Photosynthetic Vegetation Cover Research Articles

Effect of shade on simultaneous estimation of non-photosynthetic and photosynthetic vegetation cover using the NDVI-NSSI normalized difference triangular space

ABSTRACT The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV.

Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives

Time series data provided by the Sentinel-2 and Landsat satellite missions offer manifold opportunities for grassland monitoring. The high intra-annual observation density of Sentinel-2 combined with the continuous long-term data record of Landsat enable analyses at seasonal, annual, and decadal scales. Fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil provide essential information to describe grassland conditions and processes. Yet, retrieving consistent grassland fractional cover time series from Landsat and Sentinel-2 imagery represents a major challenge. In this study, we implemented a multisensor spectral unmixing approach for retrieving multidecadal (i.e., 1984 to 2021) fractional cover time series of PV, NPV, and soil for Germany's permanent grasslands from the Landsat and Sentinel-2 archives. The spectral consistency of Landsat 5/7/8 and Sentinel-2A/B imagery as well as the coherency of a Sentinel-2-based spectral library to be used across Landsat and Sentinel-2 sensors served as the foundation for implementing the unmixing approach. We then employed regression-based unmixing using synthetic training data from spectral libraries for developing spatially and temporally generalized models. Applying these models to the Landsat and Sentinel-2 data facilitated multidecadal fractional cover mapping at a national-scale. We evaluated the quality of our multidecadal grassland fractional cover time series using statistical validation and linear correspondence analysis. The statistical validation was based on a multitemporal reference dataset spanning 2017 to 2021, derived from very high-resolution (VHR) imagery. Landsat 7/8- and Sentinel-2A/B-derived fractions showed similar Mean Absolute Errors (MAEs), i.e., 0.067 and 0.08 for PV, 0.149 and 0.15 for NPV, and 0.135 and 0.129 for soil. Linear correspondence analysis confirmed consistent PV and NPV fractional cover estimates among Landsat and Sentinel-2 sensors, suggesting similar errors beyond the statistical validation period. However, higher errors and weaker linear correspondence pointed to remaining uncertainties in soil fractional cover estimates. We further showed that the differences in spatial and spectral resolutions, i.e., the pixel size and the number of spectral bands, between Landsat and Sentinel-2 had a minor effect and were well mitigated by the spectral unmixing approach. We finally illustrated the value of the dense time series available for more recent years for describing seasonal trajectories of grassland conditions and land use intensities, as well as the use of the entire time series for analyzing long-term grassland dynamics based on annual fraction anomalies. Our study emphasizes the efficacy of generalized multisensor spectral unmixing approaches for retrieving consistent PV, NPV, and soil cover fractions across space, time, and sensors, providing a valuable means for grassland monitoring.

Spatiotemporal variability of photosynthetic and non-photosynthetic vegetation under climate change in arid and semiarid regions in China

ABSTRACT Understanding the spatiotemporal characteristics of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is critical for the study of vegetation in arid and semiarid regions. However, the evaluation of the fractional cover of PV (f PV) and NPV (f NPV) in Xinjiang has received very little attention. Thus, this study aims to evaluate the spatiotemporal variation characteristics of PV and NPV and their responses to precipitation and temperature in Xinjiang from 2001 to 2020. The results showed that the overall stability of f PV and f NPV were of the low fluctuation and high fluctuation types, respectively. A significant increase trend of f PV was found in most regions (P < 0.01), while f NPV showed an insignificant increase trend in general, except for eastern Xinjiang (P > 0.05). The comparison of f PV and f NPV shows that the vegetation of Xinjiang has improved over time, except for the Yili region, showing the need to strengthen the environmental protection of this region. Through the spatial analysis, some agricultural oasis areas experienced an expansion in the distribution of vegetation, while some sparse vegetation distribution area has not improved. From the climate perspective, the response degree of f PV and f NPV with precipitation is higher than that of temperature. The responses of f PV and f NPV to precipitation are greater in the summer and spring phases, respectively. Whereas, the responses of both f PV and f NPV to temperature are greater in the autumn phase. In the time phase, precipitation mainly promotes the distribution of PV and NPV, while temperature mainly inhibits their distribution. This study provides a theoretical basis for ecological restoration and conservation in Xinjiang and other arid and semiarid regions worldwide.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events.

Limited Evidence of Cumulative Effects From Recurrent Droughts in Vegetation Responses to Australia's Millennium Drought

AbstractDrought‐induced vegetation declines have been reported across the globe and may have widespread implications for ecosystem composition, structure, and functions. Thus, it is critical to maximizing our understanding of how vegetation has responded to recent drought extremes. To date, most drought assessments emphasized the importance of drought intensity for vegetation responses. However, drought timing, duration, and repeat exposure all may be important aspects of ecosystem response with the potential for non‐linear effects. Cumulative effects are one such phenomenon, representing the additional decline due to repeated exposure to drought, and indicating gradual loss of ecosystem resistance. This study quantifies the frequency and magnitude of cumulative effects among Australian ecosystems as they responded to the Millennium Drought. Three distinct biophysical variables derived from satellite remote sensing were analyzed, including fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, and canopy density derived from passive microwave data. Cumulative effects were detected in only 8%–20% of the fire‐free landscape exposed to repeat or long‐duration drought, and could be a statistical artifact. In those limited cases, they approximately doubled drought impacts on leaf abundance, canopy cover, and vegetation density. Cultivated lands and grasslands were the most susceptible to cumulative effects, losing resistance to recurrent droughts, but could be false discovery. Despite being relatively infrequent in forests and savannas, cumulative effects caused larger additional declines in these ecosystems. Overall, our study demonstrates that repeated exposure appears to have limited influence on the magnitude of drought impacts on canopy structure affecting only a few areas.

Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery

In vegetation-abundant ecosystems, accurate fractional cover estimation of multiple targets, including non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV) and bare soil (BS) is an important indicator of carbon storage and climate change, and a contributing factor to soil and water conservation, wildfire, and drought. Satellite remote sensing began with sensors limited to the visible and near infrared (VNIR, 380 nm to 1000 nm), and advancing to sensors spanning the whole solar domain (350 nm to 2500 nm) over the last five decades. However, NPV is still difficult to distinguish from BS in satellite images covering large spatial areas, since their spectral reflectances are always mixed and have subtly different spectral features. Many spectral indices have been proposed to identify NPV, however, most include a short-wave infrared band which is lacking on many current and historical spaceborne sensors. To fully utilize current and historical airborne/satellite images with only VNIR bands, a novel spectral index – the VNIR spectral shape index (SSI) – is proposed in this paper. Using spectra of PV, NPV, and BS acquired from world-recognized spectral libraries, the SSI was devised using only blue, green, red, and NIR bands. When applied to Sentinel-2 MSI images, the SSI was effective in estimating/mapping the fractional cover of PV, NPV, and BS using the triangular space method. The resulting fractional cover was evaluated in three 3 km × 2 km study areas including one grassland, one woodland, and one cropland. The spectral reflectance collected by spectroradiometer in field work and digital image collect by digital camera mounted on unmanned aerial vehicle (UAV) support that the triangular space method is able to estimate the fractional cover of photosynthetic and non-photosynthetic vegetation simultaneously, and that endmember selection relying solely on satellite images is reasonable. Comparing the abundance of each target in UAV images to corresponding MSI pixels, the SSI index and triangular space method proved effective for fractional cover estimation in varied vegetated ecosystems. For fractional cover evaluation in grassland, woodland, and cropland, the R2 reaching to 0.59, 0.62, and 0.70, respectively. These results demonstrate that the SSI is feasible for multiple sensors, enabling potential large temporal and/or spatial scale applications.

A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series

Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential detail to understand how grasslands are affected by recent and future drought periods in the 21st century. In this regard, Sentinel-2A/B offer frequent large-area observations, which have not yet been fully exploited for a spatially continuous drought monitoring of highly dynamic Central European grasslands. In this study, we developed a generalized drought monitoring framework for Central European grasslands linking Sentinel-2 data, field survey information, and spectral unmixing. We first implemented a consistent and repeatable strategy to obtain a grassland spectral library supported by the Europe-wide Land Use/Cover Area frame statistical Survey (LUCAS) and multitemporal Sentinel-2 data. Our library captured the spectral variability of PV, NPV, and soil cover from 12 grassland areas distributed along typical environmental and land use gradients of Central Europe. We trained a generalized regression-based unmixing model with synthetic data generated from the spectral library and compared fractional cover estimates to a multitemporal reference dataset. PV, NPV, and soil were estimated with good accuracy, achieving MAEs of 6.54%, 13.7%, and 12.2%, respectively. Local unmixing models trained on area-specific library subsets were overall outperformed by the generalized model highlighting the value of a comprehensive grassland library for generalized spectral unmixing. Based on fractional cover time series from 2017 to 2021, we calculated time series of the grassland-specific Normalized Difference Fraction Index (NDFI) capturing proportions of NPV and soil relative to PV. Comparison of annual growing season drought metrics derived from the NDFI to annual meteorological drought statistics from the Standardized Precipitation Evapotranspiration Index (SPEI) as well as the Soil Moisture Index (SMI) revealed widespread drought impacts on grasslands during the persistent drought period in Central Europe from 2018 to 2020. While impacts on grasslands overall closely followed meteorological and soil drought conditions, regionally varying drought metrics underline that local to regional environmental and hydrological conditions shaped the drought response of Central European grasslands. Our study emphasizes the value of combining Sentinel-2 data, field survey information, and spectral unmixing to enable drought monitoring across grassland gradients of Central Europe with Sentinel-2 time series.

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

Non-photosynthetic vegetation (NPV) is an essential component in various vegetation-soil ecosystems. Both phenology and disturbance lead to a transition from photosynthetic vegetation to NPV and vice versa. Due to the similar spectral reflectance of NPV and bare soil (BS) in the visible-near infrared region (400–1000 nm), NPV and BS separation is relying on the shortwave infrared (SWIR) bands in most cases. The lignin and cellulose absorption feature is around 2100 nm, which is the most distinctive feature of NPV. However, the water absorption feature is much stronger in the SWIR, increasing the difficulty for NPV-BS separation when wet. Recently, Sentinel-2/3 satellites add more bands in the near infrared (NIR), which provide an extra opportunity for index building and application. Based on the difference captured by derivative spectra, a spectral index, NPV-Soil Separation Index (NSSI), is proposed to realize the separation using two NIR bands within 750–900 nm range in this study. Using spectra of photosynthetic vegetation (PV), NPV, and BS acquired from world-recognized spectral libraries, NSSI is built and validated as effective for lab-collected data. With the triangle method, one of the linear spectral unmixing methods, the fractional cover of PV, NPV, and BS can be estimated. Over a woodland study area, the fractional cover retrieved by cellulose absorption index (CAI) and NDVI combination of ZY1-02D AHSI hyperspectral image is 26.41%, 37.56%, 36.03% for PV, NPV, and BS in order. With the proposed NSSI-NDVI combination, the corresponding estimated fractional cover is 23.31%, 38.44%, 38.25% using Sentinel-2 MSI and 24.58%, 36.74%, and 38.68% using Sentinel-3 OLCI image. The comparable validation result confirms that the proposed NSSI is effective for NPV-BS separation. Moreover, the triangle method of NSSI-NDVI combination is applied on both grassland and cropland images to examine its feasibility on varied types of typical vegetation-soil ecosystems, and the well-built triangular space supports its feasibility. Relying on NIR bands, NSSI can avoid strong water absorption in the SWIR. Also, the feasibility of NSSI being used on multiple multispectral satellite sensors, especially the Sentinel series, makes continuous mapping for NPV over a large spatial scale possible.

Drought Impacts on Australian Vegetation During the Millennium Drought Measured With Multisource Spaceborne Remote Sensing

AbstractDuring the period from 1997 to 2009, Australia experienced a severe and persistent drought known as the millennium drought (MD). Major water shortages were reported across the continent, and there were some field accounts of tree mortality and dieback, but large‐area assessment has been lacking. Given uncertain projections of future drought conditions in South‐East Australia, analysis of the MD presents a valuable opportunity to assess possible impacts of these future trends. In this study, we analyzed the magnitude and sensitivity of vegetation responses to the MD with satellite‐derived information including the fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, canopy density derived from vegetation optical depth, and aboveground biomass carbon. Results show that the most severe impacts were concentrated in southeastern Australia where all four biophysical variables exhibited the highest absolute declines. Cultivated lands, followed by grasslands, experienced the largest drought impact and also demonstrated the highest sensitivity to drought compared to natural biomes. Shrublands showed the greatest resistance to drought‐induced declines. Forests exhibited the lowest sensitivity in canopy properties but average drought sensitivity for biomass, similar to other natural biomes. More arid settings tended to have higher drought sensitivity for canopy properties but lower sensitivity to biomass loss. Drought‐induced carbon releases were largest in forested ecosystems and in more humid climatic settings. High sensitivity of forest biomass to drought suggests that a large amount of carbon could be vulnerable to release to the atmosphere.

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, based on the MODIS NBAR data from 2001 to 2015. The unmixing results were validated through comparison with the field samples. The results show the method adopted could acquire rational and accurate estimation of fractional cover of photosynthetic vegetation (R2 = 0.6297, RMSE = 0.2443) and non-photosynthetic vegetation (R2 = 0.3747, RMSE = 0.2568). The dataset could provide key data support for the users in land degradation surveillance fields.

Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation

ABSTRACTThis study explores the use of the relationship between the normalized difference vegetation index (NDVI) and the shortwave infrared ratio (SWIR32) vegetation indices (VI) to retrieve fractional cover over the structurally complex natural vegetation of the Cerrado of Brazil using a time series of imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Data from the EO-1 Hyperion sensor with 30 m pixel resolution is used to sample geographic and seasonal variation in NDVI, SWIR32, and the hyperspectral cellulose absorption index (CAI), and to derive end-member values for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS) from a suite of protected and/or natural vegetation sites across the Cerrado. The end-members derived from relatively pure 30 m pixels are then applied to a 500 m pixel resolution MODIS time series using linear spectral unmixing to retrieve PV, NPV, and BS fractional cover (FPV, FNPV, and FBS). The two-way interaction response of MODIS-equivalent NDVI and SWIR32 was examined for regions of interest (ROI) collected within protected areas and nearby converted lands. The MODIS NDVI, SWIR32 and retrieved FPV, FNPV, and FBS are then compared to detailed cover and structural composition data from field sites, and the influence of the structural and compositional variation on the VIs and cover fractions is explored. The hyperion ROI analysis indicated that the two-way NDVI–SWIR32 response behaved as an effective surrogate for the two-way NDVI–CAI response for the campo limpo/grazed pasture to cerrado sensu stricto woody gradient. The SWIR32 sensitivity to the NPV and BS variation increased as the dry season progressed, but Cerrado savannah exhibited limited dynamic range in the NDVI–CAI and NDVI–SWIR32 two-way responses compared to the entire landscape, which also comprises fallow croplands and forests. Validation analysis of MODIS retrievals with Quickbird-2 images produced an RMSE value of 0.13 for FPV. However, the RMSE values of 0.16 and 0.18 for FBS and FNPV, respectively, were large relative to the seasonal and inter-annual variation. Analysis of site composition and structural data in relation to the MODIS-derived NDVI, SWIR32 and FPV, FNPV, and FBS, indicated that the VI signal and derived cover fractions were influenced by a complex mix of structure and cover but included a strong year-to-year seasonal effect. Therefore, although the MODIS NDVI–SWIR32 response could be used to retrieve cover fractions across all Cerrado land covers including bare cropland, pastures and forests, sensitivity may be limited within the natural Cerrado due to sub-pixel heterogeneity and limited BS and NPV sensitivity.

Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data

Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (fpv) and NPV (fnpv) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of fpv and fnpv in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For fpv, all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R2 = 0.49, RMSE = 0.17), followed by MESMA (R2 = 0.48, RMSE = 0.21), and SMA (R2 = 0.47, RMSE = 0.27). For fnpv, MESMA had the lowest performance (R2 = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates fnpv (R2 = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of fnpv (R2 = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land.

Development of new remote sensing methods for mapping green vegetation and exposed bedrock fractions within heterogeneous landscapes

Karst rocky desertification is a process of land desertification associated with human disturbance of the fragile karst ecosystems. The fractional cover of photosynthetic vegetation (PV) and exposed bedrock (Rock) are the main land-surface symptoms of karst rocky desertification. In this study, we explored a new methodology for quantifying PV and Rock by remote sensing. To reduce the effects of the high heterogeneity of karst landscapes on vegetation information extraction, a whole image was segmented into relatively homogeneous subsets and then the PV was estimated using a normalized difference vegetation index spectral mixture analysis (NDVI-SMA) model. The percentage of Rock was estimated using a karst rocky desertification synthesis index (KRDSI) and lignin cellulose absorption index (LCA). The results showed that the heterogeneity of a complex landscape is a major factor in the uncertainty of PV retrievals. The fractional cover of PV can be accurately estimated by the proposed method, but might be underestimated using NDVI and overestimated using the SMA-NDVI model. The bedrock fractions can be rapidly and objectively estimated with Hyperion or simulated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Compared with multispectral images, hyperspectral images could be used to estimate PV and Rock more accurately. Our findings indicate that PV and Rock can be directly and efficiently quantified using remote sensing techniques within heterogeneous landscapes.

Spectral indices for estimating ecological indicators of karst rocky desertification

Karst rocky desertification is a process of land desertification associated with human disturbance of the fragile eco-geological setting of karst ecosystems. The fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and exposed bedrock are key ecological indicators of the extent and degree of land degradation in karst regions. In this study, field spectral-reflectance measurements were used to develop a karst rocky desertification synthesis index (KRDSI) based on unique spectral features observed in non-vegetation land-cover types (NPV, bare soil and exposed bedrock) and were used to estimate the fractional cover of NPV, bare soil and exposed bedrock. Compared with linear spectral unmixing (LSU) using a tied-spectrum transform, the KRDSI is more consistent with the field measurement of non-vegetation land-cover fractions. This study indicates that ecological indicators of karst rocky desertification can be extracted relatively simply with the combination of vegetation indices and KRDSI values.

Regional aboveground live carbon losses due to drought-induced tree dieback in piñon–juniper ecosystems

Recent large-scale dieback of piñon–juniper (P–J) woodlands and forests across the western US occurred as a result of multi-year drought and subsequent insect and disease outbreaks. P–J vegetation is spatially extensive, thus large-scale mortality events such as the one that has occurred over the past several years could significantly alter regional carbon (C) budgets. Our objective was to use a remote sensing technique coupled with field-based data to estimate changes in aboveground live C stocks across a 4100 km 2 region of Colorado caused by P–J tree mortality. We hypothesized that dieback would amplify the phenological dynamics of P–J vegetation, and these variations would be related to drought-induced losses of live P–J aboveground biomass (AGB) that are discernible using time-series remote sensing vegetation data. Here, we assess live P–J AGB loss using dry season fractional photosynthetic vegetation cover (PV) derived from multi-year Landsat images. Our results showed a strong linear positive relationship between the maximum decline in PV and field-measured losses of live P–J AGB during the period 2000–05 ( r 2 = 0.64, p = 0.002). These results were then used to map AGB losses throughout the study region. Mean live aboveground C loss (± sd) was 10.0 (± 3.4) Mg C ha − 1 . Total aboveground live P–J C loss was 4.6 Tg C, which was approximately 39 times higher than the concurrent C loss attributed to wildfire and management treatments within or near to the national forests of the study region. Our results suggest that spatially extensive mortality events such as the one observed in P–J woodlands across the western US in the past decade may significantly alter the ecosystem C balance for decades to come. Remote sensing techniques to monitor changes in aboveground C stocks, such as the one developed in our study, may support regional and global C monitoring in the future.

Objective indicators of pasture degradation from spectral mixture analysis of Landsat imagery

Degradation of cattle pastures is a management concern that influences future land use in Amazonia. However, “degradation” is poorly defined and has different meanings for ranchers, ecologists, and policy makers. Here we analyze pasture degradation using objective scalars of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed soil (S) derived from Landsat imagery. A general, probabilistic spectral mixture model decomposed satellite spectral reflectance measurements into subpixel estimates of PV, NPV, and S covers at ranches in western and eastern Amazonia. Most pasture management units at all ranches fell along a single line of decreasing PV with increasing NPV and S, which could be considered a degradation continuum. The ranch with the highest stocking densities and most intensive management had greater NPV and S than a less intensively managed ranch. The number of liming, herbiciding, and disking treatments applied to each pasture management unit was positively correlated with NPV and negatively correlated with PV. Although these objective scalars revealed signs of degradation, intensive management kept exposed soil to &lt;40% cover and maintained economically viable cattle production over several decades. In ranches with few management inputs, the high PV cover in young pastures declined with increasing pasture age, while NPV and S increased, even where grazing intensity was low. Both highly productive pastures and vigorous regrowth of native vegetation cause high PV values. Analysis of spectral properties holds promise for identifying areas where grazing intensity has exceeded management inputs, thus increasing coverage of senescent foliage and exposed soil.

MODELING THE EFFECT OF PHOTOSYNTHETIC VEGETATION PROPERTIES ON THE NDVI–LAI RELATIONSHIP

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.

Carbon uptake and change in net primary productivity of oasis-desert ecosystem in arid western China with remote sensing technique

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and water are unmixed, using the remote sensing spectral mixture analysis. We try the method to unmix the canopy funation structure of arid land cover in order to avoid the differentiation of regional vegetation system and the disturbance of environmental background. We developed a modified production efficiency model NPP-PEM appropriate for the arid area at regional scale based on the concept of radiation use efficiency. This model refer to the GLO-PEM and CASA model was driven with remotely sensed observations, and calculates not just the conversion efficiency of absorbed photosynthetically active radiation but also the carbon fluxes that determine net primary productivity (NPP). We apply and validate the model in the Kaxger and Yarkant river basins in arid western China. The NPP of the study area in 1992 and 1998 was estimated based on the NPP-PEM model. The results show that the improved PEM model, considering the photosynthetical activation of heterogeneous functional vegetation, is in good agreement with field measurements and the existing literature. An accurate agreement (R2 = 0.85, P<0.001) between the estimates and the ground-based measurement was obtained. The spatial distribution of mountain-oasis-desert ecosystem shows an obvious heterogeneous carbon uptake. The results are applicable to arid ecosystem studies ranging from characterizing carbon cycle, carbon flux over arid areas to monitoring change in mountain-oasis-desert productivity, stress and management.

Desertification alters regional ecosystem–climate interactions

AbstractWe studied the spatial patterns and temporal dynamics of vegetation structural responses to precipitation variation in grassland, transitional, and desertified‐shrubland ecosystems in an 800 km2 region of Northern Chihuahua, USA. Airborne high‐fidelity imaging spectroscopy data collected from 1997 to 2001 provided spatially detailed measurements of photosynthetic and senescent canopy cover and bare soil extent. The observations were made following wintertime and summer monsoonal rains, which varied in magnitude by &gt;300% over the study period, allowing an assessment of ecosystem responses to climate variation in the context of desertification.Desertification caused a persistent increase in both photosynthetic vegetation (PV) and bare soil cover, and a lasting decrease in nonphotosynthetic vegetation (NPV). We did not observe a change in the spatial variability of PV cover, but its temporal variation decreased substantially. In contrast, desertification caused the spatial variability of NPV to increase markedly, while its temporal variation did not change. Both the spatial and temporal variation of exposed bare surfaces decreased with desertification. Desertification appeared to be linked to a shift in seasonal precipitation use by vegetation from mainly summer to winter inputs, resulting in an apparent decoupling of vegetation responses to inter‐annual monsoonal variation. Higher winter rainfall led to decreased springtime spatial variability in the PV cover of desertified areas. Higher summer rainfall resulted in decreased PV cover variation in grassland, transition and desertified‐shrubland regions. The effects of desertification on NPV dynamics were more than three times greater than on PV or bare soil dynamics. Using remotely sensed PV and NPV as proxies for net primary production (NPP) and litter dynamics, respectively, we estimated that desertification decreases the temporal variability of NPP and increases spatial variation of litter production and loss. Quantitative studies of surface biological materials and ecosystem processes can now be measured with high ‘structural’ detail using imaging spectroscopy and shortwave‐infrared spectral mixture analysis.