首页期刊导航|International journal of applied earth observation and geoinformation
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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：Tree height is an important structural trait, critical in forest ecology and for above ground biomass estimate, and difficult to accurately measure in the field especially in dense forests, such as the tropical ones. The accuracy of height measurements depend on several factors including forest status, the experience of the observer, and the equipment used, with large subjectivity, heterogeneity and uncertainty in results, that can propagate when tree height is used in models. A comparison of Terrestrial Laser Scanning, Airborne Lidar Scanning, and stereo-photogrammetry (with imagery acquired by a RGB camera mounted on Unmanned Aerial Vehicle) approaches for estimating tree height was here performed, also with reference to ground methods. In fact, all those technique may increase the possibility of precise tree height measures, while reducing manual effort in comparison to more traditional ground techniques. The research was carried out in a dense tropical forest in Ghana; differences in measured heights as well as their impact on above ground biomass estimation were analyzed. All the different methods were characterized by pros and cons: the obtained results indicate that in dense forests, where sight occlusion problems occur, ground traditional techniques can lead to overestimation, while with the other mentioned techniques underestimation can occur, but in variable amount according to the considered instrument. The different height measures caused a remarkable variation in the estimated biomass of this tropical forest: more accurate height measurements are needed to reduce the uncertainty in biomass mapping efforts at any scale. Possibly, the simultaneous use of different methods can help in correctly estimate height uncertainty and reach a convergent and accurate result.
查看更多>>摘要：Convolutional neural networks offer a new approach to classifying high resolution imagery. We use the U-net neural network architecture to map the presence or absence of trees and large shrubs across the Australian state of Queensland. From a state-wide mosaic of 1 m resolution 3-band Earth-i imagery, a selection of 827 squares (1 km(2)) are manually labeled for the presence of trees or large shrubs, and these are used to train the neural network. The training is intended to capture the textures which are primary visual cues of such vegetation. The trained neural network has an accuracy on independent data of around 90%. The resulting map over the whole of Queensland (1.73 million km(2)) is intended to be manually checked, and edited where necessary, to provide a high quality map of woody vegetation extent to serve a range of government policy objectives.
查看更多>>摘要：Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0 degrees or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products.
查看更多>>摘要：Soil monitoring information is important to improve our understanding of the role of soil on global environment change such as invasion of foreign species. For regions with dense vegetation cover the use of remote sensing data provides an attractive solution to soil prediction through the relationship between soil and remotely sensed information of vegetation, especially considering the availability of multi-temporal series of synthetic aperture radar (SAR) data such as Sentinel-1. In this study, we used a structural equation model (SEM) to link soil organic carbon (SOC) and bulk density (BD) with temporal variation of SAR signals, taking into account possible interacting relationships of the soil-vegetation system. The test area is in the coastal wetlands of east-central China, where Sentinel-1 data were acquired during the vegetation growing season in 2017. A total of fifteen sites were sampled at three depths: 0-30 cm, 30-60 cm, and 60-100 cm. Predictive accuracy was assessed using leave-oneout cross-validation (LOOCV). Results showed that SE models successfully predicted SOC (RMSE = 1.63 g kg(-1), RPD = 1.22) and BD (RMSE = 0.14 g cm(-3), RPD = 1.25) at three depths. We found that SEM supported the idea that the interrelationships exist among soil, vegetation, and remotely sensed information, and improved our ability to investigate relationships between SAR backscatters and soil attributes. The use of time series Sentinel-1 data allowed capturing characteristics of vegetation dynamics and the possible relationships between soil attribute and vegetation. The findings from this study highlight the usefulness of dense temporal SAR data and SEM in soil prediction.
查看更多>>摘要：In the application of machine learning to geographic object based image analysis, several parameters influence overall classifier performance. One of the first parameters is segmentation size-for example, how many pixels should be grouped together to form an image object. Often, trial and error methods are used to obtain segmentation parameters that best delineate the borders of real world objects. Several attempts at automated methods have produced promising results, but manual intervention is still necessary. Meanwhile, numerous measures of segmentation quality have been defined, but their relationship to classifier performance is not then directly shown. For example, as measures of segmentation quality improve, do classification results improve as well? Our work considers the problem of building classification in high resolution aerial imagery of urban areas. Based on user defined training polygons generated with or without a reference segmentation, we have found several measures of segmentation quality and feature performance that can help users narrow the range of appropriate segmentations. Furthermore, our work finds that given this range, performance of machine learning algorithms remains relatively constant for any given segmentation as long as features used for classification are chosen correctly. We find that the range of scale parameters capable of producing an accurate classification is much broader than typically assumed and trial and error methods for finding this parameter may be an acceptable approach.
查看更多>>摘要：Soil clay content is a key parameter that influences many other soil properties and processes. The potential of adding new and contemporary satellite data for soil property mapping in France is assessed in this study. The soil property maps used for this analysis were produced within the framework of GlobalSoilMap, which was created to deliver global fine grids of soil properties and associated uncertainties using existing soil information and ancillary data to predict these properties based on digital soil mapping techniques. In this study, we evaluate the added value of Moderate Resolution Imaging Spectroradiometer (MODIS), Project for On-Board Autonomy-Vegetation (PROBA-V), and Sentinel-2 (S2) data for predicting the soil clay content at 90 m resolution for mainland France. The rationale behind adding these data is that satellite images and derived products may enable the biogeo-chemical characteristics of the earth's surface to be captured more effectively, which in turn enables more precise predictions of the soil clay content. For this methodology, we i) create composite bare soil mosaics and derive the spectral indices from S2 data acquired during sowing periods from 2016 to 2017, ii) extract the first three principal components of harmonized MODIS and PROBA-V normalized difference vegetation index (NDVI) time series acquired in 2003 and 2016 to represent vegetation changes, and iii) test whether the complementary datasets are able to improve the soil clay information compared to a benchmark value. The soil clay content is obtained by using quantile regression forest (QRF) for each GlobolSoilMap depth interval of 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm along with a 10-fold cross-validation having 10 replicates. The results show that the complementary satellite data improve the clay content estimation on bare soil for the topsoil layers (e.g., 0-30 cm) by increasing the R-2 and decreasing the bias at averages of 0.05 and 1 g kg(-1), respectively. Moreover, the first principal component of the harmonized NDVI data is shown to be the second most important variable for estimating the clay content, as indicated by the QRF models. However, the use of only the satellite data and products as input for the QRF does not yield a satisfactory estimate of the clay content. Finally, this work provides a reference for embedding new remote sensing data in existing national soil inventories and national soil information systems. Further research should incorporate new techniques for considering the spatial-temporal variability of the earth's surface parameters such as soil moisture and roughness.
Hu, TianLi, HuaCao, Biaovan Dijk, Albert I. J. M....
11页
查看更多>>摘要：The angular variation of land surface emissivity (LSE) is rarely considered in the split-window algorithm for retrieving land surface temperature (LST), and this can cause large uncertainties in LST retrievals. To analyze the influence of angular LSE variation on LST retrievals, we built a look-up table (LUT) of directional emissivities from the MYD21A1 LST/LSE product in the Moderate Resolution Imaging Spectroradiometer (MODIS) split-window channels. The extracted directional emissivities were then input into the MODIS generalized split-window (GSW) algorithm to substitute for the classification-based emissivities. A simulation analysis was first conducted based on the LUT. Furthermore, the LST retrievals estimated from MODIS observations using the directional emissivities were compared with those estimated using the classification-based emissivities. In-situ measurements from the US SURFRAD and China's HiWATER networks were used to evaluate LST retrievals obtained using the two different emissivities. The results showed that angular LSE variations in the split-window channels for vegetated surfaces were generally minor during the daytime, but more pronounced during the night-time (approximately 0.005 between nadir and 60 degrees). For barren surfaces, the angular LSE variation in the (similar to)12 mu m channel was negligible but reached approximately 0.01 in the (similar to)11 mu m channel. In the simulation, the influence of angular LSE variation was minor for view-zenith angles (VZA) < 40 degrees, but pronounced for VZA > 40 degrees reaching approximately 1.0 and 0.7 K at VZA 65 degrees for barren and vegetated surfaces, respectively. In the evaluation, the LST estimated using the directional emissivities showed a higher accuracy than those estimated using the classification-based emissivities, especially over barren surfaces where the improvement reached > 1 K. We conclude that angular LSE variation cannot be ignored in LST estimation using the GSW algorithm when VZA is > 40 degrees, especially over barren surfaces. The accuracy of the GSW algorithm is improved pronouncedly by using the directional emissivities extracted from the MYD21 product.
Xiao, XiangmingDoughty, Russell B.Liu, MingyueJia, Mingming...
12页
查看更多>>摘要：Aquaculture is one of the fastest growing animal food production sectors mainly developed in fertile coastal areas. Monitoring and mapping of aquaculture ponds are of utmost importance for the sustainable management of coastal ecosystems. In this study, an integrated updating and object-based classification approach was developed to generate maps of coastal aquaculture ponds in China from 1984 to 2016 at 30-m spatial resolution. The current extent and change of coastal aquaculture ponds in China were analyzed over the course of 32 years. In addition, spatial-temporal dynamics of coastal aquaculture ponds were examined by buffer and overlay analyses. The results showed that the total area of coastal aquaculture ponds in China expanded by 10,463 km(2), with the largest gain occurring from 1990 to 2000 (4,207 km(2)). The coastal provinces of Guangdong, Shandong, Jiangsu, Liaoning, and Hebei had significant increases of aquaculture ponds areas, accounting for 83% of totally expanded ponds in the coastal zone of China. Rapid expansion of coastal aquaculture ponds was observed in the 0-10 km inshore buffer and the loss of wetlands and arable land contributed more than 50% to the expansion. Socio-economic factors helped drive the continual increase of coastal aquaculture ponds in China. Scientific environmental regulations and planning and management strategies at the national and international policy levels should be enhanced to consider the ecological impacts of aquaculture expansion.
NSTL
Elsevier