Nonphotosynthetic Vegetation Cover Research Articles

Modeling global indices for estimating non-photosynthetic vegetation cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows

Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can help improve the accuracy of soil erosion evaluations. Three deep learning semantic segmentation models, DeepLabV3+, PSPNet, and U-Net, are often used to extract features from unmanned aerial vehicle (UAV) images; however, their extraction processes are highly dependent on the assignment of massive data labels, which greatly limits their applicability. At the same time, numerous shadows are present in UAV images. It is not clear whether the shaded features can be further classified, nor how much accuracy can be achieved. This study took the Mu Us Desert in northern China as an example with which to explore the feasibility and efficiency of shadow-sensitive PV/NPV classification using the three models. Using the object-oriented classification technique alongside manual correction, 728 labels were produced for deep learning PV/NVP semantic segmentation. ResNet 50 was selected as the backbone network with which to train the sample data. Three models were used in the study; the overall accuracy (OA), the kappa coefficient, and the orthogonal statistic were applied to evaluate their accuracy and efficiency. The results showed that, for six characteristics, the three models achieved OAs of 88.3–91.9% and kappa coefficients of 0.81–0.87. The DeepLabV3+ model was superior, and its accuracy for PV and bare soil (BS) under light conditions exceeded 95%; for the three categories of PV/NPV/BS, it achieved an OA of 94.3% and a kappa coefficient of 0.90, performing slightly better (by ~2.6% (OA) and ~0.05 (kappa coefficient)) than the other two models. The DeepLabV3+ model and corresponding labels were tested in other sites for the same types of features: it achieved OAs of 93.9–95.9% and kappa coefficients of 0.88–0.92. Compared with traditional machine learning methods, such as random forest, the proposed method not only offers a marked improvement in classification accuracy but also realizes the semiautomatic extraction of PV/NPV areas. The results will be useful for land-use planning and land resource management in the areas.

Comparison of the backpropagation network and the random forest algorithm based on sampling distribution effects consideration for estimating nonphotosynthetic vegetation cover

Non–photosynthetic vegetation (NPV) plays a crucial role in arid and semi-arid ecosystems. Remote sensing methods can extract NPV information accurately and quantitatively, which helps in studying the water use, community health, and climate response of vegetation communities. This study used the backpropagation network (BP) and random forest (RF) methods to test NPV cover extraction from Landsat 8-OLI images in Mu Us Sandy Land. Pixel-level NPV cover, photosynthetic vegetation (PV) cover, and bare soil (BS) cover from unmanned aerial vehicle (UAV) field sampling data were used to model the BP and RF. After the generalisation ability of the NPV detection model of BP and RF was evaluated using ten-fold cross-validation, the influence of the distribution of sampling data on BP and RF fitting results was also evaluated. The results were as follows: 1. Considering the selection of appropriate parameters and input layers, both BP and RF exhibited high accuracy in detecting NPV, and the detection accuracy of the RF algorithm for PV and BS was slightly higher than that of the BP algorithm (R2RF-NPV = 0.8426, R2BP-NPV = 0.8277, R2RF-PV = 0.8606, R2BP-PV = 0.8514, R2RF-BS = 0.8123, R2BP-BS = 0.7396). 2. When the BP and RF algorithms were used for geospatial continuous value prediction, the distribution of samples affected the final prediction results. The RF algorithm is less sensitive to the sample data distribution. 3. The random sampling method is the best method for collecting training samples. Even with uniform sampling, when there was a large difference between the distribution of the sampling value and the distribution of the real value, the fitting result would have a large deviation. This paper provides suggestions for the fitting of nonphotosynthetic vegetation in arid and semi-arid regions and provides a new method for evaluating the results of remote sensing regression fitting.

Estimating fractional cover of non-photosynthetic vegetation for various grasslands based on CAI and DFI

Non-photosynthetic vegetation (NPV) is a vital component of terrestrial ecosystems and an important indicator of grassland degradation, therefore, it is of great significance to realize its accurate evaluation. Here, we analyzed spectral characteristics of NPV of different biomass types in undisturbed vegetation (herb, subshrub and shrub), and established relationship models among dead fuel index (DFI), cellulose absorption index (CAI) and fractional cover of NPV (fNPV) based on ground hyperspectral data; then, fNPV of four grassland types were evaluated based on the models. Our results showed that: (1) NPV reflectance exhibited similar change trends for herb, subshrub, shrub, and a mixed type, although there were significant differences among values, (2) DFI and CAI, CAI and fNPV, DFI and fNPV were significantly positively correlated (p < 0.001), (3) the maximum fNPV estimation accuracy of CAI was higher than that of DFI, and the values were 85 and 75%, respectively, (4) fNPV differed significantly among the four grasslands, with highest in meadow grassland (77%) and lowest in desert grassland (43%). We conclude that fNPV has obvious heterogeneity among different vegetation types, and both CAI and DFI can be used to reflect fNPV although there is difference in evaluation performance.

Remote sensing of trophic cascades: multi‐temporal landsat imagery reveals vegetation change driven by the removal of an apex predator

Trophic cascade theory predicts that predators indirectly benefit plants by limiting herbivore consumption. As humans have removed large predators from most terrestrial ecosystems the effect of their absence is unrecognized. A manipulation of dingo populations across Australia’s dingo-proof fence, within the Strzelecki Desert, was used to assess how predator absence has altered vegetation cover dynamics at landscape and site scales. Landscape-scale analysis used Landsat fractional vegetation cover time series statistics to classify landforms and examine vegetation dynamics either side of the dingo fence. Generalised additive models were used to analyse the influence of predator absence on site-scale observations of fauna abundance and vegetation cover. The location of the dingo fence was visible as a change in both the standard deviation and maximum of non-photosynthetic vegetation (NPV) cover (e.g. wood and dry leaves) over 32 years (1988–2020). On average, NPV cover of swales decreased in the standard deviation by 1.4% and in the maximum by 5.0% where dingo abundance was reduced. The differences were consistent with suppressed vegetation growth following rainfall, due to high grazing pressure, where predators were rare. The landscape-scale analysis was supported by site-scale observations. The influence of the trophic cascade was observable at both the landscape and site scales, suggesting that apex predator removal has significantly affected the arid ecosystem’s responses to resource pulses. Analogous effects may exist across the large areas of the planet over which apex predators have been extirpated.

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands.

Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study proposed to develop a method to quantify both GV and standing dead matter (SDM) fraction cover from field-taken digital RGB images with semi-automated batch processing capabilities (i.e., written as a python script) for mixed grasslands with more complex background information including litter, moss, lichen, rocks and soil. The results show that the GV cover extracted by the method developed in this study is superior to that by subjective visual estimation based on the linear relation with normalized vegetation index (NDVI) calculated from field measured hyper-spectra (R2 = 0.846, p < 0.001 for GV cover estimated from RGB images; R2 = 0.711, p < 0.001 for subjective visual estimated GV cover). The results also show that the developed method has great potential to estimate SDM cover with limited effects of light colored understory components including litter, soil crust and bare soil. In addition, the results of this study indicate that subjective visual estimation tends to estimate higher cover for both GV and SDM compared to that estimated from RGB images.

Mapping the fractional cover of non-photosynthetic vegetation and its spatiotemporal variations in the Xilingol grassland using MODIS imagery (2000−2019)

Obtaining accurate information on the fractional cover of non-photosynthetic vegetation (NPV) (f NPV) on grasslands is essential for monitoring soil erosion risk, assessing grassland productivity and managing grassland ecosystems. However, few studies have monitored f NPV in the Xilingol grassland region (XGR) and its spatiotemporal variations based on Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this study, we determined the upper dead fuel index (DFI) threshold for NPV in the XGR. Then, a remote-sensing inversion model for f NPV was established based on the ground-measured f NPV and the DFI derived from MODIS images. Based on the inversion model, we obtained the spatial distribution and spatiotemporal variations in f NPV from 2000 to 2019. The results indicated that the DFI can reflect the NPV in the XGR with an upper threshold of 27.2. The DFI-f NPV linear regression model showed a good performance, with a coefficient of determination (R2) of 0.60 and a root mean square error of leave-one-out cross-validation (RMSECV) of 0.1574. Furthermore, the spatial distribution of f NPV exhibited significant heterogeneity, and f NPV decreased from the northeastern XGR to the southwestern XGR. The overall trend of the interannual f NPV in the XGR increased in a fluctuating manner during 2000–2019. The f NPV increased in 66.92% of the XGR and decreased in a relatively small proportion (18.21%) of the XGR.

The response of vegetation cover and dune activity to rainfall, drought and fire observed by multitemporal satellite imagery

AbstractThe stable longitudinal dunes in the northern Simpson Desert, Australia, were observed in satellite imagery to become more active after vegetation cover was reduced by fire and drought. Subsequent rainfall events also resulted in significant vegetation regrowth and dune stabilization. These switches between more active and stable conditions have not been previously described in the largely vegetated dune fields of central Australia. The observations, made on 12 dune sites, relied on high spatial resolution satellite imagery to observe dune crest activity, and seasonal Landsat fractional cover imagery to observe vegetation cover changes. The non‐photosynthetic vegetation (NPV) component of the fractional vegetation cover images revealed significant changes in hummock grass cover on the dunes between 1988 and 2018, with a positive relationship with the three‐year cumulative rainfall, disrupted by two periods of patchy burning. Only those sites that had burnt became active, and only after vegetation cover had remained low (NPV &lt; 16%) during the ‘Millennium Drought’. There is no threshold in vegetation cover, below which dune crests become active, but active dune features require four‐years of low NPV cover (&lt; 16%) to develop. The large rainfall event that ended the drought increased NPV cover, stabilizing the dunes. Similar hummock grass covered dunes are present across large areas of the arid zone, and are likely to respond in similar ways, given that fire and drought are common occurrences in Australia. © 2019 John Wiley &amp; Sons, Ltd. © 2019 John Wiley &amp; Sons, Ltd.

Estimation of fractional cover of non-photosynthetic vegetation in typical steppe based on MODIS data

Estimation of fractional cover of non-photosynthetic vegetation in typical steppe based on MODIS data

Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

ABSTRACT Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f NPV) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f NPV in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f NPV data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f NPV. Based on the determined NPVIs, we established a remote sensing estimation model for f NPV in typical grassland areas using MODIS image data. The spatial distribution of f NPV in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR32), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R2 ) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f NPV using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f NPV in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f NPV gradually decreased from the north-eastern to south-western portions of the study area.

Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery

Vegetation cover is a key environmental variable often mapped from satellite and aerial imagery. The derivation of ultra-high spatial resolution fractional vegetation cover (FVC) based on multispectral imagery acquired from an Unmanned Aerial System (UAS) has several applications, including the potential to revolutionise the collection of field data for calibration/validation of satellite products. In this study, abundance maps were derived using three methods, applied to data collected in a typical Australian rangeland environment. The first method used downscaling between Landsat FVC maps and UAS images with Random Forest regression to predict bare ground, photosynthetic vegetation and non-photosynthetic vegetation cover. The second method used spectral unmixing based on endmembers identified in the multispectral imagery. The third method used an object-based classification approach to label image segments. The accuracy of all UAS FVC and Landsat FVC products were assessed using 20 field plots (100 m diameter star transects), as well as from 138 ground photo plots. The classification method performed best for all cover fractions at the 100 m plot scale (12–13% RMSE), with the downscaling approach only able to accurately predict photosynthetic cover. The downscaling and unmixing generally over-predicted non-photosynthetic vegetation associated with Chenopod shrubs. When compared with the high-resolution photo plot data, the classification method performed the worst, while the downscaling and unmixing methods achieved reasonable accuracy for the photosynthetic component only (12–13% RMSE). Multispectral UAS imagery has great potential for mapping photosynthetic vegetation cover in rangelands at ultra-high resolution, though accurately separating non-photosynthetic vegetation and bare ground was only possible when the data was scaled-up to coarser resolutions.

A Review on Estimating Fractional Cover of Non-photosynthetic Vegetation by Using Remote Sensing

A Review on Estimating Fractional Cover of Non-photosynthetic Vegetation by Using Remote Sensing

Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

AbstractThe drivers of dust emission interannual variability in North Africa, the largest dust source on Earth, are still debated. Early studies outlined the role of previous‐season rainfall and vegetation growth, while some recent studies emphasize the role of wind variability. Here we use a newly developed estimation of dry‐season nonphotosynthetic vegetation cover in the Sahel based on Moderate Resolution Imaging Spectroradiometer (MODIS) short‐wave infrared bands over the 2002–2015 period. The vegetation growth anomalies caused by variability of rainfall in June–September translate to anomalies of dry vegetation cover that persist throughout the dry season until May. These vegetation anomalies explain 43% (50%) of the year‐to‐year variance in Sahelian‐mean dry‐season aerosol optical depth (AOD) as derived from MODIS Deep Blue (Sun photometers). Similar explained variance is found with 10 m wind speed and dust uplift potential. The central Sahel proves more important than the western Sahel for dry‐season AOD variability.

Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

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.

The contribution of brown vegetation to vegetation dynamics

Indices of vegetation dynamics that include both green vegetation (GV) and non-photosynthetic vegetation (NPV), that is, brown vegetation, were applied to MODIS surface reflectance data from 2000 to 2006 for the southwestern United States. These indices reveal that the cover of NPV, a measure of vegetation brownness and a component of ecosystems worldwide, is highly variable in both space and time in the study region. In the more mesic regions of the study area, the timing of peaks in NPV appears to result from simple senescence of GV at the end of the growing season. In these regions, the amplitude of GV cyclicity dominates the total vegetation signal. In contrast, in arid and semiarid regions, the amplitude of cyclicity of NPV dominates the total vegetation signal, showing the vegetation of these regions to be unexpectedly dynamic. Shrublands of southwestern United States exhibit temporal behavior in which the annual peak in NPV cover precedes the annual peak in GV cover by a few months. Several explanations for this behavior are offered. This study shows the importance of vegetation indices that include NPV, or vegetation brownness, in understanding terrestrial ecosystem dynamics, as well as the response to change for these ecosystems.

Satellite Monitoring of Vegetation Phenology and Fire Fuel Conditions in Hawaiian Drylands

Abstract Grass-fueled fires accelerate grassland expansion into dry Hawaiian woodlands by destroying native forests and by producing a disturbance regime that favors grass-dominated plant communities. Knowledge of grassland phenology is a key component of ecosystem assessments and fire management in Hawaii, but diverse topographic relief and poor field-sampling capabilities make ground studies impractical. Remote sensing offers the best approach for large-scale, spatially contiguous measurements of dryland vegetation phenology and fire fuel conditions. A 500-m spatial resolution, 8-day temporal resolution Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and exposed substrate conditions was developed for the island of Hawaii between 2000 and 2004. The results compared favorably with similar measurements of drylands from higher-resolution aircraft data. The satellite time series was compared with available environmental data on precipitation, fire history, and grazing intensity. From these analyses, the temporal patterns of PV and its conversion to NPV and finally to bare substrate were observed. An NPV buildup following fire of 7–8 yr was projected, and more heavily grazed lands were found to exhibit reduced NPV cover, most notably during the summer fire season. These results demonstrate the effects that land use and disturbance history have on fire conditions, and they support the concept that grazed lands managed to reduce litter buildup pose a lower risk of fire across ample geographic scales. Time series of satellite observations with modern analysis techniques can be used with environmental data to support a regional fire-monitoring program throughout Hawaii.

DESERTIFICATION IN CENTRAL ARGENTINA: CHANGES IN ECOSYSTEM CARBON AND NITROGEN FROM IMAGING SPECTROSCOPY

Many arid and semiarid (dryland) regions are subject to desertification from intensive land-use pressures such as cattle ranching. However, the lack of quantitative approaches required to assess desertification has slowed our understanding of how vegetation and soils are changing in dryland regions. Using airborne high-fidelity imaging spectroscopy and field measurements, we developed the first regional assessment of vegetation structural and soil biogeochemical properties in the Monte Desert biome, Argentina. We evaluated the long-term impacts of grazing on vegetation cover and soil carbon (C) and nitrogen (N) storage, which are core indicators of biogeochemical status and change in drylands. A comparative analysis was carried out on vegetation and soil properties in four major Monte Desert plant communities, as well as within the U.N. Nacunan Man-and-Biosphere Reserve and adjacent areas subjected to long-term grazing. The four dominant plant communities differed substantially in vegetation cover, leaf area index, foliar N concentration, and soil organic C and N stocks. Imaging spectroscopy with Monte Carlo spectral mixture analysis provided accurate estimates of fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil cover at <5-m spatial resolution throughout a 763-km2 region. Hotspots of grazing management (e.g., ranch centers, water sources) have undergone some woody vegetation encroachment, but the spatial patterns were localized and variable. More clearly, a widespread NPV decrease and bare soil increase was common outside of the reserve. Soil organic C and N stocks were highly correlated with PV + NPV cover fractions. Soil organic C and N storage was 25–80% lower in areas subjected to long-term grazing, as compared to protected ecosystems within the Nacunan Reserve. Long-term grazing has depleted stores of C and N in soils and dramatically altered vegetation structure in the Monte Desert. A persistent lack of NPV due to grazing has likely impaired C and N cycles considered central to the biogeochemical functioning of the region. This study demonstrates a novel approach to remotely measure both surface and soil properties most indicative of progressive land degradation and desertification in dryland regions. Corresponding Editor: D. Peters