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

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha-1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha-1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

Canopy reflectance models illustrate varying NDVI responses to change in high latitude ecosystems

Multiyear trends in Normalized Difference Vegetation Index (NDVI) have been used as metrics of high latitude ecosystem change based on the assumption that NDVI change is associated with ecological change, generally as changes in green vegetation amount (green leaf area index [LAI] or plant cover). Further, no change in NDVI is often interpreted as no change in these variables. Three canopy reflectance models including linear mixture model, the SAIL (Scattering from Arbitrarily Inclined Leaves) model, and the GeoSail model were used to simulate scenarios representing high latitude landscape NDVI responses to changes in LAI and plant cover. The simulations showed inconsistent NDVI responses. Clear increases in NDVI are generally associated with increases in LAI and plant cover. At higher values of LAI, the change in NDVI per unit change in LAI decreases, with very little change in spruce forest NDVI where crown cover is >50% and at the tundra-taiga ecotone with transitions from shrub tundra to spruce woodland. These lower responses may bias the interpretation of greening/browning trends in boreal forests. Variations in water or snow coverage were shown to produce outsized nonbiological NDVI responses. Inconsistencies in NDVI responses exemplify the need for care in the interpretation of NDVI change as a metric of high latitude ecosystem change, and that landscape characteristics in terms of the type of cover and its characteristics, such as the initial plant cover, must be taken into account in evaluating the significance of any observed NDVI trends.

Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery

There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.

Vegetation Response to Urban Landscape Spatial Pattern Change in the Yangtze River Delta, China

Urbanization has destroyed and fragmented large amounts of natural habitats, resulting in serious consequences for urban ecosystems over past decades, especially in the rapidly urbanizing areas of developing countries. The Yangtze River Delta Urban Agglomeration, which has experienced the fastest socioeconomic development in China, was selected as the study area. To explore the relationship between urbanization and vegetation dynamics at the agglomeration scale, the spatiotemporal characteristics of urban expansion and vegetation variation of the study area were evaluated by landscape spatial analysis, regression analysis, and trend analysis. The results show that the urbanization level of the study area exhibited a continuous upward trend, with Shanghai as the regional core city, and the level of urbanization gradually decreased from the center towards the periphery of the urban agglomeration. The overall urban expansion presented obvious landscape spatial heterogeneity characteristics and the emergence of new cities and towns enhanced landscape connectedness and created a more aggregated urban agglomeration. Noticeable spatiotemporal differences of vegetation variation were observed from 2004 to 2013. Areas with relatively low vegetation coverage showed a steady growth trend, while those with higher vegetation coverage reported a significant decreasing trend. The spatial heterogeneity analysis of the vegetation trend demonstrated that vegetation degradation was a dominant and inevitable process across the study area. However, some parts of the urban sprawl area, especially at the periphery of the metropolis, may experience a greening trend rather than a browning trend, indicating that urbanization does not necessarily lead to large-scale vegetation degradation. Although urbanization poses a negative impact on vegetation and physical environments, urbanization has not yet reduced a large area of vegetation at the regional level.

Unmanned Aerial Vehicle for Remote Sensing Applications—A Review

The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

Replacing the Red Band with the Red-SWIR Band (0.74ρred+0.26ρswir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background

Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.

A gradient of nutrient enrichment reveals nonlinear impacts of fertilization on Arctic plant diversity and ecosystem function

Rapid environmental change at high latitudes is predicted to greatly alter the diversity, structure, and function of plant communities, resulting in changes in the pools and fluxes of nutrients. In Arctic tundra, increased nitrogen (N) and phosphorus (P) availability accompanying warming is known to impact plant diversity and ecosystem function; however, to date, most studies examining Arctic nutrient enrichment focus on the impact of relatively large (>25x estimated naturally occurring N enrichment) doses of nutrients on plant community composition and net primary productivity. To understand the impacts of Arctic nutrient enrichment, we examined plant community composition and the capacity for ecosystem function (net ecosystem exchange, ecosystem respiration, and gross primary production) across a gradient of experimental N and P addition expected to more closely approximate warming-induced fertilization. In addition, we compared our measured ecosystem CO ₂ flux data to a widely used Arctic ecosystem exchange model to investigate the ability to predict the capacity for CO ₂ exchange with nutrient addition. We observed declines in abundance-weighted plant diversity at low levels of nutrient enrichment, but species richness and the capacity for ecosystem carbon uptake did not change until the highest level of fertilization. When we compared our measured data to the model, we found that the model explained roughly 30%-50% of the variance in the observed data, depending on the flux variable, and the relationship weakened at high levels of enrichment. Our results suggest that while a relatively small amount of nutrient enrichment impacts plant diversity, only relatively large levels of fertilization-over an order of magnitude or more than warming-induced rates-significantly alter the capacity for tundra CO ₂ exchange. Overall, our findings highlight the value of measuring and modeling the impacts of a nutrient enrichment gradient, as warming-related nutrient availability may impact ecosystems differently than single-level fertilization experiments.

Greater deciduous shrub abundance extends tundra peak season and increases modeled net CO2 uptake

Satellite studies of the terrestrial Arctic report increased summer greening and longer overall growing and peak seasons since the 1980s, which increases productivity and the period of carbon uptake. These trends are attributed to increasing air temperatures and reduced snow cover duration in spring and fall. Concurrently, deciduous shrubs are becoming increasingly abundant in tundra landscapes, which may also impact canopy phenology and productivity. Our aim was to determine the influence of greater deciduous shrub abundance on tundra canopy phenology and subsequent impacts on net ecosystem carbon exchange (NEE) during the growing and peak seasons in the arctic foothills region of Alaska. We compared deciduous shrub-dominated and evergreen/graminoid-dominated community-level canopy phenology throughout the growing season using the normalized difference vegetation index (NDVI). We used a tundra plant-community-specific leaf area index (LAI) model to estimate LAI throughout the green season and a tundra-specific NEE model to estimate the impact of greater deciduous shrub abundance and associated shifts in both leaf area and canopy phenology on tundra carbon flux. We found that deciduous shrub canopies reached the onset of peak greenness 13 days earlier and the onset of senescence 3 days earlier compared to evergreen/graminoid canopies, resulting in a 10-day extension of the peak season. The combined effect of the longer peak season and greater leaf area of deciduous shrub canopies almost tripled the modeled net carbon uptake of deciduous shrub communities compared to evergreen/graminoid communities, while the longer peak season alone resulted in 84% greater carbon uptake in deciduous shrub communities. These results suggest that greater deciduous shrub abundance increases carbon uptake not only due to greater leaf area, but also due to an extension of the period of peak greenness, which extends the period of maximum carbon uptake.

Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Complex carbon cycle responses to multi-level warming and supplemental summer rain in the high Arctic

The Arctic has experienced rapid warming and, although there are uncertainties, increases in precipitation are projected to accompany future warming. Climate changes are expected to affect magnitudes of gross ecosystem photosynthesis (GEP), ecosystem respiration (ER) and the net ecosystem exchange of CO2 (NEE). Furthermore, ecosystem responses to climate change are likely to be characterized by nonlinearities, thresholds and interactions among system components and the driving variables. These complex interactions increase the difficulty of predicting responses to climate change and necessitate the use of manipulative experiments. In 2003, we established a long-term, multi-level and multi-factor climate change experiment in a polar semidesert in northwest Greenland. Two levels of heating (30 and 60 W m(-2) ) were applied and the higher level was combined with supplemental summer rain. We made plot-level measurements of CO2 exchange, plant community composition, foliar nitrogen concentrations, leaf δ(13) C and NDVI to examine responses to our treatments at ecosystem- and leaf-levels. We confronted simple models of GEP and ER with our data to test hypotheses regarding key drivers of CO2 exchange and to estimate growing season CO2 -C budgets. Low-level warming increased the magnitude of the ecosystem C sink. Meanwhile, high-level warming made the ecosystem a source of C to the atmosphere. When high-level warming was combined with increased summer rain, the ecosystem became a C sink of magnitude similar to that observed under low-level warming. Competition among our ER models revealed the importance of soil moisture as a driving variable, likely through its effects on microbial activity and nutrient cycling. Measurements of community composition and proxies for leaf-level physiology suggest GEP responses largely reflect changes in leaf area of Salix arctica, rather than changes in leaf-level physiology. Our findings indicate that the sign and magnitude of the future High Arctic C budget may depend upon changes in summer rain.

Incident radiation and the allocation of nitrogen within Arctic plant canopies: implications for predicting gross primary productivity

Arctic vegetation is characterized by high spatial variability in plant functional type (PFT) composition and gross primary productivity (P). Despite this variability, the two main drivers of P in sub-Arctic tundra are leaf area index (LT ) and total foliar nitrogen (NT ). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT . Our objective was to test the LT -NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan-Arctic. Including PFT-specific parameters in models of LT -NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT -NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass (NM ), and we show for the first time that LT -NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area (LM ). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT -NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales.

Demographic projection of high-elevation white pines infected with white pine blister rust: a nonlinear disease model

Matrix population models have long been used to examine and predict the fate of threatened populations. However, the majority of these efforts concentrate on long-term equilibrium dynamics of linear systems and their underlying assumptions and, therefore, omit the analysis of transience. Since management decisions are typically concerned with the short-term (< 100 years), asymptotic analyses could lead to inaccurate conclusions or, worse yet, critical parameters or processes of ecological concern may go undetected altogether. We present a stage-structured, deterministic, nonlinear, disease model which is parameterized for the population dynamics of high-elevation white pines in the face of infection with white pine blister rust (WPBR). We evaluate the model using newly developed software to calculate sensitivity and elasticity for nonlinear population models at any projected time step. We concentrate on two points in time, during transience and at equilibrium, and under two scenarios: a regenerating pine stand following environmental disturbance and a stand perturbed by the introduction of WPBR. The model includes strong density-dependent effects on population dynamics, particularly on seedling recruitment, and results in a structure favoring large trees. However, the introduction of WPBR and its associated disease-induced mortality alters stand structure in favor of smaller stages. Populations with infection probability (beta) > or = 0.1 do not reach a stable coexisting equilibrium and deterministically approach extinction. The model enables field observations of low infection prevalence among pine seedlings to be reinterpreted as resulting from disease-induced mortality and short residence time in the seedling stage. Sensitivities and elasticities, combined with model output, suggest that future efforts should focus on improving estimates of within-stand competition, infection probability, and infection cost to survivorship. Mitigating these effects where intervention is possible is expected to produce the greatest effect on population dynamics over a typical management timeframe.