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Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
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    Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa

    Abera, Temesgen AlemayehuHeiskanen, JanneMaeda, Eduardo EijiHailu, Binyam Tesfaw...
    16页
    查看更多>>摘要：While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001-2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.
      原文链接:
    • NSTL
    • Elsevier

    A revisit of global wind-sea and swell climate and variability using multiplatform altimeters

    Jiang, HaoyuYang, Zheng
    13页
    查看更多>>摘要：A well-performed (root mean square error approximate to 0.35 m) look-up table model was established to separate the wind-sea and swell significant wave height (H-s) using 10-m sea surface wind speed (U-10) and H-s data from altimeters. Based on this model, global wind-sea and swell climate and variability were investigated using 27-year jointly calibrated altimeter data. The climatology of the swell and wind-sea H-s (H-WS and H-SW) are in good agreement with those of the total H-s and U-10, respectively. This is because of the dominance of swells in the World Ocean with respect to both occurrence frequency and energy proportion and the close coupling between wind and wind-seas. The interannual variability of H-WS and H-SW are closely related to some climate indices, but the responses of wind-seas and swells are different for the same atmospheric oscillation because of the large propagation distances of swells. For long-term variability, significant positive/negative long-term trends of H-WS/H-SW were found almost all over the World Ocean. Such opposite trends can be explained by the positive/negative trends of U-10/H-s: a shorter fetch or duration of wind leads to a lower H-s, but a higher U-10 leads to higher wind-sea probability and energy weight. However, large uncertainty still exists in these trends and further exploration is needed.
      原文链接:
    • NSTL
    • Elsevier

    Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

    Zheng, ZhijiaDu, ShihongTaubenboeck, HannesZhang, Xiuyuan...
    23页
    查看更多>>摘要：Sand dunes are one of the most abundant aeolian landforms and play an important role in understanding how aeolian environments evolve. Since the 1970s, remote sensing has enabled large-scale investigations of dunes at comparatively low costs and with temporally continuous observations, which greatly advances our knowledge of aeolian systems. In this context, we provide a review of recent progress in three research topics that have been greatly facilitated by remote sensing techniques. These topics are 1) mapping sand extent and dune types, 2) dune pattern quantification, and 3) monitoring dune dynamics. Sand dune mapping was the early focus of aeolian geomorphologists, and continued progress has been made in refining classification schemes and developing advanced classification techniques. Dune pattern quantification can be resolved in two geomorphometric approaches, and a careful design that takes into consideration the image resolution, the data quality, and the uncertainty in dune discretization is necessary. Dune dynamics typically exhibit as dune migration, dune interactions, and dune fine-scale morphodynamics. The wide application of change detection algorithms, especially COSI-Corr, provides great insights into dune migration, while the exploration of dune interactions is still in its infancy. Future directions are highlighted in four key areas: unifying classification schemes regarding dune morphology, developing methods that are capable of recognizing diverse dune forms at large spatial extents, designing modularized workflows and more complex matching rules to quantify dune migration, and improving quantitative analysis of dune interactions.
      原文链接:
    • NSTL
    • Elsevier

    Applications of a thermal-based two-source energy balance model coupled to surface soil moisture

    Song, LishengDing, ZhonghaoKustas, William P.Xu, Yanhao...
    13页
    查看更多>>摘要：The two-source energy balance (TSEB) model using the land surface temperature (LST) as a key boundary has been used to estimate land surface evapotranspiration (ET) over various landcovers and environmental conditions. However, LST may not always provide an adequate boundary condition to simultaneously constrain the soil evaporation and plant transpiration especially under water limited conditions. A refinement to TSEB model by coupling surface soil moisture information to derive the soil and vegetation component temperatures and a new transpiration algorithm was developed (TSEB-SM). The TSEB-SM model was evaluated under a wide range of surface soil water content values and vegetation cover conditions and compared with the performance with the original TSEB model using only LST. While the results showed that the TSEB-SM model produced similar agreement in the fluxes and ET as the original TSEB for the cropland and grassland sites, TSEB-SM model performance was notably improved at the shrub-forest and desert steppe sites with a significant reduction in mean absolute percent difference in daily ET from nearly 65% to 25% and from approximately 50% to 40%, respectively. It also appears to be more reliable in partitioning ET into soil evaporation and plant transpiration when compared to the partitioning using the water use efficiency (uWUE) approach in combination with the eddy covariance measurements. With satellite data such as MODIS LST and leaf area index, and surface soil moisture retrievals from microwave satellite observations, the TSEB-SM model may potentially be a more reliable tool for monitoring regional ET partitioning under sparse canopy cover conditions.
      原文链接:
    • NSTL
    • Elsevier

    Monitoring standing herbaceous biomass and thresholds in semiarid rangelands from harmonized Landsat 8 and Sentinel-2 imagery to support within-season adaptive management

    Kearney, Sean P.Porensky, Lauren M.Augustine, David J.Gaffney, Rowan...
    15页
    查看更多>>摘要：Adaptive management requires rangeland managers to respond to changing forage conditions (e.g., standing herbaceous biomass) within the grazing season at the scale of individual pastures. While within-season biomass can be measured or estimated in the field, it is often impractical to make field measurements in extensive rangeland systems with adequate frequency and spatial representation for responsive decision-making by rangeland managers. We sought to develop a single model to accurately predict daily herbaceous biomass across seasonally and annually varying conditions from the Harmonized Landsat-Sentinel satellite time series. We also sought to assess how information about plant community composition derived from a high-spatial resolution map would improve model performance. We used herbaceous biomass data from 1764 ground observations collected over 8 years in North American shortgrass steppe for training in a cross-validated model selection approach to evaluate (1) predictive performance of candidate models both spatially and temporally, (2) relative variable importance of individual spectral bands, vegetation indices, and recently developed broadband spectral angle indices, and (3) the degree to which including plant community composition improved model performance. All 11 candidate models identified in the model selection procedure contained a band angle index and an individual spectral band, and 6 contained one of each input feature type, demonstrating the benefit of combining spectral features for predicting herbaceous biomass across varying conditions. The spatial and temporal cross-validation and selection procedures produced the same top model with similar performance (mean absolute error = 151-182 kg ha(-1); R-2 = 0.75-0.79), suggesting that a single model performs well over space and time. Including plant community composition in the model reduced mean absolute error by 11-13%. Bootstrapping revealed that -six to seven years of training data were required to achieve consistent model performance across years with varying environmental conditions (e.g., wet, average, dry). The top model could accurately detect (70-87% accuracy) the week that biomass dropped below management-related thresholds as low as 450 kg ha(-1) in an independent dataset (n = 950) with modest commission error (10-26%). We discuss how maps showing the probability that herbaceous biomass is below a given threshold can support adaptive management in extensive semiarid rangelands across differing scenarios of risk perception and avoidance. In addition to producing maps to support precision rangeland management strategies, this study demonstrates the importance of combining complementary vegetation indices and acquiring long-term training datasets to achieve reliable predictions of herbaceous standing biomass in highly variable systems.
      原文链接:
    • NSTL
    • Elsevier

    Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

    Thornley, RachaelGerard, France F.White, KevinVerhoef, Anne...
    18页
    查看更多>>摘要：According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits.
      原文链接:
    • NSTL
    • Elsevier

    Direct estimation of photosynthetic CO2 assimilation from solar-induced chlorophyll fluorescence (SIF)

    Yu, QiangWang, YunfeiPeng, XiongbiaoCai, Huanjie...
    14页
    查看更多>>摘要：Much progress has been made in predicting terrestrial gross primary productivity (GPP) from solar-induced chlorophyll fluorescence (SIF). However, SIF-GPP relationships are mostly built by statistically relating top-of-canopy (TOC) SIF observations (SIFTOC) to eddy covariance flux-tower GPP estimates. We developed a process-based model, based on the mechanistic light response (MLR) model, to mechanistically link SIFTOC with the photosynthetic activity of vegetation. To apply this mechanistic model at the canopy scale, we 1) reformulate the equations by replacing the fraction of open PSII reaction centers (q(L)) and the maximum quantum yield of photosystem II (Phi(Pmax)) with nonphotochemical quenching (NPQ) and the quantum yield of photosystem II (Phi(P)); 2) reconstruct hemispherical broadband SIF fluxes at photosystem II (PSII) from the directional observed SIFTOC that is contributed from photosystem I and II; 3) estimate other key parameters including KDF (ratio between the rate constants for constitutive heat loss and fluorescence), C-c (chloroplastic CO2 partial pressure), and Gamma* (chloroplastic compensation point of CO2) at the canopy scale based on assumptions and in-situ measurements. A comparison against flux-tower based GPP at a winter-wheat study site, demonstrates that the modeled GPP, driven by SIFTOC at 760 nm, air temperature, incoming photosynthetically active radiation (PAR), and directional reflectance in the red and near-infrared region, is able to quantify canopy photosynthesis with good accuracy at both half-hourly (R-2 = 0.85, RMSE = 5.62 mu mol m(-2) s(-1), rRMSE = 9.10%) and daily (R-2 > 0.90, RMSE = 3.25 mu mol m(-2) s(-1) and rRMSE = 8.69%) scales. The present model enhances our ability to mechanistically estimate GPP with SIF at the canopy scale, an essential step to model carbon uptake using satellite SIF at regional and global scales.
      原文链接:
    • NSTL
    • Elsevier

    Mapping invasive alien species in grassland ecosystems using airborne imaging spectroscopy and remotely observable vegetation functional traits

    Gholizadeh, HamedFriedman, Michael S.McMillan, Nicholas A.Hammond, William M....
    17页
    查看更多>>摘要：Lespedeza cuneata (sericea lespedeza; hereafter "sericea") is an invasive species brought to the U.S. from East Asia in the 1890s to be used as forage. However, it has now become a growing ecological and economic threat in grasslands of several states in the U.S. southern Great Plains including Oklahoma, Kansas, Missouri, and Nebraska. Here, we demonstrate the capability of airborne imaging spectroscopy to map sericea in a large natural grassland within the Tallgrass Prairie Preserve, the largest protected tallgrass prairie in the world, located in northeastern Oklahoma. Through this research, we investigated which remotely observable vegetation functional traits (referring to biochemical, physiological, and structural traits) contribute to distinguishing sericea from cooccurring native species and whether we can detect sericea remotely through quantifying these functional traits using imaging spectroscopic data (also known as hyperspectral data). To achieve these objectives, full-range airborne hyperspectral data with spatial resolution of 1 m were collected from the study area in August 2020. In addition, a total of 12 vegetation functional traits were measured through field sampling for model development. We first identified functional traits that contributed to separating sericea from other species, and then used them in a classification model to detect sericea in our study site. We found total carotenoids (sum of neoxanthin, violaxanthin, antheraxanthin, zeaxanthin, and lutein), chlorophyll a + b (sum of chlorophyll a and chlorophyll b), total nitrogen, canopy height, potassium, and magnesium as the main functional traits contributing to the detection of sericea; an overall classification accuracy of approximately 94% was reported. However, the proposed approach overestimated sericea cover in species-rich plant communities. Overall, our findings demonstrated an essential role for airborne remote sensing in 1) direct mapping of invasive plants and 2) quantifying functional traits associated with success strategies of invasive species. Eventually, experiments like ours can aid in developing large-scale and science-driven management practices to both identify the current extent, and to control the spread of invasive species in grasslands and similar short-stature environments. This will not only improve management practices but will have major societal and economic benefits.
      原文链接:
    • NSTL
    • Elsevier

    A new nonlinear method for downscaling land surface temperature by integrating guided and Gaussian filtering

    Guo, FengxiangHu, DieSchlink, Uwe
    14页
    查看更多>>摘要：Land surface temperature (LST), retrieved from thermal infrared (TIR) bands of remote sensing satellites, is an important parameter for various climate and environmental models. TIR bands detect a range of low-energy wavelengths, resulting in a coarser spatial resolution than other multispectral bands, and limiting applicability in heterogeneous urban regions. In this study, a new nonlinear method for LST downscaling, called Three Layers Composition (TLC), was proposed. The TLC integrates large-scale temperature variations, re-constructed detailed characteristics of LSTs, and strong boundary information. The performance of TLC is compared with disaggregation of radiometric surface temperature (DisTrad), thermal imagery sharpening (TsHARP), and random forest (RF) for a complex landscape in Beijing city, which has agriculture, forest, and massive impervious surfaces. The scale effects on the downscaled LSTs (DLST) were analyzed from the aspects of spatial resolution and spatial contexts. The experimental results indicate that the nonlinear algorithms (TLC and RF) perform better than linear methods (DisTrad and TsHARP). Indicated by coefficient of determination (R-2), centered root-mean square error (CRMSE), and correlation coefficient (CC), TLC (R-2 = 0.901, CRMSE = 0.319, CC = 0.951) was the most effective and workable technique for predicting LSTs, followed by RF (0.768, 0.502, 0.874), TsHARP (0.544, 0.652, 0.734), and DisTrad (0.518, 0.751, 0.719). Larger experimental regions and larger ratios between initial and target resolution weaken the accuracy of DLST. TLC indicated a stronger ability to resist the influence of such scale effects. Traditional downscaling methods (DisTrad, TsHARP, and RF) are trained with global LSTpredictor relationships and predict the DLST point by point, which can result in significantly biased estimates for very high or very low temperatures. Addressing this issue, TLC advantageously preserves the texture similarity between LST and its predictors, and yields more precise DLST, which showed higher consistency with the reference LST. Considering high accuracy and low computation time, TLC may be a safe technique for LST downscaling in other regions and different remote sensing sensors.
      原文链接:
    • NSTL
    • Elsevier

    Non-linearity between gross primary productivity and far-red solar-induced chlorophyll fluorescence emitted from canopies of major biomes

    Liu, YihongChen, Jing M.He, LimingZhang, Zhaoying...
    15页
    查看更多>>摘要：Solar-induced chlorophyll fluorescence (SIF) observed from vegetation has been considered as a promising proxy of gross primary productivity (GPP) in several studies, and recent work has shown advantages of using the total emitted SIF (SIFtotal) to capture the variation of GPP. The non-linearity between SIF and GPP at the canopy level was often observed in many previous studies, but the non-linearity between SIFtotal and GPP has not yet been systematically investigated. In this study, based on a theoretical analysis of the relationship between SIF and GPP at the leaf level and how it propagates to the canopy level with the consideration of sunlit and shaded leaf fractions in the canopy, we asserted that non-linear relationships between SIFtotal and GPP at the canopy level are general and physically sound. We derived SIFtotal from two different approaches using TROPOMI SIF data. One is based on the canopy escape ratio of SIF signals observed by satellite sensors (SIFobs), and the other is an angular normalization method. At the site level, we established linear and non-linear (exponential, hyperbolic, and polynomial) regressions between TROPOMI SIF and GPP data from 25 flux tower sites covering the eight biomes. The results indicate that non-linearity between SIFtotal and GPP exists among eight major biomes, and expo-nential regression is the best regression method to capture the non-linearity between SIF and GPP at the site level. We developed a simple variable, SIFnon-linear, to capture the non-linearity between SIFtotal and GPP. R-2 values of the linear correlations between SIFnon-linear and GPP are equal or close to those of exponential corre-lations between SIFtotal and GPP. SIFnon-linear captures the non-linearity well, even though different biomes have different degrees of non-linearity. On the global scale, TROPOMI SIF was compared with GPP simulations of the BEPS model and the SMAP GPP product, showing that SIFnon-linear is a better proxy of GPP than SIFobs and SIFtotal. We find that the non-linearity between SIFtotal and GPP generally exists among eight major biomes regardless of the spatial or temporal scales. The non-linearity is driven by the seasonal variation of the absorbed photosyn-thetically active radiation (APAR). The degree of the non-linearity varies among different biomes, and it is controlled by three factors: the light saturation point of GPP, the variation of APAR during the growing season, and the ratio between the sunlit and shaded portion of the canopy. Temporal aggregation of SIFtotal reduces the non-linearity. Our results indicate that the simple variable SIFnon-linear can replace SIFobs or SIFtotal to improve global SIF-GPP related studies in the future.
      原文链接:
    • NSTL
    • Elsevier