A new differential code bias (C1–P1) estimation method and its performance evaluation

The current satellite clock products are computed using the ionosphere-free phase (L1/L2) and code (P1/P2) observations. Thus, if users conduct undifferenced positioning using these clock products together with C1 and P2 observations, the differential code bias (DCB) (C1–P1) should be properly compensated. The influence of DCB (C1–P1) on the undifferenced ambiguity solutions is investigated. Based on the investigation, we propose a new DCB (C1–P1) estimation method. Using it, the satellite DCB (C1–P1) can be computed. A 30-day (DOY 205–234, 2012) dual-frequency GPS data set is processed to estimate the DCB (C1–P1). Comparing the estimated results with that of IGS DCB products, the accuracy is better than 0.13 m. The performances of DCB (C1–P1) in the code-based single-point positioning, precise point positioning (PPP) convergence and wide-lane uncalibrated phase delay (UPD) estimation are investigated using the estimated DCB (C1–P1). The results of the code-based single-point positioning show that the influence of DCB (C1–P1) on the up direction is more evident than on the horizontal directions. The accuracy is improved by 50 % and reaches to decimeter level with DCB (C1–P1) application. The performance of DCB (C1–P1) in PPP shows that it can accelerate PPP convergence through improving the accuracy of the code observation. The computed UPD values show that influence of DCB (C1–P1) on UPD of each satellite is different, and some values are larger than 0.3 cycles.     

Evaluating the performance of a new classifier – the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery

In this study, we compare three commonly used methods for hyperspectral image classification, namely Support Vector Machines (SVMs), Gaussian Processes (GPs) and the Spectral Angle Mapper (SAM). We assess their performance in combination with different kernels (i.e. which use distance-based and angle-based metrics). The assessment is done in two experiments, under ideal conditions in the laboratory and, separately, in the field (an operational open pit mine) using natural light. For both experiments independent training and test sets are used. Results show that GPs generally outperform the SVMs, irrespective of the kernel used. Furthermore, angle-based methods, including the Spectral Angle Mapper, outperform GPs and SVMs when using distance-based (i.e. stationary) kernels in the field experiment. A new GP method using an angle-based (i.e. a non-stationary) kernel – the Observation Angle Dependent (OAD) covariance function – outperforms SAM and SVMs in both experiments using only a small number of training spectra. These findings show that distance-based kernels are more affected by changes in illumination between the training and test set than are angular-based methods/kernels. Taken together, this study shows that independent training data can be used for classification of hyperspectral data in the field such as in open pit mines, by using Bayesian machine-learning methods and non-stationary kernels such as GPs and the OAD kernel. This provides a necessary component for automated classifications, such as autonomous mining where many images have to be classified without user interaction.     

Using Maximum Likelihood (ML) and Maximum A Prior Probability (MAP) in Iterative Self‐Organizing Data (ISODATA)

Abstract

With the availability of high‐resolution satellite imagery featuring as high as 1 meter resolution in the panchromatic mode and 3‐meter resolution in the multispectral mode, there is an interest by many new commercial and public service applications such as cellular telephones coverage area design, urban/land cover planning, and real estate marketing to extract features from images automatically. To that end, the demand for unsupervised classification techniques is growing. In this paper, the Maximum Likelihood (ML) and Maximum A prior Probability (MAP) algorithms are used as decision rules to find boundaries of classes computed by the Iterative Self‐Organizing Data (ISOADATA) algorithm. Different satellite images with different resolutions were used to experiment with these algorithms. The results of comparing and analyzing the algorithms revealed that MAP‐ISODATA performed better than ML‐ISODATA even when the same initial matrix was used. It was shown that there was no significant difference between ML‐ISODATA and MAP‐ISODATA in terms of accuracy. It was also realized that better results could be obtained if homogenous initialization strategies were used.     

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.     

A global,high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km² of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).     

Prioritization of Sub-watersheds in Khanapara–Bornihat Area of Assam–Meghalaya (India) Based on Land Use and Slope Analysis Using Remote Sensing and GIS

Improper utilization of natural resources without any conservation work is the prime cause of the watershed deterioration. Fast developmental activities and population pressure in the hills of Khanapara?CBornihat area near Guwahati city (about 10?km east of Guwahati) results rapid alteration of the land use/land cover in the recent times. This also causes the growth of land use over the unsuitable topography. As a result, there is a general degradation of the natural resources within the area. So, urgent measures have to be adopted to take up the conservation measure for the management of natural resources. Watershed wise conservation is considered to be the most acceptable and convenient approach. In the context of watershed management, watershed prioritization gained importance in natural resource management. The present study makes an attempt to prioritize the sub-watersheds for adopting the conservation measure. The prioritization is based on land use and slope analysis using Remote Sensing and GIS techniques in Khanapara?CBornihat area of Assam and Meghalaya state (India). The study area of 323.17?sq. km is divided into three 5th order, four 4th order and two 3rd order sub-watersheds. Land use/Land cover change analysis of the sub-watersheds has been carried out using multi temporal data of SOI toposheets of 1972 and IRS LISS III imagery of 2006. The study shows the significance changes in land use pattern especially in settlement and forest lands from 1972 to 2006. Slope map of the sub-watersheds prepared from the contour values in the toposheets show the wide variation of slope in the area ranging from 0° to 87°. Based on the extent/nature of land use/land cover changes over time and land use/land cover??slope relationship analysis, the sub-watersheds are classified into three categories as high, medium and low in terms of priority for conservation and management of natural resources.     

Rescue of the historical sea level record of Marseille (France) from 1885 to 1988 and its extension back to 1849–1851

This paper describes the historical sea level data that we have rescued from a tide gauge, especially devised originally for geodesy. This gauge was installed in Marseille in 1884 with the primary objective of defining the origin of the height system in France. Hourly values for 1885–1988 have been digitized from the original tidal charts. They are supplemented by hourly values from an older tide gauge record (1849–1851) that was rediscovered during a survey in 2009. Both recovered data sets have been critically edited for errors and their reliability assessed. The hourly values are thoroughly analysed for the first time after their original recording. A consistent high-frequency time series is reported, increasing notably the length of one of the few European sea level records in the Mediterranean Sea spanning more than one hundred years. Changes in sea levels are examined, and previous results revisited with the extended time series. The rate of relative sea level change for the period 1849–2012 is estimated to have been \(1.08\pm 0.04\)  mm/year at Marseille, a value that is slightly lower but in close agreement with the longest time series of Brest over the common period ( \(1.26\pm 0.04\)  mm/year). The data from a permanent global positioning system station installed on the roof of the solid tide gauge building suggests a remarkable stability of the ground ( \(-0.04\pm 0.25\)  mm/year) since 1998, confirming the choice made by our predecessor geodesists in the nineteenth century regarding this site selection.     

Monitoring of deforestation and land use changes (1925–2012) in Idukki district,Kerala, India using remote sensing and GIS

The studies on forest cover change can reveal the status of forests and facilitate for its conservation planning. Idukki is the largest district in the state of Kerala having a total geographical area of 5019 km². The objectives of the present study are to map forest cover in Idukki district using multi-temporal remote sensing data (1975, 1990, 2001 and 2012) and topographical maps (1925), to analyze the trends in deforestation and land use changes. Overall statistics for the period of 1925 indicate that about 4675.7 km² (93.2 %) of the landscape was under forest. The forest cover in 2012 was estimated as 2613.4 km² (52.1 %). Recently, due to the implementation of policies and protection efforts, the rate of deforestation was greatly reduced. The commencement of hydroelectric projects during 1925–1990 responsible for an increase of area under water bodies by inundating other land uses. The long term analysis shows agricultural area been decreasing and commercial plantations been increasing in the district. There has been a significant increase in the area of plantations from 1236.2 km² (1975) to 1317.3 km² (2012).     

Deforestation and fragmentation of seasonal tropical forests in the southern Yucatán,Mexico (1990–2006)

The southern Yucatán (SY) has been recognized as a hotspot of biodiversity with great risk of deforestation. Land change analysis, based on classified Landsat TM and ETM?+?satellite imagery (1990, 2000 and 2006), was used to estimate the annual deforestation rates of 141 land management units of the SY, and spatial patterns of forest fragmentation around and within the Calakmul Biosphere Reserve (CBR), which comprises approximately one-third of the region. Results indicate a decrease in annual deforestation rates over 1990–2006, from 0.15% year^?1 to 0.06% year^?1, but with significant sub-regional variations in the quantity and rate of forest loss. Despite a decline in deforestation during this period, there was considerable fragmentation both inside and outside the CBR. While population pressures and the expansion of pasture have caused deforestation across the region, agricultural intensification, diversified income strategies and reserve conservation may have contributed to reduced forest loss during the study period.     

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.     

Variations in annual snowline and area of an ice-covered stratovolcano in the Cordillera Ampato,Peru, using remote sensing data (1986–2014)

This research focuses on the recent variations in the annual snowline and the total glaciated area of the Nevado Coropuna in the Cordillera Ampato, Peru. Maximum snowline altitude towards the end of dry season is taken as a representative of the equilibrium line altitude of the year, which is an indirect measurement of the annual mass balance. We used Landsat and IRS LISS3 images during the last 30 years due to its better temporal coverage of the study site. It is found that there was a decrease of 26.92% of the glaciated area during 1986–2014. We calculated the anomalies in precipitation and temperature in this region and also tried to correlate the changes in glacier parameters with the combined influence of El Niño – Southern Oscillation (ENSO) and pacific decadal oscillation (PDO). It is concluded that the snowline of Nevado Coropuna has been fluctuated during ENSO, and maximum fluctuations were observed when ENSO and PDO were in phase.     

Effect of linear and non-linear mixing on hyper-spectral signatures of snow in the optical region (350–2500 nm)

Study of hyper-spectral behaviour of snow is important to interpret, analyse and validate optical remote sensing observations. To map and understand response of snow-mixed pixels in RS data, field experiments were conducted for linear mixing of external materials (i.e. Vegetation, Soil) with snow, using spectral-radiometer (350–2500 nm). Further, systematic non-linear mixing of snow contaminants (soil, coal, ash) in terms of size and concentration of contaminants is analysed to imitate and understand spectral response of actual field scenarios. Sensitivity of band indices along with absorption peak characteristics provide clues to discriminate the type of contaminants. SWIR region is found to be useful for discriminating size of external contaminants in snow e.g. Avalanche deposited snow from light contaminated forms. Present research provide inputs for mapping snow-mixed pixels in medium/coarse resolution remote sensing RS data (in terms of linear mixing) and suitable wavelength selections for identification and discriminating type/size of snow contaminants (in terms of non-linear mixing).     

Land use and land cover changes over a century (1914–2007) in the Neyyar River Basin,Kerala: a remote sensing and GIS approach

Abstract

Land use and land cover change, perhaps the most significant anthropogenic disturbance to the environment, mainly due to rapid urbanization/industrialization and large scale agricultural activities. In this paper, an attempt has been made to appraise land use/land cover changes over a century (1914–2007) in the Neyyar River Basin (L=56 km; Area = 483.4 km²) in southern Kerala – a biodiversity hot spot in Peninsular India. In this study, digital remote sensing data of the Indian Remote Sensing satellite series I-D (LISS III, 2006–2007) on 1:50,000 scale, Survey of India (SOI) toposheet of 1914 (1:63,360) and 1967 (1:50,000) have been utilized to map various land use/land cover changes. Maps of different periods have been registered and resampled to similar geographic coordinates using ERDAS Imagine 9.0. The most notable changes include decreases in areas of paddy cultivation, mixed crops, scrub lands and evergreen forests, and increases in built-up areas, rubber plantations, dense mixed forests, and water bodies. Further, large scale exploitation of flood plain mud and river sand have reached menacing proportions leading to bank caving and cut offs at channel bends. Conservation of land and water resources forms an important aspect of ecosystem management in the basin.     

Nighttime light data-based analysis of spatiotemporal patterns evolution and driving forces of economic development in China at prefecture level (1992–2020)

Intercity lighting data are an important resource for studying spatial and temporal patterns in regional urban development as an indicator of the intensity of urban social and economic activity. Understanding the evolutionary characteristics of the spatial pattern of regional economic development can support decision-making in regional economic coordination and sustainable development strategies. Based on a long time series of nighttime lighting data from 1992 to 2020, this study used the Theil index, Markoff transfer matrix, spatial autocorrelation, and spatial regression to analyze spatiotemporal evolutionary characteristics and drivers of urban economic development in China. The study found that from 1992 to 2020, China's economic development hot spots have been concentrated in highly developed urban agglomerations namely the Beijing–Tianjin–Hebei region, Shandong Peninsula, Yangtze River Delta, and Pearl River Delta. Cold spots were mainly concentrated in the central-west and southwest of the country. The economic growth rate shows an opposite spatial pattern, which demonstrates the effectiveness of the national coordinated development strategy for regions. The Theil index for urban economic development in China shows an overall downward trend, and the overall economic disparity is mainly due to the relatively low economic development of Tibet, Xinjiang, Gansu, and other western provinces. Therefore, regional economic development remains significantly uneven. In China, the economic type of cities is relatively stable, and the probability of leapfrogging types is low; however, the level of cities with high resource dependence or a single economic structure easily downgrades. The level of economic development and the related socioeconomic factors of neighboring cities influence an obvious spatial spillover effect in the development of urban economies in China. The pattern of China's urban economic development is mainly affected by innovation capacity, financial support, capital investment, transportation infrastructure, and industrial structure.     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal,South Africa

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.