planting area information is of significance for understanding regional food security and agricultural development planning. While increasing numbers of medium resolution satellite imagery and improved classification algorithms have been used for crop mapping, limited efforts have been made in feature selection, despite its vital impacts on crop classification. Furthermore, different crop types have their unique spectral and phenology characteristics; however, the different features of individual crop types have not been well understood and considered in previous studies of crop mapping. Here, we examined an optimized strategy to integrate specific features of individual crop types for mapping an improved crop type layer in the Sanjiang Plain, a new food bowl in China, by using all Sentinel-2 time series images in 2018. First, an automatic spectro-temporal feature selection (ASTFS) method was used to obtain optimal features for individual crops (rice, corn, and soybean), including sorting all features by the global separability indices for each crop and removing redundant features by accuracy changes when adding new features. Second, the ASTFS-based optimized feature sets for individual crops were used to produce three crop probability maps with the Random Forest classifier. Third, the probability maps were then composited into the final crop layer by considering the probability of each crop at every pixel. The resultant crop layer showed an improved accuracy (overall accuracy = 93.94%, Kappa coefficient = 0.92) than the other classifications without such a feature optimizing process. Our results indicate the potential of the ASTFS method for improving regional crop mapping.

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

Can Medium-Resolution Satellite Imagery Measure Economic Activity at Small Geographies? Evidence from Landsat in Vietnam

AbstractThis study explores the potential and the limits of medium-resolution satellite data as a proxy for economic activity at small geographic units. Using a commune-level dataset from Vietnam, it compares the performance of commonly used nightlight data and higher resolution Landsat imagery, which measures daytime light reflection. The analysis suggests that Landsat outperforms nighttime lights at predicting enterprise counts, employment, and expenditure in simple regression models. A parsimonious combination of the first two moments of the Landsat spectral bands can explain a reasonable share of the variation in economic activity in the cross-section. There is, however, poor prediction power of either satellite measure for changes over time.

Mapping Tea Plantations from Multi-seasonal Landsat-8 OLI Imageries Using a Random Forest Classifier

It is very challenging to extract tea plantations from medium-resolution satellite imageries. This paper presents a new methodological framework based on the random forest classifier for extracting tea plantations from Landsat-8 OLI imageries. Analysis is facilitated by a dataset of three Landsat-8 OLI images (spring, autumn and winter) in 2014 covering the Anji County, one of the major tea production regions in China. More specifically, in order to determine the relative importance of spectra, texture, vegetation index and seasonality on classification accuracy, we design a series of classification feature sets, including: initial feature sets for different seasons, feature selection feature sets for different seasons and multi-seasonal feature sets. The results show that the multi-seasonal feature selection set has the best feature set performance (PA = 0.88; OA = 0.92; Kappa = 0.90). Our study demonstrates that the random forest classifier is reliable and practical for extracting tea plantations from medium-resolution images. Highlights of this study are: mapping tea plantations, an important cash crop to local agriculture, in a fragmented landscape; integrating textual, vegetational, and seasonal features, which were helpful for improving tea plantation mapping accuracy in combination; taking advantage of the feature selection function of random forest, supporting high-dimensional data classification, which leads to a higher classification accuracy.

MONITORING ALPINE GLACIERS FROM CLOSE-RANGE TO SATELLITE SENSORS

Abstract. In this paper the use of different types of remote-sensing techniques for monitoring topographic changes of Alpine glaciers is presented and discussed. Close range photogrammetry based on Structure-from-Motion approach is adopted to process images recorded from ground-based and drone-based stations in order to output dense point clouds. These are then directly compared to detect local changes by mean of M3C2 algorithm, while digital elevation models are interpolated to find global ice thinning and retreat. Medium-resolution satellite imagery can be exploited to monitor the glacier evolution at lower resolution but including the development and collapse of large crevasses. A case study concerning the Forni Glacier in the Raethian Alps (Italy) is presented to demonstrate the feasibility of the proposed approach by adopting data sets collected from 2016 to 2018.

Transfer learning approach to map urban slums using high and medium resolution satellite imagery

Slums provide cheaper workforce and informal services which contribute substantially towards GDP. However, such areas, due to the high population density, sub-standard housing and lack of essential services are urban risks. The socio-physical development of such settlements has often been neglected due to poor laws and provisions in urban management and policies. One of the primary reasons for negligence has been the unavailability of slum maps to study the evolution of slums and to actively manage and contain them. Various remote sensing techniques have been utilized to answer the problem but have not produced universal solutions. In recent years, Deep Learning (DL) techniques with remote sensing have been found beneficial in comprehending the underlying structure of physical features present in the satellite imageries. This study deals with one of the Deep Learning techniques which use pre-trained convolutional networks for slum detection in Very High Resolution (VHR) and Medium Resolution (MR) satellite imagery. We created a training dataset which comprises of four classes including slums, built, green and water. We further trained the model to detect these classes in the entire city. Classification performance was evaluated for Very high and Medium Resolution imagery with the help of manually delineated slum boundaries gathered from urban local authorities of Mumbai. The Overall accuracy of 94.2 and 90.2 and kappa of 0.70 and 0.55 is obtained from VHR and MR imagery respectively. We provide a comprehensive technique for the detection of informal settlements which can be tailored and applied to any city to detect various landforms.

Remote Sensing Analysis of Seagrass Beds in Bontosua Island, Spermonde Archipelago

Research on mapping and monitoring of seagrass ecosystems using satellite imagery data is still lacking. The availability of spatial information on benthic habitat is becoming very important with the increasing awareness of environmentally based management. Various methods of analysing remote sensing information on benthic habitat have been developed and utilized, such as the application of the Lyzenga algorithm. This algorithm requires data on the variation of water depth in the coastal areas to be mapped. The purpose of this research was to study the effect of using Lyzenga algorithm on the mapping of coral reef / seagrass ecosystem by comparing the results of seabed classification from Sentinel-2a imagery processed and without using the Lyzenga algorithm. Image classification with Lyzenga algorithm is easily recognizable with the Lyzenga index value format that has been freed from the effect of water depth. In this study, benthic cover in the shallow waters around Bontosua Island, Spermonde Archipelago-Indonesia, was classified using six habitat types: aquatic, land, sand, rubble, coral and seagrass. Seagrass habitat was further classified based on percentage cover as class 4 (76-100%), class 3 (51-75%), class 2 (26-50%) and class 1 (0-25%). Based on the analysis of Sentinel-2a data, the area with seagrass cover was estimated at 31.27 Ha. Seagrass mapping using medium-resolution satellite imagery can be helpful in providing data over a more extensive area than is likely to be possible using direct observation in the field alone.

Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery.

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

Impact of urbanization on effective impervious area of Ahmedabad city in India

ABSTRACT With increasing urbanization and population pressure, the barren ground is altered from undisturbed soils to disturbed soils, which multiplies fast growth of impervious areas in cities. An impervious area is a modified surface that prevents water from infiltrating into the soil. Hydraulically connected impervious areas are denoted as effective impervious area (EIA) and hydraulically disconnected are denoted as non-effective impervious area (NEIA). The summation of above two impervious covers amounts to the total impervious area (TIA). Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. The estimation of TIA is comparatively simple as compared to the estimation of EIA, despite the fact that later is viewed as more proper for hydrologic studies. In this paper, emphasis was given on the estimation of impervious area (TIA) for the year 2000 and 2014 to scrutinize urbanization growth of Ahmedabad city in India using medium resolution satellite imageries by using geospatial techniques under the ArcGIS 10.1 platform. The more hydraulically relevant EIA is estimated using two indirect methods, i.e. empirical equations derived from the whole basin or sub-basin parameters from the estimated TIA and using an annual hydrograph methodology. A graphical demonstration of the estimated EIA and TIA for different years is illustrated to analyze the rate of urban expansion.