Eddy De Pauw

Hyperspectral vegetation indices and their relationships with agricultural crop characteristics

The objective of this paper is to determine spectral bands that are best suited for characterizing agricultural crop biophysical variables. The data for this study comes from ground-level hyperspectral reflectance measurements of cotton, potato, soybeans, corn, and sunflower. Reflectance was measured in 490 discrete narrow bands between 350 and 1,050 nm. Observed crop characteristics included wet biomass, leaf area index, plant height, and (for cotton only) yield. Three types of hyperspectral predictors were tested: optimum multiple narrow band reflectance (OMNBR), narrow band normalized difference vegetation index (NDVI) involving all possible two-band combinations of 490 channels, and the soil-adjusted vegetation indices. A critical problem with OMNBR models was that of “over fitting” (i.e., using more spectral channels than experimental samples to obtain a highly maximum R2 value). This problem was addressed by comparing the R2 values of crop variables with the R2 values computed for random data of a large sample size. The combinations of two to four narrow bands in OMNBR models explained most (64% to 92%) of the variability in crop biophysical variables. The second part of the paper describes a rigorous search procedure to identify the best narrow band NDVI predictors of crop biophysical variables. Special narrow band lambda (λ1) versus lambda (λ2) plots of R2 values illustrate the most effective wavelength combinations (λ1 and λ2) and bandwidths (Δλ1 and Δλ2) for predicting the biophysical quantities of each crop. The best of these two-band indices were further tested to see if soil adjustment or nonlinear fitting could improve their predictive accuracy. The best of the narrow band NDVI models explained 64% to 88% variability in different crop biophysical variables. A strong relationship with crop characteristics is located in specific narrow bands in the longer wavelength portion of the red (650 nm to 700 nm), with secondary clusters in the shorter wavelength portion of green (500 nm to 550 nm), in one particular section of the near-infrared (900 nm to 940 nm), and in the moisture sensitive near-infrared (centered at 982 nm). This study recommends a 12 narrow band sensor, in the 350 nm to 1,050 nm range of the spectrum, for optimum estimation of agricultural crop biophysical information.

Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables

Recent studies have shown that novel genetic variation for resistance to pests and diseases can be detected in plant genetic resources originating from locations with an environmental profile similar to the collection sites of a reference set of accessions with known resistance, based on the Focused Identification of Germplasm Strategy (FIGS) approach. FIGS combines both the development of a priori information based on the quantification of the trait-environment relationship and the use of this information to define a best bet subset of accessions with a higher probability of containing new variation for the sought after trait(s). The present study investigates the development strategy of the a priori information using different modeling techniques including learning-based techniques as a follow up to previous work where parametric approaches were used to quantify the stem rust resistance and climate variables relationship. The results show that the predictive power, derived from the accuracy parameters and cross-validation, varies depending on whether the models are based on linear or non-linear approaches. The prediction based on learning techniques are relatively higher indicating that the non-linear approaches, in particular support vector machine and neural networks, outperform both principal component logistic regression and generalized partial least squares. Overall there are indications that the trait distribution of resistance to stem rust is confined to certain environments or areas, whereas the susceptible types appear to be limited to other areas with some degree of overlapping of the two classes. The results also point to a number of issues to consider for improving the predictive performance of the models.

Assessing woody biomass in African tropical savannahs by multiscale remote sensing

Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.

Using remote sensing to assess impacts of land management policies in the Ordos rangelands in China

Implementation of land management policies influences land use and hence causes environmental change. Taking the Ordos rangelands in China as a case study, this paper explores the potential of remote sensing to assess in dryland areas the impacts of policies on the environment. Thirteen Landsat images of the period 1978–2010 were acquired and those corresponding to the starting dates of implementation of different policies were selected for land-cover change analysis; others were used to check the detected change and track the normalized difference vegetation index (NDVI) trajectory matched with time series of meteorological data for calibration of natural response of rangelands to rainfall. The results indicate that policy impacts are complex and include both positive and negative aspects depending on the locality in space. On one hand, policies have aroused the enthusiasm of people in agricultural production and sand-control leading to the recovery of about 2618 km2 of desertified rangeland and sandy land, and economic growth, on the other hand, provoked vegetation degradation with an accumulated area of 2439 km2 when policies cannot reconcile the conflict between environmental protection and the interest of rural people. However, degradation is not absolute and can be mitigated by the implementation of rational policies.

Policy Impacts on Land Degradation: Evidence Revealed by Remote Sensing in Western Ordos, China

This paper presents a multi-temporal monitoring and assessment of biomass dynamics in response to land cover change in Western Ordos, one of the most important dry areas in China, aiming to reveal the impacts of governmental land management policies on the biomass production of the rangeland ecosystem and on land degradation. Multi-temporal Landsat images (MSS 1978, 1979; TM 1987, 1989, 1991, 2006 and 2007; ETM+ 1999, 2001, 2002, 2004) were used in this research. An integrated processing algorithm, indicator differencing and-thresholding and post-classification differencing, was applied to reveal the land biophysical change and rangeland degradation, and a relevant biomass estimation model was developed for the rangeland ecosystem based on other researchers’ work. Meteorological data since the 1960s were incorporated in the analysis to avoid false signals of degradation, as could arise from normal climatic variability. The results show that to some extent land management policies have been instrumental in the protection and recovery of grasslands biomass production. On the other hand, in the non-controlled and weakly monitored zones land degradation, in the form of biomass loss due to desert extension, vegetation degradation, salinisation and water-table decline has continued. This could be attributed to a combination of both natural and human factors, such as lack of protection against strong winds, collective grazing in the permitted rotation areas and previously controlled zones, and over-pumping for agricultural and sand control activities. From this case study, it seems that the effectiveness and rationality of land use policy depend on whether it can coincide with the interests of the local people while conserving the environment. Where there is a conflict between economic viability and environmental sustainability, land degradation is inevitable.

In silico evaluation of plant genetic resources to search for traits for adaptation to climate change

Plant genetic resources display patterns resulting from ecological and co-evolutionary processes. Such patterns are instrumental in tracing the origin and diversity of crops and locating adaptive traits. With climate change and the anticipated increase in demand for food, new crop varieties will be needed to perform under unprecedented climatic conditions. In the present study, we explored genetic resources patterns to locate traits of adaptation to drought and to maximize the utilization of plant genetic resources lacking ex ante evaluation for emerging climate conditions. This approach is based on the use of mathematical models to predict traits as response variables driven by stochastic ecological and co-evolutionary processes. The high congruence of metrics between model predictions and empirical trait evaluations confirms in silico evaluation as an effective tool to manage large numbers of crop accessions lacking ex ante evaluation. This outcome will assist in developing cultivars adaptable to various climatic conditions and in the ultimate use of genetic resources to sustain agricultural productivity under conditions of climate change.