Classification of multi‐spectral remote sensing data using a local transfer function classifier

This paper introduces a new neural network, called the local transfer function classifier (LTF‐C), for classification of multi‐spectral remote sensing data. The network structure of LTF‐C is similar to that of the radial basis function neural network (RBF), but LTF‐C utilizes an entirely different learning algorithm. In particular, the network structure of LTF‐C is not predetermined, but changes dynamically during the learning. Such a learning algorithm fits well to the classification problem, and guarantees that the size of the network is as large as is needed. The classification results show that LTF‐C evidently has a better classification accuracy than the six other classifiers in the experiment.     

Analysis of urban growth using multi‐temporal satellite data in Istanbul,Turkey

Uncontrolled population growth, especially in developing countries, causes serious problems, such as scarcity of food, informal settlements, environmental pollution, destruction of ecological structure, unemployment, etc. This phenomenon will require advanced methodologies, such as space technologies, to enable city planners, economists, environmentalists, ecologists and resource managers to solve these problems. In Turkey, as a result of the undesired population growth, new settlements are continuously appearing and adverse developments and changes are occurring in the presently populated areas. In Turkey's major cities, such as in Istanbul, Izmir, Ankara, Adana, Bursa and Antalya, which have seen mass migrations of people, considerable urban developments and changes have occurred. Consequently, the mostly negative impacts of uncontrolled population growth on the urban environment must be monitored continuously.

The objective of this study was to investigate the impact of urban growth on land‐use changes, especially the agricultural land in the district of Büyükçekmece in suburban Istanbul. The study is based on the 1984–1997 population database, multi‐temporal satellite data and remote sensing methods. The study revealed significant loss of agricultural and natural land areas to urban developments throughout the period 1984–1998.     

Changes in the glaciers of Chandra–Bhaga basin,Himachal Himalaya,India, between 1980 and 2010 measured using remote sensing

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km² in 2010 from 377.6 km² in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year^?1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km² in 1980 to (1.26 ± 0.03) km² in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.     

Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District,Vietnam

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule‐based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land‐cover classes. The proposed approach aims to minimize commission errors in the post‐classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.     

Monitoring cropland changes along the Nile River in Egypt over past three decades (1984–2015) using remote sensing

The Nile River basin is the main agricultural area in Egypt. In recent decades, human activities and climate change have remarkably influenced the ecological environment there. Those changes have caused land degradation, sea level rise, and conflicts between land and population, threatening the agricultural system and food security of Egypt. In this study, cropland mapping along the Nile in Egypt over the past three decades (1984–2015) was conducted at annual frequency, using 961 Landsat TM/ETM+/OLI images. Spectral features of selected growing season images and band ratio-based indices were used in supervised classification. Thereafter, terrain and time series information were used to filter possible classification errors on the basis of logical judgment and statistical analysis. The average overall classification accuracy of cropland was greater than 90%. Furthermore, temporal and spatial characteristics of cropland expansion were analysed. The results highlight the annual geographical distribution of cropland dynamics from the Nile Valley to desert. In total, cropland areas had increased by 33.7% from 2848.1 kha in 1984 to 3807.8 kha in 2015, with an annual average increase of 31.0 kha in these 32 years.     

Uncertainty analysis of flash‐flood system using remote sensing and a geographic information system based on GcIUH in the Yeongdeok Basin,Korea

The current paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub‐basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 min, considering rainfall duration, peak discharge and flooding in the Yeongdeok basin.     

Coral spawn and bathymetric slicks in Synthetic Aperture Radar (SAR) data from the Timor Sea,north‐west Australia

Annular‐ to crescent‐shaped low backscatter SAR slicks over carbonate reefs and shoals in the Timor Sea that exhibit ‘feathering’, and within the coral spawning period for the region, are interpreted to be caused by a coral spawn event. This represents the first time that such an event has been interpreted through satellite remote sensing. Additionally, ocean current data and detailed swath bathymetry of the sea floor to the south‐east of the coral spawn slicks suggest that elongate repeating slicks in this area are related to current flow over submarine channels. Assessment of these slicks in association with ancillary data, such as bathymetry, current velocities, weather, and timing of SAR acquisition allow a more robust interpretation of their origins. Through differentiating coral spawn and bathymetric slicks from oil and other biological slicks in shallow carbonate systems, such as the Timor Sea, petroleum and environmental assessments for these areas can be improved.     

Assessment of the impact of coastal reclamation activities on seagrass meadows in Sungai Pulai estuary,Malaysia, using Landsat data (1994–2017)

Seagrass meadows play a critical role in supporting coastal biodiversity and in providing food and habitats for other marine organisms but are continuously threatened by human activities, such as coastal reclamation in the maritime countries. The Sungai Pulai estuary harbours mangroves, mudflats, and shoals with one of the Malaysia’s largest seagrass meadow (Merambong, Tanjung Adang, and Tanjung Laut) in Johor, Malaysia. This study assessed the environmental impact of coastal reclamation activities based on Landsat imagery (1994–2017) through mapping cover and distribution changes of those seagrass meadows by using a previously developed image enhancement technique with further improvement in the classification scheme (87% overall accuracy). The image-difference maps showed changes in seagrass meadows and mudflat cover with the extension of reclaimed land. Some seagrass meadows experienced large-scale changes, and coastal reclamation activities have been suggested as main factors responsible for habitat degradation, reduction in coverage, and total loss due to physical damage and excessive sedimentation. Sufficient protection of on-site construction materials is essential if coastal areas are to conserve seagrass meadows. This Landsat-based image classification approach should help the coastal manager to map seagrass meadows and to monitor the environmental impact of reclamation activities with a large spatio-temporal scale.     

Cover: Observing estuarine currents and fronts in the Tay Estuary,Scotland, using an airborne SAR with along‐track interferometry (ATI)

Estuaries are extremely dynamic environments where large and frequent changes in bathymetry and channel locations can occur. Because estuaries are major centres of population and industry, there is an ongoing requirement to monitor and predict changes in the current fields. The tidal range, surface wind speed, atmospheric pressure, fresh water inflow and most importantly the stage of the tidal cycle affect the flow vectors. Existing boat‐based methods are unable to provide measurements of current fields with sufficient spatial and depth coverage for accurate modelling of hydrodynamic processes in estuaries. Remotely sensed data offer more extensive, synoptic, spatial coverage. However, previous studies to map the full details of the current field based on conventional optical and thermal imaging have been limited by insufficient temporal coverage and the lack of identifiable features that can be tracked. Synthetic aperture radar (SAR) imaging with along‐track interferometry (ATI) has the potential to overcome both of these limitations because it can retrieve quantitative measurements of sea surface state parameters and instantaneous surface flow from a single pass over a whole estuary. The preliminary results of ATI observations over the Tay Estuary, Scotland, validated with coincident in situ boat based observations, are presented here.