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：Accurate growing stock volume (GSV) estimation is essential for forest inventory updating, terrestrial carbon stocks reporting, and ecosystem services assessment. This study investigates the potential of spectral and spatial features derived from single-date and multi-seasonal Sentinel-2 Multi Spectral Instrument (Sentinel-2 MSI) images, for GSV estimation in a Mediterranean region of Northeastern Greece. Original spectral bands, spectral indices, first-order statistics, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, and Local Indicators of Spatial Association (LISA), based on the multi-seasonal and single-date Sentinel-2 MSI imagery, were used for GSV model development using the bagging LASSO algorithm. For both single and multi-date approaches, the spectral indices models were more accurate compared to the respective ones developed with the original Sentinel-2 MSI bands. Also, models based on texture were more efficient than the spectral models. The GLCM measures derived from July image, provided the most accurate single-date estimate of GSV (R-2 = 0.89, RMSE = 35.21), while their multi-seasonal counterparts improved slightly the accuracy (R-2 = 0.91, RMSE = 32.77). Fusion of spatial and textural information resulted in marginal or no-improvement on the texture model accuracy, however the fused models yielded higher predictive results than the spectral models alone.
查看更多>>摘要：Seagrasses are rapidly losing their ability to serve ecosystem services (ESs) with the loss of global biodiversity and coastal habitat degradation over the past few decades. Monitoring ESs is therefore important for tracking subsequent decline or recovery. The development of new Earth Observation (EO) technologies and approaches involved in observing and analyzing data collected from remote sensing (RS) satellite/aircraft would make for a useful application: monitoring and mapping spatial distribution of ESs that seagrasses provide to marine ecosystems and human well-being. Unfortunately, current approaches greatly rely on spatial proxy measures to map distribution of ESs. Many of biophysical parameters are currently detectable by EO instruments, with relevance to ESs. This paper review the capabilities of advanced RS techniques for informing species diversity, growth traits, health condition, ecological processes, and water quality variables linking ESs and describe how these EO products can contribute to ES assessments. Incorporation of both the direct (seagrass extent) and indirect (water related ESs) estimates derived from EO data can now provide more direct estimates of seagrass ecosystem properties (seagrass habitat quality and biodiversity) influencing ESs than the spatial proxies presently in use and they can support in developing more mechanistic models in GIS framework and spatially explicit maps of ESs. The increasing range of EO system and data sets suitable for measuring ES indicators has potential to supporting integrated coastal land use planning. Because each ES indicator and service responds to the environment, there is no 'one approach fits all' solution. Selecting EO products, with required resolution to be analyzed will guide to improving mapping efforts. This work also shed lights to sensitize discussion about need of holistic methodologies, challenges, and to motivate an enhanced use of EO-based technology and data. The need for a multidisciplinary project team of ecologists, sociologists, biologists and RS experts has been suggested for proper identification of ES indicators and advanced analysis of EO data. By doing so, we anticipate rapid progress in satellite based ES assessment and characterization of ESs and, in turn, supporting conservation and management of coastal ecosystem.
查看更多>>摘要：Accurately monitoring rubber plantations dynamics is essential for assessing eco-environmental effects in soil, hydrology and biodiversity especially in the northern edge of the Asian tropics (e.g. Xishuangbanna, China). In this study, a novel phenology-based multiple normalization approach was firstly proposed to annually map rubber plantations between two critical phenological phase (defoliation and foliation) in Xishuangbanna during 1987-2018. It included three key steps, namely (1) Landsat non-visible bands normalization for calculating the Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR), (2) normalization of NDMI and NBR for the Normalized Vegetation Index (NVI), and (3) re-normalization of NVIs for the Re-Normalized Vegetation Index (RNVI). The NVI highlighted the temporal differences of NDMI in land surface moisture content and NBR in soil moisture as well as vegetation survival of rubber plantations during the shifting period of defoliation and foliation. The RNVI fully considered the inverse patterns of the NVIs between defoliation and foliation phase. Rubber plantations were featured by negative NVI and RNVI values within the two temporal windows, while the positive NVI and RNVI values or zero stood for other non-rubber land cover types. The average overall accuracy of five-year mature rubber plantations maps (2010, 2013, 2015, 2016, and 2018) was up to 94.7% with the average kappa coefficient of 0.88, showing the great potential of multiple normalization approach. The total area of rubber plantations increased about 5.9 times from 1987 to 2018, showing clear expansion trends from centralization to scattering in Xishuangbanna as well as continuous spread in Sino-Lao (near Luang Namtha) and Sino-Myanmar (near Mongphak of Shan) border regions on the Chinese side. In addition, annual average analysis showed that about 91.4% of rubber plantations were invariably distributed around Jinghong City and Mengla County in the past decades.
Crabbe, Richard A.Janous, DaliborDarenova, EvaPavelka, Marian...
11页
查看更多>>摘要：Monitoring forest soil carbon dioxide efflux (FCO2) is important as it contributes significantly to terrestrial ecosystem respiration and is hence a major factor in global carbon cycle. FCO2 monitoring is usually conducted by the use of soil chambers to sample various point positions, but this method is difficult to replicate at spatially large research sites. Satellite remote sensing is accustomed to monitoring environmental phenomenon at large spatial scale, however its utilisation in FCO2 monitoring is under-explored. To this end, this study explored the potential of LANDSAT-8 to estimate FCO2 with the specific aims of deriving land surface temperature (LST) from LANDSAT-8 and then develop FCO2 model on the basis of LANDSAT-8 LST to account for seasonal and inter-annual variations of FCO2. The study was conducted over an old European beech forest (Fagus sylvatica) in Czech Republic. In the end, two kinds of linear mixed effect models were built; Model-1 (inter-annual variations of FCO2) and Model-2 (seasonal variations of FCO2). The difference between Model-1 and Model-2 lies in their random factors; while Model-1 has 'year' of FCO2 measurement as a random factor, Model-2 has 'season' of FCO2 measurement as a random factor. When modelling without random factors, LANDSAT-8 LST as the fixed predictor in both models was able to account for 26% (marginal R-2 = 0.26) of FCO2 variability in Model-1 whereas it accounted for 29% in Model-2. However, the parameterisation of random effects improved the performance of both models. Model-1 was the best in that it explained 65% (conditional R-2 = 0.65) of variability in FCO2 and produced the least deviation from observed FCO2 (RMSE = 0.38 pmol/m(2)/s). This study adds to the limited number of previous similar studies with the aim of encouraging satellite remote sensing integration in FCO2 observation.
查看更多>>摘要：The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha(-1) to 55 t ha(-1) (37% to 67% relative RMSE), and an overall bias ranging from -1 t ha(-1) to +5 t ha(-1) at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha(-1)) in the lower AGB classes, and underestimation (up to 85 t ha(-1)) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.
查看更多>>摘要：High spectral resolution (hyperspectral) remote sensing has already demonstrated its capabilities for soil constituent mapping based on absorption feature parameters. This paper tests different parametrizations of the 1.75 mu m gypsum feature for the determination of gypsum abundances, from the laboratory to remote sensing applications of recent as well as upcoming hyperspectral sensors. In particular, this study focuses on remote sensing imagery over the large body of the Omongwa pan located in the Namibian Kalahari. Four common absorption feature parameters are compared: band ratio through the introduction of the Normalized Differenced Gypsum Index (NDGI), the shape-based parameters Slope, and Half-Area, and the Continuum Removed Absorption Depth (CRAD). On laboratory soil samples from the pan, CRAD and NDGI approaches perform best to determine gypsum content tested in cross validated regression models with XRD mineralogical data (R-2 = 0.84 for NDGI and R-2 = 0.86 for CRAD). Subsequently the laboratory prediction functions are transferred to remote sensing imagery of spaceborne Hyperion, airborne HySpex and simulated spaceborne EnMAP sensor. Variable results were obtained depending on sensor characteristics, data quality, preprocessing and spectral parameters. Overall, the CRAD parameter in this wavelength region proved not to be robust for remote sensing applications, and the simple band ratio based parameter, the NDGI, proved robust and is recommended for future use for the determination of gypsum content in bare soils based on remote sensing hyperspectral imagery.
Saepuloh, Asepvan der Meer, FreekShrestha, Dhruba Pikha
10页
查看更多>>摘要：Optical remote sensing data has been extensively used since early seventies for mapping and monitoring land cover. But, cloud cover has always been hindering the optimal use of the data, especially in the tropical and temperate regions. Because of cloud cover, researchers are forced to use only cloud free images or generate a composite using data from different dates to fill in the cloud and cloud-shadow areas. It is not a problem if the filling image is from the same season and from the same year. If there is large time gap between the acquisition date of image with the clouds and that of the filling image it will have an effect on classification accuracy. In this paper, we describe an approach in land cover classification of areas covered by cloud and cloud shadows. We use the synergy of optical image classification, processing of SAR data taking advantage of its ability of penetrating clouds, and decision rules using terrain parameters. We hypothesize that surface roughness of different land cover types derived from processing of SAR data and in combination with other topographic parameters (slope and elevation) can be applied to classify areas covered by clouds and cloud-shadows. The method was applied in a case study in Mts. Wayang-Windu and Patuha areas, near Bandung in West Java, Indonesia. Landsat 8 OLI-TIRS, Sentinel-1 A C band and SRTM DEM data of the study area were obtained. Initial classification of the optical data was carried out using training samples and fuzzy classification. Class labelling was done by applying fuzzy c-means algorithm. The Sentinel-1 A C band data was processed to get surface roughness. Decision boundaries for surface roughness, terrain slope and elevation were generated for each land cover type, which were implemented in the classification of the areas covered by cloud and cloud shadows. The result shows that it is possible to solve the problem of cloud cover using terrain parameters.
查看更多>>摘要：This article gives methods to analyze the difference between two datasets that describe a mutual set of categories. The methods can analyze classification error between mapped and reference categories, or change between two time points. Previous work showed how to compute difference size as the sum of three components: Quantity, Exchange and Shift. These components exist for difference by category and for difference overall. These components can be challenging to compare when the categories' differences vary by size. To address this challenge, this article introduces equations to compute a component's intensity, which is the size of the component divided by the size of the difference. Component intensities facilitate comparison of each category with other categories and with difference overall. The case study illustrates how to use component intensities to characterize temporal change using remotely sensed data. Results show how an intensive Exchange component can signal possible confusion of two categories with each other. The literature shows that authors could benefit from interpretation of component intensities. Readers can perform the calculations for free by using the diffeR package in R or the PontiusMatrix spreadsheet available at www.clarku.edu/similar to rpontius.
Mathews, Adam J.Frazier, Amy E.Nghiem, Son, VNeumann, Gregory...
8页
查看更多>>摘要：Accurately mapping urban infrastructure and extent is a high priority for resource management and service allocation as well as for addressing environmental, socioeconomic, and geopolitical concerns. Most available data products only document surficial (two-dimensional) land use and land cover (LULC), yet a substantial component of urban growth occurs in the vertical dimension. Light detection and ranging (lidar) data offer the potential for monitoring three-dimensional (3D) change, but the extreme lack of systematic lidar coverage worldwide inflicts considerable gaps in both spatial and temporal coverage. Satellite scatterometer (radar) data may serve as an alternative data source for characterizing urban growth and development in both the horizontal and vertical directions. The accuracy of these radar-based datasets for estimating building volumes remains to be validated quantitatively. For nine U.S. cities, we test whether scatterometer data can be used to estimate 3D urban built-up volume. We found strong, linear correlations between the lidar-derived and radar-derived building volume estimates for all cities with r(2) values as high as 0.98 when using spatial trend analysis. Given the high expense that limits lidar data acquisition to small areas at sporadic points in time, satellite scatterometer data provide a breakthrough method for monitoring both vertical growth and horizontal expansion of cities across the world with a continuous decadal time scale.
Wang, Qiangvan der Velde, RogierFerrazzoli, PaoloChen, Xuelong...
11页
查看更多>>摘要：An algorithm is developed for retrieving soil moisture at plateau scale by combined usage of Aquarius active and passive L-band observations. In this algorithm, Look-Up-Tables (LUTs) are established for bare soil and vegetated areas by using the physical based Tor Vergata discrete electromagnetic model (hereafter, TV-DEM). In the case of vegetated area, the LUT is built based on simulations with varying soil moisture and Leaf Area Index (LAI). Only soil moisture is variable for the bare soil case, and values calibrated in previous works are adopted for the other TV-DEM parameters. Soil moisture is then retrieved by minimizing a squared difference object function based on the emissivity and backscatter coefficient observed by Aquarius and the corresponding TV-DEM simulations included in the LUT. The soil moisture retrievals are assessed at Aquarius footprint scale using in-situ measurements collected at three regional scale networks spread across the Tibetan Plateau. The unbiased root mean squared differences (ubRMSDs) from the three networks vary from 0.016 to 0.050 m(3) m(-3) and coefficients of determination (R-2) are from 0.274 to 0.499 (-). This ubRMSD and R-2 is in the same order of the passive-only official Aquarius product, Metop-A Advanced SCATterometer (ASCAT) L2 soil moisture product (hereafter ASCAT) as well as reanalysis data generated by European Centre for Medium-Range Weather Forecasts (hereafter, ERA-Interim). At Plateau-scale, all four soil moisture products capture the seasonal trend, whereby the dynamic range during the monsoon season captured by the ERA-Interim product is relatively small. Further, the northwest-southeast dry-wet gradient due to precipitation and evapotranspiration is well captured by the soil moisture spatial distributions produced by TV-DEM Aquarius, official Aquarius and ASCAT products, but less pronounced in the ERA-Interim product. This study demonstrates that the TV-DEM based algorithm can be used to retrieve soil moisture over a Plateau scale with robust results in terms of error statistics (e.g. bias, R-2 and ubRMSD) and can generate realistic spatial patterns for soil moisture at Plateau-scale.
NSTL
Elsevier