Spectral Mixture Analysis Research Articles

Long-Term Annual Surface Water Change in the Brazilian Amazon Biome: Potential Links with Deforestation, Infrastructure Development and Climate Change

The Brazilian Amazon is one of the areas on the planet with the fastest changes in forest cover due to deforestation associated with agricultural expansion and infrastructure development. These drivers of change, directly and indirectly, affect the water ecosystem. In this study, we present a long-term spatiotemporal analysis of surface water annual change and address potential connections with deforestation, infrastructure expansion and climate change in this region. To do that, we used the Landsat Data Archive (LDA), and Earth Engine cloud computing platform, to map and analyze annual water changes between 1985 and 2017. We detected and estimated the extent of surface water using a novel sub-pixel classifier based on spectral mixture analysis, followed by a post-classification segmentation approach to isolate and classify surface water in natural and anthropic water bodies. Furthermore, we combined these results with deforestation and infrastructure development maps of roads, hydroelectric dams to quantify surface water changes linked with them. Our results showed that deforestation dramatically disrupts small streams, new hydroelectric dams inundated landmass after 2010 and that there is an overall trend of reducing surface water in the Amazon Biome and watershed scales, suggesting a potential connection to more recent extreme droughts in the 2010s.

Effect of Land Cover Fractions on Changes in Surface Urban Heat Islands Using Landsat Time-Series Images.

Man-made materials now cover a dominant proportion of urban areas, and such conditions not only change the absorption of solar radiation, but also the allocation of the solar radiation and cause the surface urban heat island effect, which is considered a serious problem associated with the deterioration of urban environments. Although numerous studies have been performed on surface urban heat islands, only a few have focused on the effect of land cover changes on surface urban heat islands over a long time period. Using six Landsat image scenes of the Metropolitan Development Area of Wuhan, our experiment (1) applied a mapping method for normalized land surface temperatures with three land cover fractions, which were impervious surfaces, non-chlorophyllous vegetation and soil and vegetation fractions, and (2) performed a fitting analysis of fierce change areas in the surface urban heat island intensity based on a time trajectory. Thematic thermal maps were drawn to analyze the distribution of and variations in the surface urban heat island in the study area. A Multiple Endmember Spectral Mixture Analysis was used to extract the land cover fraction information. Then, six ternary triangle contour graphics were drawn based on the land surface temperature and land cover fraction information. A time trajectory was created to summarize the changing characteristics of the surface urban heat island intensity. A fitting analysis was conducted for areas showing fierce changes in the urban heat intensity. Our results revealed that impervious surfaces had the largest impacts on surface urban heat island intensity, followed by the non-chlorophyllous vegetation and soil fraction. Moreover, the results indicated that the vegetation fraction can alleviate the occurrence of surface urban heat islands. These results reveal the impact of the land cover fractions on surface urban heat islands. Urban expansion generates impervious artificial objects that replace pervious natural objects, which causes an increase in land surface temperature and results in a surface urban heat island.

Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis.

The accurate assessment of rice yield is crucially important for China’s food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%.

A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis

Crop residue deposited on the soil surface helps protect against water and wind erosion, improve soil quality and increase soil organic matter and soil carbon storage. Linear spectral mixture analysis (LSMA) is an important technique in field crop residue estimation. Traditionally, only one single or fixed standard crop residue and soil endmember spectrum is performed for each of the presented endmember classes or field. Because the variation in soil endmember spectrum signatures significantly changes with soil moisture (SM), spectral unmixing with fixed endmember spectra can lead to poor accuracy of the abundance of the spectral constituents of pure crop residue. Herein, this paper presents a dynamic soil endmember spectrum selection approach for improving the performance of soil and rice residue spectral unmixing analysis in rice residue cover (RRC) estimation. This new approach uses SM and soil spectral reflectance model to modify soil endmember spectra in each spectral unmixing analysis. Two validation datasets computed results have verified the feasibility and correctness of the dynamic soil endmember method. Results indicated that the distribution of SM in farmland was crucial for RRC estimation. Compared with traditional fixed min and fixed mean soil endmember spectrum methods, the results of the method developed herein showed this method significantly improved RRC estimation accuracy for an SM content lower than 20% (volumetric water content) over the traditional methods tested. Therefore, our proposed approach can be used to improve RRC estimation accuracy in harvest field.

The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014–2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The data were atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances from each endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.

Mapping and Characterizing Spatiotemporal Dynamics of Impervious Surfaces Using Landsat Images: A Case Study of Xuzhou, East China from 1995 to 2018

As an effective indicator of urbanization, impervious surfaces play a significant role in urban planning and ecological protection. It is, therefore, important to characterize impervious surfaces in urban geographical studies. As a key city in East China, Xuzhou has experienced rapid urbanization in recent decades and is now becoming an environmentally friendly city. To better understand the spatiotemporal heterogeneity of Xuzhou’s urban development, we extracted its impervious surfaces from Landsat images of 1995, 2003, 2010, and 2018 by a linear spectral mixture analysis. Then, a range of complementary methods including landscape indices, profile lines, median centers, standard deviational ellipses, and spatial autocorrelation were adopted to analyze the landscape pattern and expansion of impervious surfaces on both city and district scales. Results show that (1) there was a constant impervious surface expansion, originating in downtown Xuzhou; (2) promoting ecological protection in urban areas fragmented impervious surfaces with increasing heterogeneity and diversity overall; and (3) expansion directions and rates of impervious surfaces varied with district and town, and the central urban area expanded towards east and southeast, which could be related to their own resources and governmental policies. Findings from this study provide useful insights into urban planning of this economically prospective region.

A multisensoral approach for high-resolution land cover and pasture degradation mapping in the humid tropics: A case study of the fragmented landscape of Rio de Janeiro

Pasture degradation is of increasing global concern as it enforces erosion processes and impacts the carbon storage capacities of the soil. Reliable methods for pasture degradation mapping are thus of great use to provide important information for sustainable landscape planning. Our research focusses on the Guapi-Macacu watershed (Rio de Janeiro (RJ), Brazil) as part of the biodiversity hotspot Mata Atlântica. The area is characterized by strong forest fragmentation and pasture degradation. We investigate the suitability of RapidEye and Landsat 5 TM data in comparison to a high-resolution image composite product based on RapidEye and downscaled Landsat 5 TM SWIR bands for land cover classification and pasture degradation mapping. Land cover classification results improved significantly for the image composite product (overall accuracy (OAA) 89%) compared to the application of RapidEye (OAA 87%) or Landsat 5 TM (OAA 85%) data alone. Pasture degradation was mapped using degradation class thresholds derived from field data and vegetation cover fractions on a per pixel basis and modelled using multiple endmember spectral mixture analysis (MESMA). The pasture degradation map based on the image composite achieved an overall accuracy of 77.5%, compared to 75% (RapidEye) and 61% (Landsat 5 TM). We further tested the relationship between degradation and slope class and concluded that more than 90% of the pastures on slopes >10° show signs of degradation, whereby on above 20° slopes the portion of moderate to strong degradation is above 57%.

Analysis of Landsat-derived multitemporal vegetation cover to understand drivers of oasis agroecosystems change

Oasis agroecosystems monitoring plays a significant role in the economic development, sustainable management, and policy-making of arid and Saharan regions. The aims of this study are to analyze the spatiotemporal changes of oasis vegetation and discuss possible driving forces of changes. This analysis employed field and ancillary data, geographic information system, Landsat imagery, and remote-sensing techniques. Minimum noise fraction is used for endmembers extraction, and spectral mixture analysis is applied to each image to extract vegetation fraction, which is used as an indicator of change. Change detection is performed in six oases in south-eastern Morocco over eight separate periods from 1984 to 2017 using Landsat data. The pattern of the spatiotemporal changes in vegetation cover is analyzed using time- and space-oriented change detection algorithms. Results indicate that the Mezguita, Tinzouline, and Ternata oases had an evenly distributed and constant expansion during the last three decades (of ∼41.35 % ), whereas the oases of Fezouata and Ktaoua presented low expansion and randomly distributed change (∼29 % ). However, the vegetation cover of M’Hamid oasis decreased by 23% over the studied period. The results show that spectral mixture analysis yields high accuracies for oasis vegetation extraction in arid areas and accounts for mixed pixel issues. The results are discussed considering also climate and socioeconomic factors, showing that the driving forces of these dynamics are primarily anthropogenic.

Study on Urban Expansion Using the Spatial and Temporal Dynamic Changes in the Impervious Surface in Nanjing

Impervious surface area is a key factor affecting urbanization and urban environmental quality. It is of great significance to analysis timely and accurately the dynamic changes of impervious surface for urban development planning. In this study, we use a comprehensive method to extract the time series data on the impervious surface area (ISA) from the multi-temporal Landsat remote sensing images with a high overall accuracy of 90%. The processes and mechanisms of urban expansion at different political administration and direction level in the Nanjing metropolitan area are investigated by using the comprehensive classification method consisting of minimum noise fraction, linear spectral mixture analysis, spectral index, and decision tree classifiers. The expansion of Nanjing is examined by using various ISA indexes and concentric regression analyses. Results indicate that the overall classification accuracy of ISA is higher than 90%. The ISA in Nanjing has dramatically increased in the past three decades from 427.36 km2 to 1780.21 km2 and with a high expansion rate of 0.48 from 2000 to 2005. The city sprawls from monocentric to urban core with multiple subcenters in a concentric structure, and the geometric gravity center of construction land moves southward annually. The stages of urbanization in different district levels and the dynamic changes in different direction levels are influenced by the topographic and economic factors.

Detailed urban surface characterization using spectra from enhanced spatial resolution Sentinel-2 imagery and a hierarchical multiple endmember spectral mixture analysis approach

Detailed mapping of urban surfaces is one of the most challenging tasks in remote sensing due to the three-dimensional structure of cities, spatial diversity, and material spectral variability. Satellite urban applications demand better spatial, spectral, and temporal resolution, although there are strict technical constraints among them. Therefore, the development of sophisticated methods that exploit both high spectral and spatial data sources becomes necessary. A hierarchical multiple endmember spectral mixture analysis (MESMA) approach is developed and applied on Sentinel-2 imagery for the detailed quantification of the urban land cover, taking advantage of Worldview-2 high spatial resolution. The case study is the urban and peri-urban area of Heraklion, Greece. The area to point regression kriging (ATPRK) method is applied to downscale Sentinel-2 bands from 10 and 20 m to 2 m (WorldView-2 spatial resolution) and create a spectral library (SL) of urban materials, which contain 180 separate spectra. The urban SL is then used in the developed hierarchical MESMA approach to estimate the abundances of 11 urban land cover classes based on the original Sentinel-2 image. The estimated land cover fractions validate against a very high-resolution (1 m) land cover map of the area. It is proved that the complexity of the urban land cover can be efficiently investigated by the proposed methodology. Error analysis shows good accuracy of the results in all estimated class fractions. Moreover, the good validation results lead to the conclusion that ATPRK fusion algorithm between Sentinel-2 and WorldView-2 bands produced reliable urban material spectra, capable for advanced spectral analysis on Sentinel-2 imagery. The developed methodology is easily transferable to other cities since it is based exclusively on earth observation data and is suitable for multiple urban applications related to urban climate, urban sprawl, and urban regeneration.

Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau

The snow cover distribution is crucial to Earth's climate and hydrological systems. Snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely utilized in climate research and water-source management. Three MODIS-derived fractional snow cover (FSC) products, namely, MOD10A1 version 6, MODSCAG, and MODAGE, are assessed to evaluate the accuracy of FSC data over the Tibetan Plateau from May 2013 to April 2015. A Landsat-8/ Operational Land Imager (OLI) snow map that is based on linear spectral mixture analysis is regarded as the “reference dataset.” A total of 149 OLI images that span the Tibetan Plateau are used. The performance of these three datasets is compared to the “reference dataset” by using binary and fractional metrics over forest, grass, and bare soil, and in the Himalaya region. The binary classification assessment reveals that larger local illumination angles and vegetation coverage at the snow boundaries resulted in omission errors in MOD10A1, and snow patchiness caused this model to miss snow. MODSCAG correctly identified snow pixels, but overestimated snow at the snow boundaries and at local illumination angles that exceeded 30°. MODAGE precisely recognized snow-free areas, but underestimated snow coverage because of snow patchiness. MODSCAG and MODAGE produced smaller root mean square errors (RMSE) than MOD10A1, likely because spectral mixture analysis is superior to the normalized difference snow index-based empirical method. The behaviors of these products differed at various spatial scales. The RMSE significantly declined with decreasing spatial resolution from 500 m to 2 km. However, when aggregated to 5 km, the RMSE increased because fewer pixels were involved in the calculation.

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

Rice is the staple food for more than half of humanity. Accurate prediction of rice harvests is therefore of considerable global importance for food security and economic stability, especially in the developing world. Landsat sensors have collected coincident thermal and optical images for the past 35+ years, and so can provide both retrospective and near-realtime constraints on the spatial extent of rice planting and the timing of rice phenology. Thermal and optical imaging capture different physical processes, and so provide different types of information for phenologic mapping. Most analyses use only one or the other data source, omitting potentially useful information. We present a novel approach to the mapping and monitoring of rice agriculture which leverages both optical and thermal measurements. The approach relies on Temporal Mixture Models (TMMs) derived from parallel Empirical Orthogonal Function (EOF) analyses of Landsat image time series. Analysis of each image time series is performed in two stages: (1) spatiotemporal characterization, and (2) temporal mixture modeling. Characterization evaluates the covariance structure of the data, culminating in the selection of temporal endmembers (EMs) representing the most distinct phenological cycles of either vegetation abundance or surface temperature. Modeling uses these EMs as the basis for linear TMMs which map the spatial distribution of each EM phenological pattern across study area. The two metrics we analyze in parallel are (1) fractional vegetation abundance (Fv) derived from spectral mixture analysis (SMA) of optical reflectance, and (2) land surface temperature (LST) derived from brightness temperature (Tb). These metrics are chosen on the basis of being straightforward to compute for any (cloud-free) Landsat 4-8 image in the global archive. We demonstrate the method using a 90 × 120 km area in the Sacramento Valley of California. Satellite Tb retrievals are corrected to LST using a standardized atmospheric correction approach and pixelwise fractional emissivity estimates derived from SMA. LST and Tb time series are compared to field station data in 2016 and 2017. Uncorrected Tb is observed to agree with the upper bound of the envelope of air temperature observations to within 3 °C on average. As expected, LST estimates are 3 to 5 °C higher. Soil T, air T, Tb and LST estimates can all be represented as linear transformations of the same seasonal cycle. The 3D temporal feature spaces of Fv and LST clearly resolve 5 and 7 temporal EM phenologies, respectively, with strong clustering distinguishing rice from other vegetation. Results from parallel EOF analyses of coincident Fv and LST image time series over the 2016 and 2017 growing seasons suggest that TMMs based on single year Fv datasets can provide accurate maps of crop timing, while TMMs based on dual year LST datasets can provide comparable maps of year-to-year crop conversion. We also test a partial-year model midway through the 2018 growing season to illustrate a potential real-time monitoring application. Field validation confirms the monitoring model provides an upper bound estimate of spatial extent and relative timing of the rice crop accurate to 89%, even with an unusually sparse set of usable Landsat images.

Comparison of hyperspectral HJ-1A/HSI and multispectral Landsat 8 and Sentinel-2A imagery for estimating alpine grassland coverage in the Three-River Headwaters region

Accurate estimation of grassland coverage is important in both monitoring grassland growth and promoting grassland management. This study, for the first time, aims to evaluate the alpine grassland coverage estimation performances of four widely used methods [i.e., random forest classification (RFC), regression analysis (RA), multiple endmember spectral mixture analysis (MESMA), and support vector machine regression (SVMR)] from three typical remote sensing images [i.e., hyperspectral (HJ-1A/HSI) image and multispectral (Landsat 8 and Sentinel-2A) images] in the Three-River Headwaters region, China. The total grassland coverage and three levels of grassland subcoverage (i.e., low, moderate, and high coverage) are estimated from the three images by each method. Meanwhile, the overall accuracy (OA) and root-mean-square error (RMSE) of each coverage result are assessed. The experimental results show that (1) for the total grassland coverage estimation, RFC method combined with Sentinel-2A data and RA method used both Landsat 8 and Sentinel-2A data generated the highest OA of 79.4%, whereas the highest OAs of SVMR and MESMA methods are 3% to 14.7% lower than those of RFC and RA methods, (2) for three grassland subcoverage estimation, Landsat 8 data combined with RFC method generated the greatest OAs (100%) and the lowest RMSEs (7.34%) for low grassland coverage and Sentinel-2A data combined with SVMR method obtained the highest OAs (≥83.3 % ) for moderate and high grassland coverage, (3) Sentinel-2A and Landsat 8 data generated higher OAs than HJ-1A/HSI data in estimating the total coverage while Sentinel-2A produced better performance than Landsat 8 in estimating the grassland subcoverage, and (4) SVMR method performed more stably than other methods in estimating alpine grassland coverage.

Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data

Tools that enhance the management of the soil resource are vital for sustainability and continued productivity. The assessment and monitoring of surface-soil condition provide key information required to effectively manage this resource. This research used a simulated Environmental Mapping and Analysis Program (EnMAP) hyperspectral image cube of an agricultural region in semi-arid Mediterranean Spain to classify soil erosion states. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to derive within-pixel fractions of eroded and accumulated soil states. Soil states (eroded, intermediate, accumulated) were classified with an overall accuracy of 70.3% using the unmixing fraction maps and terrain derivatives. The results generated in this research provided an outlook for the applicability of the future EnMAP sensor for soil erosion investigations in semi-arid Mediterranean environments.

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

ABSTRACT This paper examines the application of remote sensing, based on the Vegetation-Impervious surface-Soil (V-I-S) model and spatial metrics, in an urban analysis for promoting sustainability and understanding urban growth theory. In order to improve the accuracy of land-cover classification, spectral angle mapping (SAM), spectral mixture analysis (SMA) and band ratioing were applied on satellite images for land-cover classification and comparison of the discrimination efficiency of these techniques. For the SMA, subsets of the Landsat (2, 3, 4, 5, and 7) and ASTER (1, 2, and 3N) images were selected. After endmember extraction and purification, the Bayesian probability of each component was computed and used for spectral unmixing. The classified images of different years were compared to analyze the changes in land-use and spatial pattern using V-I-S, a form of percentage of landscape (PLAND) and annualized urban sprawl index (AUSI). The result indicates that the performance of band ratioing (69% accuracy) is not asgood as that of SAM (75%) and SMA (86%) in discriminating between vegetationand agricultural land. It is concluded from the land-use analysis that the growth dynamics substantiate the urban theory of diffusion and coalescence and urban growth management strategies have not been completely successful.

Combating selective ionization in the high resolution mass spectral characterization of complex mixtures.

Direct "dilute and shoot" mass spectral analysis of complex naturally-occurring mixtures has become the "standard" analysis in environmental and petrochemical science, as well as in many other areas of research. Despite recent advances in ionization methods, that approach still suffers several limitations for the comprehensive characterization of compositionally complex matrices. Foremost, the selective ionization of highly acidic (negative electrospray ionization ((-) ESI)) and/or basic (positive electrospray ionization ((+) ESI)) species limits the detection of weakly acidic/basic species, and similar issues (matrix effects) complicate atmospheric pressure photo-ionization (APPI)/atmospheric pressure chemical ionization (APCI) analyses. Furthermore, given the wide range of chemical functionalities and structural motifs in these compositionally complex mixtures, aggregation can similarly limit the observed species to a small (10-20%) mass fraction of the whole sample. Finally, irrespective of the ionization method, the mass analyzer must be capable of resolving tens-of-thousands of mass spectral peaks and provide the mass accuracy (typically 50-300 ppb mass measurement error) required for elemental composition assignment, and thus is generally limited to high-field Fourier transform ion cyclotron mass spectrometry (FT-ICR MS). Here, we describe three approaches to combat the above issues for (+) ESI, (-) ESI, and (+) APPI FT-ICR MS analysis of petroleum samples. Each approach relies on chromatographic fractionation to help reduce selective ionization discrimination and target either specific chemical functionalities (pyridinic and pyrrolic species (nitrogen) or carboxylic acids (oxygen)) or specific structural motifs (single aromatic core (island) or multi-core aromatics (archipelago)) known to be related to ionization efficiency. Each fractionation method yields a 2-10-fold increase in the compositional coverage, exposes species that are undetectable using direct "dilute and shoot" analysis, and provides coarse selectivity in chemical functionalities that can both increase the assignment confidence and optimize ionization conditions to maximize compositional coverage.

基于多端元光谱混合分析方法的大兴安岭火后植被盖度恢复研究

基于多端元光谱混合分析方法的大兴安岭火后植被盖度恢复研究

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.