Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.

Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.

Study of Urban Sprawl and Its Impact on Vegetation, Land Surface Temperature and Air Pollution Using Remote Sensing and GIS in Kathmandu Valley from 2015 to 2020

The Kathmandu Valley has seen substantial urbanization over the past decades while being the nation’s economic centre. Built-up areas have expanded quickly along with the population, having a significantly negative influence on the environment. Recently, Kathmandu was named as the most polluted city in Asia. Urban sprawl has had a negative influence on Kathmandu’s residents in several ways. The state of urban sprawl and the effects it has had on the Kathmandu Valley have been examined using land sat imagery. In this study, IDW was used in GIS to analyze the pollution status using data of PM 2.5 and PM 10 obtained from various monitoring sites. A supervised classification was used to create a LULC map of Kathmandu for the years 2015, 2018, and 2020. To assess the state of the vegetation and determine whether the Kathmandu Valley is being affected by urban heat, NDVI and Land sat temperature calculations were also made. The study’s results were obtained using remote sensing and GIS technology. The built-up area in Kathmandu Valley has grown by 20% over the past five years, impacting land use patterns and deteriorating vegetation cover. Due to the rise of built-up area, which is a good heat absorber, the temperature in the Kathmandu Valley is rising along with the degradation of the vegetation cover. The pollution in the Kathmandu Valley is at its worst, and residents are compelled to breathe air that is significantly more polluted than the prescribed limit.

An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

Improved Geological Interpretation Using Landsat TM Data in Lancang-Jinghong Area, Yunnan Province, China

Landsat TM digital spectral data of Lancang Jinghong area (Yunnan P ro vince) has been used for the purpose of geological interpretation. To meet this object, different image processing techniques including selected band color comp osites, principal component analysis and IHS decorrelation stretching are used t o improve the discrimination of different lithological and structural features i n the area.It was found that IHS decorrelation stretching images obtained from t he transformation of false color composite 741 (in red, green and blue) prov ided the best results based on the original data.By combining the characteristic s of images produced by different approaches and other canonically transformed i mages with available geological data and surface observations, the geological in terpretation could be done with satisfactory degree of accuracy.

Erosion hotspot identified along the sandy coast of Shanwei: characteristics and origin

Based on the measured beach profile data of Sanzhou Bay from 2015 to 2019,an erosion hotspot was identified along the Shanwei coastline of eastern Guangdong,where the maximum retreat distance of the shoreline exceeded 80 m and the erosion rate was more than 20 m/a.To determine the time at which the erosion hotspot started and the potential causes of its formation,this study used 63 Landsat satellite images from 1986 to 2019 to construct a time series of shoreline positions over the past 30 years by extracting their high-tide shorelines.Next,the M-K trend test method was introduced to evaluate the non-linear shoreline behavior based on the single-transect method.The results showed that the time of approximately 2013 marked the start of the erosion hotspot,the erosion hotspot was characterized by erosion rates of more than 2 m/a(a maximum rate of 31.6 m/a),and the affected shoreline more than 4.3 km from 2013 to 2019.Furthermore,this erosion hotspot was proved to be caused by artificial sand mining in the nearshore zone,which destroyed the original beach’s morphodynamic equilibrium.With the aid of storm events,soil cliffs composed of loose sediment on the backshore were sacrificed to achieve a new equilibrium,resulting in an extremely significant retreat parallel to the coast on the west side of the study area,which reflects the combined effect of human and natural processes.This study provides a concrete example of the rapid response of shorelines to artificial sand mining activities,and the associated finding is a stark warning about the cautious development and utilization of coastal zones and the strict regulation of human activities.

Lineament analysis as a seismic precursor:The El Mayor Cucapah earthquake of April 4,2010(MW7.2),Baja California,Mexico

An earthquake called the MW7.2“El Mayor Cucapah”earthquake on April 4,2010 has been analyzed for seismic precursor.The changes in the lineament system concerning its pattern and time intervals were analyzed during the earthquake preparation period and occurrence using the automated lineament detection method.The Landsat 5 TM images were processed using LESSA and ADALGEO software obtaining similar results.The statistical analysis revealed the stress accumulation due to plate interaction during earthquake formation.The study shows that the number of extracted lineaments changes rapidly about 23 months before the earthquake,and the systems return to the initial stage after 23 months.Most lineaments coincide with the extension of the San Andreas Fault as NW direction is the dominant trend.Thus,it can be concluded that the featural changes within the Rose diagram corresponding to the different strokes direction along with oriented elongation lines as disclosed in the present study using satellite images could be identified as a mid-term and/or short-term precursors of the earthquake.However,even though the dynamism of the El Mayor Cucapah earthquake is found in the extracted lineaments,it is possible to isolate more significant earthquakes even if new ones appear near the zone.Moreover,using two algorithms for lineament detection allows for the tectonics to corroborate the obtained lineaments and dynamism.

Spatio-temporal changes in rice area at the northern limits of the rice cropping system in China from 1984 to 2013

Rice area has been expanding rapidly during the past 30 years under the influence of global change in northeastern China, which is the northernmost region of rice cultivation in China. However, the spatio-temporal dynamic changes in rice area are still unclear, although they may have important policy implications for environmental protection and adaptation to climate change. In this study, we aimed to identify the dynamic changes of the rice area in Heilongjiang Province of northeastern China by extracting data from multiple Landsat images. The study used ground quadrats selected from Google Earth and the extraction of a confusion matrix to verify the accuracy of extraction. The overall accuracy of the extracted rice area was higher than 95% as a result of using the artificial neural network （ANN） classification method. The results showed that the rice area increased by approximately 2.4×10^6 ha during the past 30 years at an annual rate of 8.0×10^4 ha, and most of the increase occurred after 2000. The central latitude of the rice area shifted northwards from 46 to 47°N during the study period, and moved eastwards from 130 to 133°E. The rice expansion area accounted for 98% of the total change in rice area, and rice loss was notably rare. The rice expansion was primarily from dryland. In addition, rice cultivation in marshland and grassland played a minor role in the rice expansion in this region.

Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)

Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats.

A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

Land use change characteristics affected by water saving practices in Manas River Basin,China using Landsat satellite images

The characteristics and influencing factors of land use change under arid conditions were studied in the Manas River Basin in Xinjiang Region,Northwest China.Landsat satellite images acquired in 1976,1990,2000,2010 and 2015 over the study area were used as basic data.Land use change,the rate of change of land use,land use transfer and other aspects revealed the characteristics of land use change and related factors as influenced by water conditions in the basin.The results showed that:(1)Over nearly 50 years,land reclamation in the Manas River Basin resulted in the rapid expansion of an artificial oasis area,and promoted the process of‘oasis urbanization’,and accelerated the development of the river basin economy.(2)In 2000,the popularization of drip irrigation under mulch technology led to the rapid growth of cultivated land and development land in the watershed.Meanwhile,the water table declined in the desert area of the lower reaches of the river basin,and the area occupied by sparse shrub forest and grassland decreased.(3)Before popularization of water-saving technology,woodland,grassland and development land transformed to cultivated land in the amounts of 93.46 km^(2),2542.93 km^(2) and 137.53 km^(2),respectively,and woodland transformed in the amount of 189.64 km^(2).After water-saving technology was popularized,woodland,grassland and development land were transformed into cultivated land in the amounts of 567.41 km^(2),1756.2 km^(2) and 37.36 km^(2),respectively.(4)The popularization of water-saving technology made the dynamic degree of cultivated land and development land more active,and further increased landscape fragmentation and landscape heterogeneity.The level of urbanization development,the level of economic development and the dry humidity of the basin became the main factors affecting the change of land use in the basin.

Characterizing regional precipitation-driven lake area change in Mongolia

Lake area is an important indicator for climate change and its relationship with climatic factors is critical for understanding the mechanisms that control lake level changes. In this study, lake area changes and their rela- tions to precipitation were investigated using multi-temporal Landsat Thermatic Mapper （TM） and Enhanced Thermatic Mapper plus （ETM＋） images collected from 10 different regions of Mongolia since the late 1980s. A lin- ear-regression analysis was applied to examine the relationship between precipitation and lake area change for each region and across different regions of Mongolia. The relationships were interpreted in terms of regional climate regime and hydromorphological characteristics. A total of 165 lakes with areas greater than 10 hm2 were identified from the Landsat images, which were aggregated for each region to estimate the regional lake area. Temporal lake area variability was larger in the Gobi regions, where small lakes are densely distributed. The regression analyses indicated that the regional patterns of precipitation-driven lake area changes varied considerably （R2=0.028-0.950）, depending on regional climate regime and hydromorphological characteristics. Generally, the lake area change in the hot-and-dry Gobi regions showed higher correlations with precipitation change. The precedent two-month pre- cipitation was the best determining factor of lake area change across Mongolia. Our results indicate the usefulness of regression analysis based on satellite-derived multi-temporal lake area data to identify regions where factors other than precipitation might play important roles in determining lake area change.

Delineating suspended sediment concentration patterns in surface waters of the Changjiang Estuary by remote sensing analysis

Three Landsat TM imageries （taken on 18 May 1987,4 August 1998 and 28 July 2007） were used as the data source to identify the spatial and temporal variations of the suspended sediment concentration （SSC） in surface waters of the Changjiang Estuary.Atmospheric correction was carried out to determine the water-leaving reflectance using the FLAASH module.A regression equation between surveyed SSC and suspended sediment index was chosen to retrieve the SSC from the Landsat TM images.In addition,tidal harmonic analysis was performed to calculate tidal conditions corresponding to the acquisition time of satellite images.The results show that the SSC spatial patterns are similar to the in situ observation results,which show the highest SSC in the region of turbidity maximum zone in the Changjiang Estuary.For the period of 1987 to 2007,the SSC pattern is controlled mainly by tidal dynamic conditions and wind speeds,rather than sediment discharges from the river.

Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System

The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia, which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20,448 (ha) whereas other land covers, such as green area, have decreased significantly by 14,554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.

Injecting spectral indices to transferable convolutional neural network under imbalanced and noisy labels for Landsat image classification

Stable and continuous remote sensing land-cover mapping is important for agriculture,ecosystems,and land management.Convolutional neural networks(CNNs)are promising methods for achieving this goal.However,the large number of high-quality training samples required to train a CNN is difficult to acquire.In practice,imbalanced and noisy labels originating from existing land-cover maps can be used as alternatives.Experiments have shown that the inconsistency in the training samples has a significant impact on the performance of the CNN.To overcome this drawback,a method is proposed to inject highly consistent information into the network,to learn general and transferable features to alleviate the impact of imperfect training samples.Spectral indices are important features that can provide consistent information.These indices can be fused with CNN feature maps which utilize information entropy to choose the most appropriate CNN layer,to compensate for the inconsistency caused by the imbalanced,noisy labels.The proposed transferable CNN,tested with imbalanced and noisy labels for inter-regional Landsat time-series,not only is superior in terms of accuracy for land-cover mapping but also demonstrates excellent transferability between regions in both time series and cross-regional Landsat image classification.

Remote sensing study of wetlands in the Pearl River Delta during 1995-2015 with the support vector machine method

In recent years, experienced rapid economic the Pearl River Delta has growth which may create a substantial burden to its ecology. In this study, the wetlands of the Pearl River Delta are investigated. Through the use of remote sensing methods, we analyze spatial and temporal variations of wetlands in this area over the past twenty years. The support vector machine （SVM） method is proven to be an effective approach for classifying the wetlands of the Pearl River Delta, and the total classifica- tion resolution reaches 94.94% with a Kappa coefficient exceeding 0.94, higher than other comparable analysis methods. Our results show that wetland areas were reduced by 36.9% during the past twenty years. The change detection analysis method shows that there was a 95.58% intertidal zone change to other land-use types, most of which （57.12%） was converted to construction land. In addition, farmland was reduced by 54.89% during the past twenty years, 47.19% of which was changed to construction land use. The inland water area increased 19.02%, but most of this growth （18.77%） was converted from the intertidal zone.