首页期刊导航|International journal of applied earth observation and geoinformation
期刊信息/Journal information
International journal of applied earth observation and geoinformation
International Institute for Aerospace Survey and Earth Sciences
International Institute for Aerospace Survey and Earth Sciences
1569-8432
International journal of applied earth observation and geoinformation/Journal International journal of applied earth observation and geoinformationISTPSCIAHCI
查看更多>>摘要：The aim of the paper is to examine and analyze the long-term spatial dynamics of LULC (Land Use/Land Cover) and the impact of their changes on the SUHI (Surface Urban Heat Island). The study covered the area of Bucharest Municipality, the capital of Romania and its neighboring areas, and involved a series of Landsat images, taken in the summer, and ranging from 1984 to 2016. To achieve the goal the following were accomplished: (i) a supervised image classification, done with good accuracy and (ii) a post classification change detection approach was used to assess the LULC changes; (iii) the thermal signature for each land use category was determined using LST (Land Surface Temperature); also, iv) NDVI (Normalized Difference Vegetation Index) has been used to determine a relationship between the thermal behavior and the characteristics of each land use category. The results have revealed that by 2016 there has been an increase in the built-up areas and the fallow land areas and a decrease in the croplands and forested areas. Also, there were observed the increase in surface temperature and a decrease for the difference between the average temperature of the urban area and the surrounding areas of the city indicating the extension of the area where the phenomenon of the Urban Heat Island of Bucharest occurs.
dos Santos Luciano, Ana ClaudiaAraujo Picoli, Michelle CristinaRocha, Jansle VieiraCamargo Lamparelli, Rubens Augusto...
10页
查看更多>>摘要：The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in Sao Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space-time classifier calibrated with all sites together on years 2009-2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R-2 = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R-2 = 0.95 and -1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation.
查看更多>>摘要：The development of improved spatial and spectral resolution sensors provides new opportunities to assess burn severity more accurately. This study evaluates the ability of remote sensing indices derived from three remote sensing sensors (i.e., Landsat 8 OLI/TIRS, Sentinel-2 MSI and Deimos-1 SLIM-6.22) to assess burn severity (site, vegetation and soil burn severity). As a case study, we used a megafire (9,939 ha) that occurred in a Mediterranean ecosystem in northwestern Spain. Remote sensing indices included seven reflective, two thermal and four mixed indices, which were derived from each satellite and were validated with field burn severity metrics obtained from CBI index. Correlation patterns of field burn severity and remote sensing indices were relatively consistent across the different sensors. Additionally, regardless of the sensor, indices that incorporated SWIR bands (i.e., NBR-based indices), exceed those using red and NIR bands, and thermal and mixed indices. High resolution Sentinel-2 imagery only slightly improved the performance of indices based on NBR compared to Landsat 8. The dNDVI index from Landsat 8 and Sentinel-2 images showed relatively similar correlation values to NBR-based indices for site and soil burn severity, but showed limitations using Deimos-1. In general, monotemporal and relativized indices better correlated with vegetation burn severity in heterogeneous systems than differenced indices. This study showed good potential for Landsat 8 OLI/TIRS and Sentinel-2 MSI for burn severity assessment in fire-prone heterogeneous ecosystems, although we highlight the need for further evaluation of Deimos-1 SLIM-6-22 in different fire scenarios, especially using bi-temporal indices.
查看更多>>摘要：Emerging electric-vehicle technologies and the global transition to renewable energy have driven the production of lithium batteries significantly in the past ten years. However, potential adverse impacts accompanying this transition require closer scrutiny. The purpose of this research is to assess the environmental impact and its possible correlation with lithium mining in the Atacama Salt Flat, the world's largest lithium extraction site. Using both Landsat imagery and MODIS land products, we investigate the mining areas to (1) determine area and rate of change over time, (2) analyze spatiotemporal patterns of changes in key environmental parameters, and (3) perform regression analysis between lithium mining activities and environmental degradation between 1997 and 2017. We use five environmental parameters for our analysis: Normalized Difference Vegetation Index (NDVI), Daytime Land Surface Temperature (Day-LST), Soil Moisture Index (SMI), Nighttime Land Surface Temperature (Night-LST), and Net Evapotranspiration (ET). Our analysis shows that lithium mining operations have expanded rapidly by 7.07% annually. Our pixel-based time-series trend analysis for each image stack, using the Mann-Kendall test and Sen's slope coefficient, shows some significant degradation over the past 20 years including (1) vegetation decline, (2) elevating daytime temperatures, (3) decreasing trend of soil moisture, and (4) increasing drought condition in national reserve areas. However, no substantial degradation in nighttime-LST and ET is observed in the study area. Our analyses of the relationship between mining activities and environmental degradation also indicate that the continuous expansion of lithium mining has strong negative correlations with the NDVI and SMI, and a strong positive correlation with LST. We identified lithium mining activities as one of the major stressors to the local environmental degradation. The results provide a baseline to evaluate future socio-environmental impacts of lithium mining in the region. We anticipate our analysis will help inform mining and environmental regulators, lithium industry decision-makers, and national park managers to provide better management of the world's largest lithium production sites for a sustainable future.
查看更多>>摘要：The aim of this investigation is to evaluate the applicability of multispectral satellite imagery for hydrothermal alteration mineral mapping and thermal anomaly detection as a proxy for the characterization of subtle geothermal systems in an aseismic geologic setting. The Yankari Park, an area in northeastern Nigeria characterized by several surface manifestations of hotsprings and hydrothermally altered rock deposits was selected for this study. The study evaluates the effectiveness of the Independent Component Analysis and Mixture Tuned Matched Filtering (MTMF) algorithm for target detection of hydrothermal alteration zones and the Single Channel Algorithm for thermal anomaly detection associated with subtle geothermal systems using spectral bands of Landsat-8, ASTER and Landsat-7, respectively. The results indicated that the use of verified image endmember spectra gives more accurate results compared to that from library spectra by implementing MTMF. Results of mapping thermally anomalous pixels did not conform to known locations of the thermal springs, however, examining the spatial correlation of the anomaly areas with the major fault-fracture systems from the geological map of the study area indicates a close affinity between them and with previously reported thermal gradients within heat conserving sedimentary formations. This investigation has significant implications as it signifies the need for the integrative use of alteration mineral mapping and thermal anomaly detection for cost-effective prefeasibility stage mapping of geothermal systems in an aseismic geologic setting located in African plate and other regions around the world.
查看更多>>摘要：Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability.
查看更多>>摘要：The red-edge bands place the recently available multispectral Sentinel-2 imagery at an advantage over other multispectral sensors, and hypothetically offer improved crop biophysical variable retrieval accuracy. In this study, Sentinel-2 data was tested for its ability to estimate winter wheat leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). Artificial neural network (ANN) and look-up table (LUT) (based on PROSAIL simulations) and vegetation index (VI) methods were applied to retrieve biophysical parameters, and compared with the biophysical processor module embedded in the Sentinel Application Platform (SNAP) software. Based on a set of in situ measurements (62 samples) and near-synchronous Sentinel-2 images, the inversion approaches were applied and validated. The results showed that: 1) Sentinel-2 red-edge bands improved the retrievals of chlorophyll / LAI compared to traditional VIs; 2) the red-edge VIs outperformed other approaches; and 3) the SNAP biophysical processor obtained comparable accuracies of LAI and CCC estimation compared to the ANN and LUT approaches, giving R-2 values above 0.5 with relatively low RMSE (1.53 m(2)/m(2) for LM, and 148.58 mu g/cm(2) for CCC). We recommend VI retrieval approach for small region with ground measurements, whereas where ground data is not available, SNAP is applicable for versatile and rapid winter wheat parameter estimation (though results need to be evaluated alongside the provided quality indicators). Summarizing, the results demonstrate the suitability of Sentinel-2 data, especially its red-edge bands, for crop biophysical variables retrieval. Future studies will need to make comparisons across canopy types to better assess the capability of the SNAP biophysical processor.
查看更多>>摘要：Traditional ground survey methods have limited the development and use of renewable energy from geothermal resources. In this paper, the geothermal potential area of Dandong, China is studied using the thermal infrared (TIR) remote sensing data from daytime Landsat 8 and nighttime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Through pre-processing such as georeferencing, radiometric calibration, and atmospheric correction, the Landsat8 TIRS and ASTER data were used to invert the land surface temperature of the study area during the daytime and nighttime using the single-channel algorithm and temperature and emissivity separation algorithm. Furthermore, the land surface temperatures during daytime and nighttime of three natural land features-water, vegetation, and bare soil-were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and relatively cold anomalies during the night. Conversely, water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Calculating the daytime and nighttime mean of land surface temperature (DNMLST) can eliminate the relatively cold/thermal anomalies exhibited by natural land features during the daytime and nighttime while highlighting the geothermal anomaly zone. Nine geothermal anomaly zones were identified using the threshold method. The auxiliary analysis of geological data excluded non-geothermal effects. Studies have shown that the distribution of the identified geothermal prospects is consistent with the development and distribution of faults. The fractures cut the land surface, causing groundwater to form structural fissure hot water through fracture structures and fissures after magma heating. The use of two higher-resolution TIR remote sensing data products to obtain the DNMLST for the detection of geothermal anomalies has proven cost-effective technical method.
Deng, Khidir Abdalla KwalLaraine, SalimPavlides, AndrewPetropoulos, George P....
12页
查看更多>>摘要：Earth Observation (EO) allows deriving from a range of sensors, often globally, operational estimates of surface soil moisture (SSM) at range of spatiotemporal resolutions. Yet, an evaluation of the accuracy of those products in a variety of environmental conditions has been often limited. In this study, the accuracy of the SMOS SSM global operational product across 2 continents (USA, and Europe) and a range of land use/cover types is investigated. SMOS predictions were compared against near concurrent in-situ SSM measurements from the FLUXNET observational network. In total, 7 experimental sites were used to assess the accuracy of SMOS derived soil moisture for 2 complete years of observations (2010-2011). The accuracy of the SMOS SSM product is investigated in different seasons for the seasonal cycle as well as different continents and land use/cover types. Results showed a generally reasonable agreement between the SMOS product and the in-situ soil moisture measurements in the 0-5 cm soil moisture layer. Root Mean Square Error (RMSE) in most cases was close to 0.1 m(3 )m(-3) (minimum 0.067 m(3) m(-3)). With a few exceptions, Pearson's correlation coefficient was found up to approx. 55%. Grassland, shrublands and woody savanna land cover types attained a satisfactory agreement between satellite derived and in-situ measurements but needle-leaf forests had lower correlation. Better agreement was found for the grassland sites in both continents. Seasonally, summer and autumn underperformed spring and winter. Our study results provide supportive evidence of the potential value of this operational product for meso-scale studies in a range of practical applications, helping to address key challenges present nowadays linked to food and water security.
查看更多>>摘要：Accurate information on crop distribution and its changes is important for food security and environmental management. Although time series analysis is a widely-used and useful tool to characterize the seasonal dynamics of crops, the traditional image stacking approach misses important phenological events. This condition makes it difficult to identify the spectral and temporal features that are potentially important for crop identification, and therefore, makes it difficult to determine the optimal feature inputs for classifying crops with both high accuracy and low computation time. To address this gap, we developed a method to automatically select the spectro-temporal features by mining crop phenology information so as to improve the accuracy of crop classifications. This method of Phenology-based Spectral and Temporal Feature Selection (PSTFS) contains two major components: to identify the features with the highest separability between each pair of classes, and to prune redundant features to retain the best for classification. Using this optimal set of features and support vector machines (SVMs), we generated a high-quality corn cultivation map of China's Heilongjiang Province for 2011. The corn map had accuracies greater than 85% and agreed well with the corn census areas. We also demonstrate the goodness of this method for selecting features with high interpretability: it identified two phenological stages (three leaf and milky mature) that could best separate corn from other land use classes in the region. Our approach indicates the great potential for using the PSTFS method in conjunction with SVM classifiers to accurately map crop types based on satellite time series data.
NSTL
Elsevier