Xuehong Chen

Do flowers affect biomass estimate accuracy from NDVI and EVI

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (pu2009=u20090.17) or between above-ground biomass and NDVI (pu2009=u20090.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSEu2009=u200943 g m−2 for NDVI and RMSEu2009=u200943 g m−2 for EVI, both p < 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.

Practical image fusion method based on spectral mixture analysis

Conventional image fusion algorithm, such as IHS, SVR, PCS, etc., may show some defects in inheriting the higher-spectral information embedded in the original lower-spatial resolution MS image. A fusion method based on spectral mixture analysis (FSMA) was proposed in previous study, which has potential in solving this problem. While published results are limited to well-behaved simulated data where the endmembers are known a priori and the FSMA method will not work well when applying to real remotely sensed images because the estimated reflectance ranging in panchromatic band derived from MS bands cannot be treated as the real panchromatic values. In this paper, an improved image fusion method based on spectral mixture analysis (IFSMA) is proposed, in which the original FSMA method was extended to real remotely sensed images by modifying the objective function of the constrained nonlinear optimization expressions. It was compared with the original FSMA, Zhang’s SVR, PCS and IHS method, and results indicated that the IFSMA method was superior to other methods in preserving the spectral and spatial information.

An inherent limitation of solar-induced chlorophyll fluorescence retrieval at the O 2 -A absorption feature in high-altitude areas

The Fraunhofer line discriminator (FLD) principle applied on the atmospheric oxygen absorption feature around 761 nm ( O2-A band) has been widely used to retrieve solar-induced chlorophyll fluorescence (Fs) from remotely sensed data. In this letter, however, we address a violation of the basic assumption caused by O2 absorption feature changes, and we evaluate the impact of diminishing O2 absorption with the increase in ground altitude on Fs retrieval accuracy. The Fs retrieval accuracy substantially decreases in higher ground altitude areas for the standard FLD and three-band FLD methods, in which relative estimation errors increase approximately 70%-80% and 10%-16%, respectively, with an increase in ground altitude from 0.01 to 4.5 km. However, this increasing trend in Fs retrieval error does not occur with the use of the improved FLD (iFLD) method, which exhibits smaller than 5% of changes in relative estimation errors. Analytical analyses reveal the causes of the changes in Fs retrieval accuracy by the three methods. Based on these findings, the iFLD method is recommended to be used in cross-altitude studies or for Fs estimation in higher ground altitude areas under conditions of low radiometric noise, although its high sensitivity to noise should be taken into account. The investigations in this letter further indicate that the impact of ground altitude should be included in the uncertainty budgets of Fs retrievals and should be considered in the interpretation of Fs signals at the O2-A absorption feature.

Land-use/land-cover change detection using change-vector analysis in posterior probability space

Land use/land cover change is an important field in global environmental change research. Remote sensing is a valuable data source from which land use/land cover change information can be extracted efficiently. A number of techniques for accomplishing change detection using satellite imagery have been formulated, applied, and evaluated, which can be generally grouped into two types. (1) Those based on spectral classification of the input data such as post-classification comparison and direct two-date classification; and (2) those based on radiometric change between different acquisition dates. The shortage of type 1 is cumulative error in image classification of an individual date. However, radiometric change approaches has a strict requirement for reliable image radiometry. In light of the above mentioned drawbacks of those two types of change detection methods, this paper presents a new method named change vector analysis in posterior probability space (CVAPS). Change-vector analysis (CVA) is one of the most successful radiometric change-based approaches. CVAPS approach incorporates post-classification comparison method and CVA approach, which is expected to inherit the advantages of two traditional methods and avoid their defects at the same time. CVAPS includes the following four steps. (1) Images in different periods are classified by certain classifier which can provide posterior probability output. Then, the posterior probability can be treated as a vector, the dimension of which is equal to the number of classes. (2) A procedure similar with CVA is employed. Compared with traditional CVA, new method analyzes the change vector in posterior probability space instead of spectral feature space. (3) A semiautomatic method, named Double-Window Flexible Pace Search (DFPS), is employed to determine the threshold of change magnitude. (4) Change category is discriminated by cosines of the change vectors. CVAPS approach was applied and validated by a case study of land use change detection in urban area of Shenzhen, China using multi-temporal TM data. Kappa coefficients of change/no-change detection and from-to types of change detection were employed for accuracy assessment. The experimental results show that CVAPS outperform than post-classification comparison method and can avoid cumulative error effectively. Besides, radiometric correction is not needed in this method compared with traditional CVA. Therefore, it is indicated that CVAPS is potentially useful in land-use/land-cover change detection.

A Novel Cloud Removal Method Based on IHOT and the Cloud Trajectories for Landsat Imagery

Cloud removal is a prerequisite for the application of Landsat datasets, as such satellite images are invariably contaminated by clouds. Clouds affect the transmission of radiation signal to different degrees because of their different thicknesses, shapes, heights and distributions. Existing methods utilize pixel replacement to remove thick clouds and pixel correction techniques to rectify thin clouds in order to retain the land surface information in contaminated pixels. However, a major limitation of these methods refers to their deficiency in retrieving land surface reflectance when both thick clouds and thin clouds exist in the images, as the two types of clouds differ in the transmission of radiation signal. As most remotely sensed images show rather complex cloud contamination patterns, an efficient method to alleviate both thin and thick cloud effects is in need of development. To this end, the paper proposes a new method to rectify cloud contamination based on the cloud detection of iterative haze-optimized transformation (IHOT) and the cloud removal of cloud trajectory (IHOT-Trajectory). The cloud trajectory is able to take consideration of signal transmission for different levels of cloud contamination, which characterizes the spectral response of a certain type of land cover under increasing cloud thickness. Specifically, this method consists in four steps. First, the cloud thicknesses of contaminated pixels are estimated by the IHOT. Second, areas affected by cloud shadows are marked. Third, cloud trajectories are fitted with the aid of neighboring similar pixels under different cloud thickness. Last, contaminated areas are rectified according to the relationship between the land surface reflectance and the IHOT. The experimental results indicate that the proposed approach is able to effectively remove both the thin and thick clouds and erase the cloud shadows of Landsat images under different scenarios. In addition, the proposed method was compared with the dark object subtraction (DOS), the modified neighborhood similar pixel interpolator (MNSPI) and the multitemporal dictionary learning (MDL) methods. Quantitative assessments show that the IHOT-Trajectory method is superior to the other cloud removal methods overall. For specific spectral bands, the proposed method performs better than other methods in visible bands, whereas it does not necessarily perform better in infrared bands.

Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000-2016 on the Tibetan Plateau

Climate warming on the Tibetan Plateau tends to induce an uphill shift of temperature isolines. Observations and process-based models have both shown that climate warming has resulted in an increase in vegetation greenness on the Tibetan Plateau in recent decades. However, it is unclear whether the uphill shift of temperature isolines has caused greenness isolines to shift upward and whether the two shifts match each other. Our analysis of satellite observed vegetation greenness during the growing season (May-Sep) and gridded climate data for 2000-2016 documented a substantial mismatch between the elevational shifts of greenness and temperature isolines. This mismatch is probably associated with a lagging response of greenness to temperature change and with the elevational gradient of greenness. The lagging response of greenness may be associated with water limitation, resources availability, and acclimation. This lag may weaken carbon sequestration by Tibetan ecosystems, given that greenness is closely related to primary carbon uptake and ecosystem respiration increases exponentially with temperature. We also found that differences in terrain slope angle accounted for large spatial variations in the elevational gradient of greenness and thus the velocity of elevational shifts of greenness isolines and the sensitivity of elevational shifts of greenness isolines to temperature, highlighting the role of terrain effects on the elevational shifts of greenness isolines. The mismatches and the terrain effect found in this study suggest that there is potentially large micro-topographical difference in response and acclimation/adaptation of greenness to temperature changes in plants. More widespread in situ measurements and fine-resolution remote sensing observations and fine-gridded climate data are required to attribute the mismatch to specific environmental drivers and ecological processes such as vertical changes in community structure, plant physiology, and distribution of species.

Asymmetric Responses of the End of Growing Season to Daily Maximum and Minimum Temperatures on the Tibetan Plateau

Climate warming has delayed the end of the growing season (EOS) in temperate and cold ecosystems. However, it is unclear whether asymmetric warming (higher warming at night than during the day) has triggered different responses in the timing of EOS. Here we used satellite-observed EOS of alpine vegetation to reveal its asymmetric responses to nighttime and daytime warming on the Tibetan Plateau. Increased preseason minimum temperature could postpone EOS by 7.92dayK(-1) (P<0.01), probably by slowing low-temperature induced leaf senescence, whereas increased preseason maximum temperature could advance EOS by 3.57dayK(-1) (P<0.05), likely due to the confounding effects of water limitations. The delaying effect of nighttime warming was stronger in more arid areas of the plateau, where daytime warming has a stronger advancing effect on EOS. Our results provide new insights into understanding and modeling autumn vegetation phenology on the Tibetan Plateau and grassland ecosystems in other temperate and cold regions.

An Orthogonal Fisher Transformation-Based Unmixing Method Toward Estimating Fractional Vegetation Cover in Semiarid Areas

Remote estimation of fractional vegetation cover (FVC) in arid and semiarid areas is crucial for understanding their roles in global climate changes and maintaining their ecological sustainability. Among the existing algorithms for remote estimation of FVC, the linear spectral mixture analysis (LSMA) has been widely adopted owing to its simplicity and flexibility. However, the spectral variability of endmembers is still a big challenge that would largely decrease the estimation accuracy of LSMA. In this letter, we proposed a novel unmixing algorithm by integrating an orthogonal Fisher transformation into the LSMA (fLSMA). Two evaluation experiments were conducted: one was based on simulations; the other was based on a field survey in Xilingol grassland, China. The proposed fLSMA yielded remarkably higher accuracies and precisions than the conventional LSMA (cLSMA), weighted SMA (wSMA) in the first experiment. In the second experiment, a root-mean-square error (RMSE) of 0.11 was derived for the fLSMA, compared with the RMSE values larger than 0.36 for the cLSMA and wSMA. Although the performance of fLSMA was somehow similar to the multiple endmember SMA (MESMA) in the two evaluation experiments, the fLSMA was much less time-consuming than the MESMA in massive computations. The results indicate the potential of the proposed fLSMA in long-term monitoring of FVC in semiarid areas based on satellite observations.