A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index

Understanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and wider coverage for the assessment of plant diversity, especially in remote areas. Theoretical basis and large-scale solutions for assessing beta-diversity have been recently presented. Yet landscape-scale and context-based analysis are missing. We assessed temporal β-diversity using Raós Q diversity derived from Landsat-based vegetation indices by considering the effect of ERA-5 monthly aggregates environmental factors (temperature and precipitation) extracted using Google Earth Engine (GEE), land use classes, and two common vegetation indices. We derived 15-year Rao’s Q diversity using Landsat-7 based normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI). We evaluated the temporal turnover in Rao’s Q on multiple land use classes, including agriculture, intact forest and areas affected by and invasive species. Vegetation index and Rao’s Q diverged between pre- and post- monsoon seasons. Rao’s Q had higher temporal turnover with NDVI than MSAVI for all vegetation classes, however the latter showed higher sensitivity towards temperature and precipitation. Moreover, agriculture generally showed higher variability than forest and invasive species. The temporal turnover was correlated between NDVI and MSAVI for all vegetation classes, which indicated that the variability among vegetation types was directly related to spectral heterogeneity. Furthermore, MSAVI was less sensitive to the effect of soil in assessing the vegetation indices, which resulted in higher global sensitivity of QMSAVI. Near infrared and red spectra used in vegetation indices are able to capture a small variation in leaf traits reflectance for vegetation types. Here, the β-diversities and their temporal dynamics derived from the vegetation indices differed based on their sensitivity to soil, vegetation density and seasonality. This approach and its open source implementation can be tested for different forest ecosystems at varying spatial scales

Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index

Space-borne remote sensing missions provide robust, timely and continuous data to assess biodiversity in remote or protected areas, where direct field observations can be prohibited by difficult accessibility. The objective of this study was to extend the concept of remote sensing based assessment of beta-diversity to multi-scale domain by multi-resolution optical satellite data. This study was conducted in a reserved forest of western Himalaya, India; a region affected by the invasive Lantana camara L (lantana). We calculated and compared Rao’s Q and Shannon indices at different spatial resolutions (0.5, 5, and 30 m) and scales (window sizes) by using imageries from Pléiades 1A, RapidEye, and Landsat-8 acquired in April 2013, the pre-monsoon season. Rao’s Q index explained diversity more accurately than Shannon index for the three analyzed stand densities. Diversity was better approximated by Rao’s Q index calculated by Pléiades 1A at a resolution of 0.5 m at low stand density. We observed higher correlations of the average coefficient of variation (CV) with Rao’s Q and Shannon indices for areas associated with mixed spectral reflectance caused by overstory and understory vegetation. Furthermore, CV was lower in open areas dominated by lantana. These results indicated a strong scale and spatial resolution dependence of Rao’s Q index on remote sensing-derived spectral heterogeneity information. When applied in heterogeneous forest environments, Rao’s Q index could represent a better remote sensing proxy to estimate beta-diversity than the conventional Shannon index

Assessment of spatio-temporal patterns of black spruce bud phenology across Quebec based on MODIS-NDVI time series and field observations

Satellite remote sensing is a widely accessible tool to investigate the spatiotemporal variations in the bud phenology of evergreen species, which show limited seasonal changes in canopy greenness. However, there is a need for precise and compatible data to compare remote sensing time series with field observations. In this study, fortnightly MODIS-NDVI was fitted using double-logistic functions and calibrated using ordinal logit models with the sequential phases of bud phenology collected during 2015, 2017 and 2018 in a black spruce stand. Bud break and bud set were spatialized for the period 2009–2018 across 5000 stands in Quebec, Canada. The first phase of bud break and the last phase of bud set were observed in the field in mid-May and at the beginning of September, when NDVI was 80.5% and 92.2% of its maximum amplitude, respectively. The NDVI rate of change was estimated at 0.07 in spring and 0.04 in autumn. When spatialized on the black spruce stands, bud break was detected earlier in the southwestern regions (April–May), and later in the northeastern regions (mid to end of June). No clear trend was observed for bud set, with different patterns being detected among the years. Overall, the process bud break and bud set lasted 51 and 87 days, respectively. Our results demonstrate the potential of satellite remote sensing for providing reliable timings of bud phenological events using calibrated NDVI time series on wide regions that are remote or with limited access

Comparing time-Lapse PhenoCams with satellite observations across the Boreal forest of Quebec, Canada

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather

Disentangling the effects of genotype and environment on growth and wood features of Balfourodendron riedelianum trees by common garden experiments in Brazil

Intraspecific studies with populations replicated in different sites allow the effects of genotype and environment on wood features and plant growth to be distinguished. Based on climate change predictions, this distinction is important for establishing future patterns in the distribution of tree species. We quantified the effects of genotype and environment on wood features and growth of 30-year-old Balfourodendron riedelianum trees. We used three provenances planted in two common garden experiments with difference in precipitation and temperature. We applied linear models to estimate the variability in wood and growth features and transfer functions to evaluate the responses of these features to temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI). Our results showed that genotype had an effect on vessels and rays, where narrower vessels with thinner walls and larger intervessel pits, and shorter, narrower and more numerous rays were observed in provenances from drier sites. We also observed the effect of the environment on wood features and growth. Trees growing in the wetter site were taller and larger, and they had wider vessels with thicker walls and lower ray density. Transfer functions indicated that an increase in temperature results in larger vessels with thicker walls, taller and denser rays, shorter and narrower fibers with thinner walls, and lower wood density. From a functional perspective, these features make trees growing in warmer environments more prone to drought-induced embolisms and more vulnerable to mechanical damage and pathogen attacks. Tree growth varied with precipitation and SPEI, being negatively affected in the drier site. Overall, we demonstrated that both genotype and environment affect wood features, while tree growth is mainly influenced by the environment. Plastic responses in hydraulic characteristics could represent important functional traits to mitigate the consequences of ongoing climate change on the growth and survival of the species within its natural range

Calibrating phenoCam data with phenological observations of a black spruce stand

Bud and leaf development are important phenological events and help in defining the growing period of trees. Canopy greenness derived from PhenoCam has been used to investigate leaf phenology. Questions remain on how much the continuous records of canopy greenness represent bud developmental phases, and how growing period boundaries are related to canopy greenness and bud phenology. In this study, we compared bud phenology of black spruce [Picea mariana (Mill.) B.S.P] during 2015, 2017 and 2018 with the canopy greenness, represented by Green Chromatic Coordinate (GCC), derived from PhenoCam images of a boreal stand in Quebec, Canada. Logit models were applied to estimate the probability of observing sequential phenological phases of bud burst and bud set along with GCC. GCC showed a bell-shaped pattern, with a slow increase in spring, a peak in summer and a gradual decrease in autumn. The start and end of budburst, and bud set, occurred when GCC reached 72% and 92% (spring), and 94% (autumn) of its maximum amplitude, respectively. These GCC values are reliable thresholds indicating the growing period boundaries. Our study builds a bridge between phenological observations and automatic near-surface remote sensing, providing a statistically sound protocol for calibrating PhenoCam with field observations

Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data

Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility

From Cauchy's determinant formula to bosonic and fermionic immanant identities

Cauchy's determinant formula (1841) involving $\det ((1-u_i v_j)^{-1})$ is a fundamental result in symmetric function theory. It has been extended in several directions, including a determinantal extension by Frobenius [J. reine angew. Math. 1882] involving a sum of two geometric series in $u_i v_j$. This theme also resurfaced in a matrix analysis setting, in computations by Loewner in [Trans. Amer. Math. Soc. 1969]; and by Belton-Guillot-Khare-Putinar [Adv. Math. 2016] and Khare-Tao [Amer. J. Math. 2021]. These formulas were recently unified and extended in [Trans. Amer. Math. Soc., in press] to arbitrary power series, with commuting/bosonic variables $u_i, v_j$. In this note we formulate analogous permanent identities, and in fact, explain how all of these results are a special case of a more general identity, for any character of any finite group that acts on the bosonic variables $u_i$ and on the $v_j$ via permutations. We then provide fermionic analogues of these formulas, as well as of the closely related Cauchy product identities.Comment: 11 pages, 1 table, no figure

Multi-scale assessment of invasive plant species diversity using Pléiades 1A,RapidEye and Landsat-8 data

"We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI - assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes.

Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries

Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R$^2$ values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions