Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

Plant ecology and biodiversity research have increasingly incorporated trait-based approaches and remote sensing. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on airborne LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the ITC- and pixel-based approaches at changing pixel size and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by airborne Pushbroom Hyperspectral Imager-3 (PHI-3) hyperspectral data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC-based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness also decreased with increasing pixel size, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, high-resolution pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage

Water level affects availability of optimal feeding habitats for threatened migratory waterbirds

Extensive ephemeral wetlands at Poyang Lake, created by dramatic seasonal changes in water level, constitute the main wintering site for migratory Anatidae in China. Reductions in wetland area during the last 15 years have led to proposals to build a Poyang Dam to retain high winter water levels within the lake. Changing the natural hydrological system will affect waterbirds dependent on water level changes for food availability and accessibility. We tracked two goose species with different feeding behaviors (greater white‐fronted geese Anser albifrons [grazing species] and swan geese Anser cygnoides [tuber‐feeding species]) during two winters with contrasting water levels (continuous recession in 2015; sustained high water in 2016, similar to those predicted post‐Poyang Dam), investigating the effects of water level change on their habitat selection based on vegetation and elevation. In 2015, white‐fronted geese extensively exploited sequentially created mudflats, feeding on short nutritious graminoid swards, while swan geese excavated substrates along the water edge for tubers. This critical dynamic ecotone successively exposes subaquatic food and supports early‐stage graminoid growth during water level recession. During sustained high water levels in 2016, both species selected mudflats, but also to a greater degree of habitats with longer established seasonal graminoid swards because access to tubers and new graminoid growth was restricted under high‐water conditions. Longer established graminoid swards offer less energetically profitable forage for both species. Substantial reduction in suitable habitat and confinement to less profitable forage by higher water levels is likely to reduce the ability of geese to accumulate sufficient fat stores for migration, with potential carryover effects on subsequent survival and reproduction. Our results suggest that high water levels in Poyang Lake should be retained during summer, but permitted to gradually recede, exposing new areas throughout winter to provide access for waterbirds from all feeding guilds

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing

Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015

For centuries, the rapid development of human society has already made human activity the dominant factor in the terrestrial ecosystem. As the city of greatest importance in China, the capital Beijing has experienced eco-environmental changes with unprecedented economic and population growth during the past few decades. To better understand the ecological transition and its correlations in Beijing, Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images were used to investigate vegetation coverage changes using a dimidiate pixel model. Piecewise linear regression, bivariate-partial correlation analysis, and factor analysis were applied to the probing of the relationship between vegetation coverage changes and climatic/human-induced factors. The results showed that from 2000 to 2005, 2005 to 2010, and 2010 to 2015, Beijing experienced both restoration (6.33%, 10.08%, and 12.81%, respectively) and degradation (13.62%, 9.35%, and 9.49%, respectively). The correlation analysis results between climate and vegetation changes demonstrated that from 2000 to 2015, both the multi-year annual mean temperature (r = −0.819, p < 0.01) and the multi-year annual mean precipitation (r = 0.653, p < 0.05) had a significantly correlated relationship with vegetation change. The Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) has shown beneficial spatial effects on vegetation restoration; the total effectiveness in conservation areas (84.94 in 2000–2010) was much better than non-BTSSCP areas (34.34 in 2000–2010). The most contributory socioeconomic factors were the population (contribution = 54.356%) and gross domestic product (GDP) (contribution = 30.677%). The population showed a significantly negative correlation with the overall vegetation coverage (r = −0.684, p < 0.05). The GDP was significantly negatively correlated with vegetation in Tongzhou, Daxing, Central city, Fangshan, Shunyi, and Changping (r = −0.601, p < 0.01), while positively related in Huairou, Miyun, Pinggu, Mentougou and Yanqing (r = 0.614, p < 0.01). These findings confirm that human activity is a very significant factor in impacting and explaining vegetation changes, and that some socioeconomic influences on vegetation coverage are highly spatially heterogeneous, based on the context of different areas

UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland

Shrub encroachment is an important ecological issue that is increasingly receiving global attention in arid and semiarid grasslands. Monitoring the spatial distribution of encroached shrub aboveground biomass (AGB) is critical for ecological conservation and adaptive ecosystem management. However, the low stature and fine spatial heterogeneity of encroached shrub communities increase difficulties for coarse spatial-resolution satellite images to adequately capture detailed characteristics of individual shrubs. Unmanned aerial vehicle (UAV) can acquire centimeter-level optical images or high-density LiDAR point cloud data, providing an effective means to map encroached shrub AGB spatially explicitly, even at the individual scale. In this study, we first extracted the individual shrubs based on thresholds in normalized difference vegetation index (NDVI) and canopy height model (CHM) using UAV-based multispectral and LiDAR data. For each shrub, we then derived and determined the dominant geometric, spectral, and textural features from the high-resolution multispectral image and the volumetric features from the LiDAR data as predictors of shrub AGB. Finally, we compared the capability of different data sources (UAV-based multispectral image, LiDAR, and their combination) and regression methods (multiple linear, random forest, and support vector regression) to estimate and map the individual shrub AGB in the study area. The volume-based approaches to individual shrub AGB, including global convex hull method, voxel method, and surface differencing method, were also employed using terrestrial laser scanning (TLS) to further calibrate the UAV-based estimation. Our results show that individual shrubs can be accurately extracted based on the threshold method with an overall classification accuracy of 91.8%. The UAV-based AGB estimation suggests that the textural feature, the sum of contrast metric within the individual shrub canopy, is the most important predictor of individual shrub AGB, followed by volumetric, geometric and spectral features. Moreover, the high-resolution multispectral image shows greater potential (R2 = 0.83, RMSE = 106.46 g) than LiDAR (R2 = 0.77, RMSE = 123.33 g) in the estimation of individual shrub AGB, and their combination can only slightly improve the estimation accuracy (R2 = 0.86, RMSE = 101.97 g). Our results also show that TLS-derived volume based on the surface differencing method obtained the best prediction accuracy of individual shrub AGB (R2 = 0.91, RMSE = 79.98 g), and can be used as an alternative of destructive harvesting. This study provides a new insight for quantifying and mapping individual shrub AGB using UAV-based optical sensors and TLS without destructive harvesting in arid and semiarid grasslands

Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest

Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- and intra-specific variation of functional traits. However, quantifying plant traits and FD consistently and cost-effectively across large and heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection and ranging (LiDAR) and imaging spectroscopy provide spatially explicit data, which allow mapping of selected forest traits and FD at different spatial scales. We develop an individual tree-based method to measure forest FD from tree neighborhoods to whole forests, and demonstrate the approach by mapping functional traits of over one million trees in a subtropical forest in China. We retrieved canopy morphological traits (95th quantile height, leaf area index and foliage height diversity) and physiological traits (proxies of nitrogen, carotenoids and specific leaf area) for each individual canopy tree crown from LiDAR and imaging spectroscopy data, respectively. Based on the multivariate trait space spanned by the six trait axes and filled by measured tree individuals, we mapped forest FD as richness, divergence and evenness, and explored spatial patterns of FD as well as FD–area and FD–tree number relationships. The results show that LiDAR-derived morphological traits and spectral indices of physiological traits are consistent with field measurements and show weak correlations between each other at individual tree level. Morphological functional richness follows a hump-shaped pattern along the elevational gradient of 984–1805 m, with maximum values at elevations around 1450 m, while high physiological functional richness occurs at medium and high elevations. At an ecosystem scale of 30 × 30 m, morphological richness increases continuously with tree density, but physiological richness decreases again at very high densities. Moreover, functional richness shows a logarithmic relationship with increasing area or number of individual trees, and local trait convergence is predominant in our study area. We demonstrate the ability to quantify FD using morphological and physiological traits by remote sensing, which provides a pathway to conduct individual-level trait-based ecology with wall-to-wall data

Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data

Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are strongly related to the city’s heterogeneity and have significant spatial-temporal characteristics but fail to address the albedo of the urban surface as a unique variable in urban thermal environment research. This study selects Beijing as the experimental area for exploring the spatial-temporal characteristics of the urban surface albedo and the albedo’s uniqueness in environmental research on urban spaces. Our results show that the urban surface albedo at high spatial resolution can better represent the urban spatial heterogeneity, seasonal variation, building canyon, and pixel adjacency effects. Urban surface albedo is associated with building density and height, land surface temperature (LST), and fractional vegetation cover (FVC). Furthermore, albedo can reflect livability and environmental rating due to the variances of building materials and architectural formats in the urban development. Hence, we argue that the albedo of the urban surface can be considered as a unique variable for improving the acknowledgment of the urban environment and human livability with wider application in urban environmental research

Trends of greening and browning in terrestrial vegetation in China from 2000 to 2020

Terrestrial vegetation condition is altering generally as a result of climate change and anthropogenic activity during the past few decades. To reveal the impact of long-term climate factors and artificial protection on multiple vegetation types, it is crucial to understand the spatial distribution of vegetation greening and browning and the effect of national ecological restoration programs. In this study, we established a persistent vegetation change index (P-value) to characterize greening (restoration) and browning (degradation) in China in 2000–2020. Firstly, we generated annual time-series normalized difference vegetation index (NDVI) data from MODIS product by averaging the monthly maximum NDVI values for each year. Secondly, we calculated the P-value to investigate the continuous change in vegetation state by incremental time interval. Finally, patterns and trends of greening and browning in forests, shrublands, and grasslands were quantified and mapped at pixel and sample point levels. The findings of our study revealed that Chinese wild vegetated lands greened up by ∼3.4 × 104 km2 (25%) and turned brown in ∼1.6 × 104 km2 (11%) between 2000 and 2020. Net greening was detected in all biomes, most conspicuously in several ecological program regions in northern China. The NDVI time-series data in 31% of field plots showed a consistent result, 11% of field plots showed a browning trend, and 58% of field plots showed a stable state. These results indicated a synergistic effect on forests, shrublands, and grasslands, but with regional variations attributed to differences in precipitation abundance, the implementation of positive ecological programs by the government and negative human activities. Additionally, these findings provide valuable insight into large-scale terrestrial vegetation transitions and have practical applications for decision-making and policy development in the assessment and restoration of ecosystems aimed at reducing carbon emissions, mitigating climate change, preserving biodiversity, and conserving water resources in China