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1.
The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R2 = 0.96), shrub cover (adj. R2 = 0.83), grass cover (adj. R2 = 0.71), bare soil (adj. R2 = 0.88), stone cover (adj. R2 = 0.83) and litter cover (adj. R2 = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies. 相似文献
2.
The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0 μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R2 of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R2 was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R2 = 0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R2 = 0.96, RMSE = 4.74% and RMSE cross validation RMSECV = 6.17%) followed by VNIR–SWIR reflectance spectra (R2 = 0.91, RMSE = 6.90% and RMSECV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R2 = 0.67, RMSE = 13.27% and RMSECV = 16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content. 相似文献
3.
Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity. 相似文献
4.
Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R670), (550 nm, R550), and (480 nm, R480), where Rλ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R670 − R550) − (670 − 550)(R670 − R480)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms. 相似文献
5.
Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg−1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg−1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg−1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra. 相似文献
6.
Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x0), center vertical coordinate (y0), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m3/m3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R2 = 0.395, RMSE = 0.061 m3/m3) compared to SSM measurements at the Yucheng Comprehensive Experimental Station. 相似文献
7.
Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5 μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jiménez-Muñoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332 K in the LST at desert and agriculture experimental sites with a rmse of 1.6 K and 2.7 K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300 K, however a cold bias up to 7.9 K was observed in MODIS LST for higher LST values (310–330 K) over the hot desert region. 相似文献
8.
Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. 相似文献
9.
Similar to vascular plants, non-vascular plant mosses have different periods of seasonal growth. There has been little research on the spectral variations of moss soil crust (MSC) over different growth periods. Few studies have paid attention to the difference in spectral characteristics between wet MSC that is photosynthesizing and dry MSC in suspended metabolism. The dissimilarity of MSC spectra in wet and dry conditions during different seasons needs further investigation. In this study, the spectral reflectance of wet MSC, dry MSC and the dominant vascular plant (Artemisia) were characterized in situ during the summer (July) and autumn (September). The variations in the normalized difference vegetation index (NDVI), biological soil crust index (BSCI) and CI (crust index) in different seasons and under different soil moisture conditions were also analyzed. It was found that (1) the spectral characteristics of both wet and dry MSCs varied seasonally; (2) the spectral features of wet MSC appear similar to those of the vascular plant, Artemisia, whether in summer or autumn; (3) both in summer and in autumn, much higher NDVI values were acquired for wet than for dry MSC (0.6 ∼ 0.7 vs. 0.3 ∼ 0.4 units), which may lead to misinterpretation of vegetation dynamics in the presence of MSC and with the variations in rainfall occurring in arid and semi-arid zones; and (4) the BSCI and CI values of wet MSC were close to that of Artemisia in both summer and autumn, indicating that BSCI and CI could barely differentiate between the wet MSC and Artemisia. 相似文献
10.
Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (Dg)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, Dg and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R2 = 0.86, p < 0.01), Dg (R2 = 0.79, p < 0.01) and WSA >0.25 mm (R2 = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and Dg were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation. 相似文献
11.
Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R2 = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China. 相似文献
12.
Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin). 相似文献
13.
This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 − 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m3 m−3, ubRMSE = 0.062 m3 m−3 for the IGBP-based calibration; against R = 0.54, bias = −0.034 m3 m−3 and ubRMSE = 0.070 m3 m−3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product. 相似文献
14.
Leaf carotenoids content (LCar) is an important indicator of plant physiological status. Accurate estimation of LCar provides valuable insight into early detection of stress in vegetation. With spectroscopy techniques, a semi-empirical approach based on spectral indices was extensively used for carotenoids content estimation. However, established spectral indices for carotenoids that generally rely on limited measured data, might lack predictive accuracy for carotenoids estimation in various species and at different growth stages. In this study, we propose a new carotenoid index (CARI) for LCar assessment based on a large synthetic dataset simulated from the leaf radiative transfer model PROSPECT-5, and evaluate its capability with both simulated data from PROSPECT-5 and 4SAIL and extensive experimental datasets: the ANGERS dataset and experimental data acquired in field experiments in China in 2004. Results show that CARI was the index most linearly correlated with carotenoids content at the leaf level using a synthetic dataset (R2 = 0.943, RMSE = 1.196 μg/cm2), compared with published spectral indices. Cross-validation results with CARI using ANGERS data achieved quite an accurate estimation (R2 = 0.545, RMSE = 3.413 μg/cm2), though the RBRI performed as the best index (R2 = 0.727, RMSE = 2.640 μg/cm2). CARI also showed good accuracy (R2 = 0.639, RMSE = 1.520 μg/cm2) for LCar assessment with leaf level field survey data, though PRI performed better (R2 = 0.710, RMSE = 1.369 μg/cm2). Whereas RBRI, PRI and other assessed spectral indices showed a good performance for a given dataset, overall their estimation accuracy was not consistent across all datasets used in this study. Conversely CARI was more robust showing good results in all datasets. Further assessment of LCar with simulated and measured canopy reflectance data indicated that CARI might not be very sensitive to LCar changes at low leaf area index (LAI) value, and in these conditions soil moisture influenced the LCar retrieval accuracy. 相似文献
15.
A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical/thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts−Ta Aqua-day and Ts−Ta Terra-day, respectively) were proposed and the results were compared with those obtained from a previously improved model (ΔTs Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS-derived EF data to SM values. Estimated SM values were then correlated with in situ SM measurements and their relationships were statistically analyzed. Results indicated statistically significant correlations between SM estimated from all three EF estimation approaches and field measured SM values (R2 = 0.42–0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts−Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R2 values and similar RMSEs) as compared with the ΔTs reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11 (vol/vol%)2. This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentially used to downscale SM estimations obtained from passive microwave remote sensing techniques. 相似文献
16.
Land use and land cover change are of prime concern due to their impacts on CO2 emissions, climate change and ecological services. New global land cover products at 300 m resolution from the European Space Agency (ESA) Climate Change Initiative Land Cover (CCI LC) project for epochs centered around 2000, 2005 and 2010 were analyzed to investigate forest area change and land cover transitions. Plant functional types (PFTs) fractions were derived from these land cover products according to a conversion table. The gross global forest loss between 2000 and 2010 is 172,171 km2, accounting for 0.6% of the global forest area in year 2000. The forest changes are mainly distributed in tropical areas such as Brazil and Indonesia. Forest gains were only observed between 2005 and 2010 with a global area of 9844 km2, mostly from crops in Southeast Asia and South America. The predominant PFT transition is deforestation from forest to crop, accounting for four-fifths of the total increase of cropland area between 2000 and 2010. The transitions from forest to bare soil, shrub, and grass also contributed strongly to the total areal change in PFTs. Different PFT transition matrices and composition patterns were found in different regions. The highest fractions of forest to bare soil transitions were found in the United States and Canada, reflecting forest management practices. Most of the degradation from grassland and shrubland to bare soil occurred in boreal regions. The areal percentage of forest loss and land cover transitions generally decreased from 2000–2005 to 2005–2010. Different data sources and uncertainty in the conversion factors (converting from original LC classes to PFTs) contribute to the discrepancy in the values of change in absolute forest area. 相似文献
17.
Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. 相似文献
18.
Significant structural damages to urban infrastructures caused by compaction of over-exploited aquifers are an important problem in Central Mexico. While the case of Mexico City has been well-documented, insight into land subsidence problems in other cities of Central Mexico is still limited. Among the cities concerned, we present and discuss the cases of five of them, located within the Trans-Mexican Volcanic Belt (TMVB): Toluca, Celaya, Aguascalientes, Morelia, and Queretaro. Applying the SBAS-InSAR method to C-Band RADARSAT-2 data, five high resolution ground motion time-series were produced to monitor the spatio-temporal variations of displacements and fracturing from 2012 to 2014. The study presents recent changes of land subsidence rates along with concordant geological and water data. It aims to provide suggestions to mitigate future damages to infrastructure and to assist in groundwater resources management.Aguascalientes, Celaya, Morelia and Queretaro (respectively in order of decreasing subsidence rates) are typical cases of fault-limited land subsidence of Central Mexico. It occurs as a result of groundwater over-exploitation in lacustrine and alluvial deposits covering highly variable bedrock topography, typical of horst-graben geological settings. Aguascalientes and Toluca show high rates of land subsidence (up to 10 cm/yr), while Celaya and Morelia show lower rates (from 2 to 5 cm/yr). Comparing these results with previous studies, it is inferred that the spatial patterns of land subsidence have changed in the city of Toluca. This change appears to be mainly controlled by the spatial heterogeneity of compressible sediments since no noticeable change occurred in groundwater extraction and related drawdown rates. While land subsidence of up to 8 cm/yr has been reported in the Queretaro Valley before 2011, rates inferior to 1 cm/yr are measured in 2013–2014. The subsidence has been almost entirely mitigated by major changes in the water management practices of the city, i.e., a 122 km long pipeline bringing surface water from an adjacent state allowed to cease pumping in half of the wells. 相似文献
19.
In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (RNIR − RGreen)/(RNIR + RGreen)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (Rx − Ry)/(Rx + Ry)), where Rx is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; Ry is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R2’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season. 相似文献
20.
Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture. 相似文献