Cho-ying Huang

Farm-scale evaluation of the impacts of transgenic cotton on biodiversity, pesticide use, and yield

Higher yields and reduced pesticide impacts are needed to mitigate the effects of agricultural intensification. A 2-year farm-scale evaluation of 81 commercial fields in Arizona show that use of transgenic Bacillus thuringiensis (Bt) cotton reduced insecticide use, whereas transgenic cotton with Bt protein and herbicide resistance (BtHr) did not affect herbicide use. Transgenic cotton had higher yield than nontransgenic cotton for any given number of insecticide applications. However, nontransgenic, Bt and BtHr cotton had similar yields overall, largely because higher insecticide use with nontransgenic cotton improved control of key pests. Unlike Bt and BtHr cotton, insecticides reduced the diversity of nontarget insects. Several other agronomic and ecological factors also affected biodiversity. Nevertheless, pairwise comparisons of diversity of nontarget insects in cotton fields with diversity in adjacent noncultivated sites revealed similar effects of cultivation of transgenic and nontransgenic cotton on biodiversity. The results indicate that impacts of agricultural intensification can be reduced when replacement of broad-spectrum insecticides by narrow-spectrum Bt crops does not reduce control of pests not affected by Bt crops.

Applications of remote sensing to alien invasive plant studies.

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions.

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.

Sensitivity of landscape metrics to classification scheme

Landscape metrics are a standard tool in the study and monitoring of landscape pattern and change, but their statistical properties and behaviour across a range of classification schemes and landscapes, as well as their sensitivity to changing landscape patterns, are still not fully understood. We therefore investigated the sensitivity of 24 metrics to a number of land cover classes for three Arizona landscapes with different spatial patterns. To do so, we applied unsupervised classification of remotely sensed data with two different nominal spatial resolutions to generate maps containing 2–35 classes. We calculated metric values for these thematic maps and classified the metrics into six groups using principal components analysis. For each group, the nature and sensitivity of responses to differences in resolution, landscape pattern, and classification detail were assessed. Our results indicated that many metrics behaved predictably with increasing classification detail, increasing or decreasing at rates that were often relatively similar and independent to sensor and landscape pattern. At lower class numbers, metrics were most sensitive to increasing classification detail, and the effects of classification scheme were most erratic and sensitive to resolution and underlying landscape pattern. Overall, this study provides a descriptive overview of the sensitivity of common metrics to changes in classification scheme, as well as a first attempt to draw some generalizations about the importance of classification scheme in conjunction with resolution effects.

Discrimination of invaded and native species sites in a semi-desert grassland using MODIS multi-temporal data

Over the past several decades, one of the most significant changes in semi‐desert grasslands of the southwestern US has been the invasion of South African grass Eragrostis lehmanniana. The objective of this study was to characterize the phenology of systems occupied by E. lehmanniana and/or native grasses using time‐series of field observations and the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (MODIS NDVI) and brightness (red and near‐infrared reflectance) data. Results demonstrated that it was possible to use NDVI and/or spectral reflectance data to discern the phenological differences across a gradient of E. lehmanniana infested grasslands due to variations in plant biodiversity, morphology and seasonal productivity. This work establishes the feasibility of integrating field and MODIS vegetation and spectral time‐series data to characterise landscapes dominated by different herbaceous species, which in turn provides opportunities to monitor E. lehmanniana in semi‐arid environments at a large spatial scale.

Impacts of vegetation onset time on the net primary productivity in a mountainous island in Pacific Asia

Vegetation phenology reflects the response of a terrestrial ecosystem to climate change. In this study, we attempt to evaluate the El Ni?o/La Ni?a-Southern Oscillation (ENSO)-associated temporal dynamics of the vegetation onset and its influence on the net primary productivity (NPP) in a subtropical island (Taiwan) of Pacific Asia. We utilized a decade-long (2001?2010) time series of photosynthetically active vegetation cover (PV) data, which were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, to delineate the vegetation phenology. These data served as inputs for the phenological analysis toolbox TIMESAT. The results indicated that the delayed vegetation onset time was directly influenced by a dry spring (February and March) in which less than 40 mm of rainfall was received. This seasonal drought impeded vegetation growth in the subsequent growing season, most likely due to delayed impacts of moisture stress related to the preceding ENSO events. The significant correlations obtained between the annual MODIS NPP and both the vegetation onset time and the length of the growing season may imply that the accumulated rainfall in the spring season governs the annual NPP. The model simulations revealed that the frequency and intensity of the ENSO-related spring droughts might increase, which would result in cascading effects on the ecosystem metabolism.

Retrieving multi-scale climatic variations from high dimensional time-series MODIS green vegetation cover in a tropical/subtropical mountainous island

There are knowledge gaps in our understanding of vegetation responses to multi-scale climate-related variables in tropical/subtropical mountainous islands in the Asia-Pacific region. Therefore, this study investigated inter-annual vegetation dynamics and regular/irregular climate patterns in Taiwan. We applied principal component analysis (PCA) on 11 years (2001∼2011) of high-dimensional monthly photosynthetically active vegetation cover (PV) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and investigated the relationships between spatiotemporal patterns of the eigenvectors and loadings of each component through time and multi-scale climaterelated variations. Results showed that the first five components contributed to 96.4% of the total variance. The first component (PC1, explaining 94.5% of variance) loadings, as expected, were significantly correlated with the temporal dynamics of the PV (r = 0.94), which was mainly governed by regional climate. The temporal loadings of PC2 and PC3 (0.8% and 0.6% of variance, respectively) were significantly correlated with the temporal dynamics of the PV of forests (r = 0.72) and the farmlands (r = 0.80), respectively. The low-order components (PC4 and PC5, 0.3% and 0.2% of variance, respectively) were closely related to the occurrence of drought (r = 0.49) and to irregular ENSO associated climate anomalies (r = −0.54), respectively. Pronounced correlations were also observed between PC5 and the Southern Oscillation Index (SOI) with one to three months of time lags (r = −0.35 ∼ −0.43, respectively), revealing biophysical memory effects on the time-series pattern of the vegetation through ENSO-related rainfall patterns. Our findings reveal that the sensitivity of the ecosystems in this tropical/subtropical mountainous island may not only be regulated by regional climate and human activities but also be susceptible to large-scale climate anomalies which are crucial and comparable to previous large scale analyses. This study demonstrates that PCA can be an effective tool for analyzing seasonal and inter-annual variability of vegetation dynamics across this tropical/subtropical mountainous islandin the Pacific Ocean, which provides an opportunity to forecast the responses and feedbacks of terrestrial environments to future climate scenarios.

An Integrated Optical Remote Sensing System for Environmental Perturbation Research

Remote sensing is the only technology that can systematically monitor physical properties of the biosphere over a vast region. However, it is still a challenge to make these measures meaningful for assessing the impacts of environmental perturbation. Here, we integrate an optical remote sensing system termed EcoiRS (Ecosystem observation by an integrated Remote Sensing system) specifically for this purpose. EcoiRS consists of three subsystems: an off-the-shelf atmospheric correction model (ACORN), a cloud/shadow removal model, and an advanced spectral mixture analysis model (AutoMCU). The core of ACORN is a set of radiative transfer codes that can be used to remove the effects of molecular/aerosol scatterings and water vapor absorption from remotely sensed data, and to convert these digital signals to surface reflectance. Shadow and cloud cover that would obscure the reflective properties of land surfaces in an image can be minimized by referring to their optical and thermal spectral profiles. AutoMCU executes iterative unmixing for each pixel using selected spectral endmembers based upon the rule of Monte Carlo simulation. The main outcomes of EcoiRS include cover fractions of green vegetation, non-photosynthetically active vegetation and bare soils, along with uncertainty measures for each pixel. The dynamics of these derived products are significant indicators for monitoring the change of states of terrestrial environments, and they can be used for investigating different environmental perturbations. Here, we demonstrate studies of implementing EcoiRS to map three major but relatively less studied cases in a western Pacific island (Taiwan): typhoons, tree diseases and alien plant invasion.

Temporal and spatial patterns of remotely sensed litterfall in tropical and subtropical forests of Taiwan

Litterfall is important for returning nutrients and carbon to the forest floor, and microbes decompose the litterfall to release CO2 into the atmosphere. Litterfall is a pivotal component in the forest biogeochemical cycle, which is sensitive to climate variability and plant physiology. In this study, we combined field litterfall estimates and time series (2001–2011) climate (the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Tropical Rainfall Measuring Mission (TRMM) precipitations) and green vegetation (MODIS photosynthetically active vegetation cover (PV)) variables to estimate regional annual litterfall in tropical/subtropical forests in Taiwan. We found that time series MODIS LST- and PV-derived metrics, the annual accumulated MODIS LST, and coefficient of variation of PV, respectively, but not the TRMM precipitation variables were salient factors for the estimation (r2 = 0.548 and p < 0.001). The mean (±standard deviation) annual litterfall was 5.1 ± 1.2 Mg ha−1 yr−1 during the observation period. The temporal dynamics of the litterfall revealed that typhoons and consecutive drought events might affect the litterfall temporal variation. Overall, the annual litterfall decreased along the elevation gradient, which may reflect a change in the vegetation type. The northeast and northwest facing slopes yielded the highest amount of annual litterfall (≥5.9 Mg ha−1 yr−1), which was in contrast with the southern aspect (5.1 Mg ha−1 yr−1). This variation may be associated with the dryness of the microclimate influenced by solar radiation. This study demonstrates the feasibility of utilizing time series MODIS LST and PV data to predict large-scale field litterfall, which may facilitate large-scale monitoring of biogeochemical cycles in forest ecosystems.

Vegetation, land surface brightness, and temperature dynamics after aspen forest die‐off

Forest dynamics following drought-induced tree mortality can affect regional climate through biophysical surface properties. These dynamics have not been well quantified, particularly at the regional scale, and are a large uncertainty in ecosystem-climate feedback. We investigated regional biophysical characteristics through time (1995–2011) in drought-impacted (2001–2003), trembling aspen (Populus tremuloides Michx.) forests by utilizing Landsat time series green and brown vegetation cover, surface brightness (total shortwave albedo), and daytime land surface temperature. We quantified the temporal dynamics and postdrought recovery of these characteristics for aspen forests experiencing severe drought-induced mortality in the San Juan National Forest in southwestern Colorado, USA. We partitioned forests into three categories from healthy to severe mortality (Healthy, Intermediate, and Die-off) by referring to field observations of aspen canopy mortality and live aboveground biomass losses. The vegetation cover of die-off areas in 2011 (26.9% of the aspen forest) was significantly different compared to predrought conditions (decrease of 7.4% of the green vegetation cover and increase of 12.1% of the brown vegetation cover compared to 1999). The surface brightness of the study region 9 years after drought however was comparable to predrought estimates (12.7–13.7%). Postdrought brightness was potentially influenced by understory shrubs, since they became the top layer green canopies in disturbed sites from a satellites point of view. Satellite evidence also showed that the differences of land surface temperature among the three groups increased substantially (≥45%) after drought, possibly due to the reduction of plant evapotranspiration in the Intermediate and Die-off sites. Our results suggest that the mortality-affected systems have not recovered in terms of the surface biophysical properties. We also find that the temporal dynamics of vegetation cover holds great potential for assessing propensity of subsequent mortality during drought itself, which could provide effective monitoring and potentially a much needed “early warning” of drought-induced tree mortality.