Pauline Dusseux

Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Evaluation of SPOT imagery for the estimation of grassland biomass

In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.

Agricultural practices in grasslands detected by spatial remote sensing

The major decrease in grassland surfaces associated with changes in their management that has been observed in many regions of the earth during the last half century has major impacts on environmental and socio-economic systems. This study focuses on the identification of grassland management practices in an intensive agricultural watershed located in Brittany, France, by analyzing the intra-annual dynamics of the surface condition of vegetation using remotely sensed and field data. We studied the relationship between one vegetation index (NDVI) and two biophysical variables (LAI and fCOVER) derived from a series of three SPOT images on one hand and measurements collected during field campaigns achieved on 120 grasslands on the other. The results show that the LAI appears as the best predictor for monitoring grassland mowing and grazing. Indeed, because of its ability to characterize vegetation status, LAI estimated from remote sensing data is a relevant variable to identify these practices. LAI values derived from the SPOT images were then classified based on the K-Nearest Neighbor (KNN) supervised algorithm. The results points out that the distribution of grassland management practices such as grazing and mowing can be mapped very accurately (Kappa index = 0.82) at a field scale over large agricultural areas using a series of satellite images.

Temporal kernels for the identification of grassland management using time series of high spatial resolution satellite images

Grasslands, and more precisely agricultural practices associated with grasslands have an important impact on water and soil quality and biodiversity. In many regions, associated with agriculture intensification, a decrease of grasslands and change in their management can be observed during the last half century. Thus, determination of grassland management types represents an important approach for the quality and preservation of the environment. In this context, the objective of this study is to identify agricultural practices on grasslands from a time series of high spatial resolution images. Based on training samples, the classification of the LAI temporal profiles extracted from satellite images was performed using 1-standard classification technique as KNN and 2- an advanced ones using temporal kernels based on Dynamic Time Warping. Results show that use of an advanced classification technique improves of 20% the quality of grassland management identification.

Contribution of radar images for grassland management identification

This paper is concerned with the identification of grassland management using both optical and radar data. In that context, grazing, mowing and a mix of these two managements are commonly used by the farmers on grassland fields. These practices and their intensity of use have different environmental impact. Thus, the objectives of this study are, firstly, to identify grassland management practices using a time series of optical and radar imagery at high spatial resolution and, secondly, to evaluate the contribution of radar data to improve identification of farming practices on grasslands. Because of cloud coverage and revisit frequency of satellite, the number of available optical data is limited during the vegetation period. Thus, radar data can be considered as an ideal complement. The present study is based on the use of SPOT, Landsat and RADARSAT-2 data, acquired in 2010 during the growing period. After a pre-processing step, several vegetation indices, biophysical variables, backscattering coefficients and polarimetric discriminators were computed on the data set. Then, with the help of some statistics, the most discriminating variables have been identified and used to classify grassland fields. In addition, to take into account the temporal variation of variables, dedicated indexes as first and second order derivatives were used. Classification process was based on training samples resulting from field campaigns and computed according six methods: Decision Trees, K-Nearest Neighbor, Neural Networks, Support Vector Machines, the Naive Bayes Classifier and Linear Discriminant Analysis. Results show that combined use of optical and radar remote sensing data is not more efficient for grassland management identification.

Identification of grassland management practices from leaf area index time series

Abstract The main objective of this study is to identify grassland management practices using time series of remote sensing images. The accelerating agricultural intensification has strongly reduced grassland surfaces in some areas, generating important changes in their management and affecting environmental and socio-economic systems. Therefore, the identification of grassland management practices in farming systems is a key issue for sustainable agriculture. To this end, the leaf area index (LAI) estimated from remote sensing images was used since its temporal evolution is informative about farming practices. We evaluate the performances of two common classification algorithms using time profiles of LAI derived from simulated data and high spatial resolution satellite images. We show that they exhibit limited performances, mainly because they rely on criteria that are not suited for the comparison of time series. We then suggest the use of more advanced classification tools that work in a transformed space designed by a kernel function. We show that a kernel based on time warping measurements which are suited for the comparison of time series, perform better than classical ones based on Gaussian functions. This is a promising result for the analyzing of the future SENTINEL data that will be embedded in many time series.

Structural and functional mapping of geosigmeta in Atlantic coastal marshes (France) using a satellite time series

Abstract Geosynphytosociology deals with the study of combinations of vegetation series – or geosigmeta – within landscape. Its main advantage is to assess conservation status based on vegetation dynamics. However, this field-based approach has not been widely applied, because local surveys are not representative of spatio-temporal landscape complexity, which leads to uncertainties and errors for geosigmeta structural and functional mapping. In this context, satellite time series appear as relevant data for monitoring vegetation dynamics. This article aims to assess the contribution of an annual satellite time series for geosigmeta structural and functional mapping. The study area, which focuses on the French Atlantic coast (4630 km²), includes salt, brackish, sub-brackish and fresh marshes. A structural vegetation map was derived from the classification of an annual time series of 38 MODIS images validated with field surveys. The functional vegetation map was derived from the annual Integral of Normalized Difference Vegetation Index (NDVI-I), as an indicator of above-ground net primary production. Results show that geosigmeta were successfully mapped at a scale of 1:250,000 with an overall accuracy of 82.9%. The geosigmeta functional map highlights a strong gradient from the lowest NDVI-I values in salt marshes to the highest values in fresh marshes.

A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning

The open access availability of satellite images from new sensors characterized by various spatial and temporal resolutions provides new challenges and possibilities for biodiversity conservation. Methodologies aiming at characterizing vegetation type, phenology, and function can now benefit from metric spatial resolution imagery combined with an improved revisit capability. Here, we test hybrid methods and data fusion, using very high spatial resolution (VHSR) sensors in different complex landscapes encompassing three French biogeographical regions. The methodological approach presented herein has a generic value in response to national conservation targets based on the concept of essential biodiversity variables accessed by remote sensing (RS-enabled EBVs). We focused on deriving five RS-enabled EBVs from natural and seminatural open ecosystems: (1) ecosystem distribution, (2) land cover, (3) heterogeneity, (4) primary productivity and (5) vegetation phenology. The challenge was to develop a method that would be technically feasible, economically viable, and sustainable in time. We demonstrated that it is possible to derive key parameters required to develop a set of EBVs from remote sensing (RS) data. The combined use of remote sensing data sources with various spatial, temporal, and spectral resolutions is essential to obtain different indicators of natural habitats. One major current challenge for an improved contribution of RS to conservation is to strengthen multiple collaborative frameworks among remote sensing scientists, conservation biologists, and ecologists in order to increase the efficiency of methodological exchange and draw benefits for successful conservation planning strategies.

PaturMata, a model to manage grassland under climate change

Agricultural intensification has greatly decreased grassland surface area in some regions, thus changing grassland management and modifying environmental and socio-economic systems. Therefore, knowledge about grassland management practices in farming systems is needed for sustainable agriculture. In this context, the PaturMata model simulates grassland management at the farm scale. The PaturMata model simulates grassland dynamics and several factors such as farming practices, grass consumption, and fertilization. The model takes into account environmental and farming system parameters such as climate, field number, size, and location; livestock units; and conventional or organic agriculture. Here, we first ran the model under climatic conditions favorable to grass growth for four farms on an experimental site located in western France. Biophysical variables extracted from remote-sensing images were used to initialize PaturMata, whose predictions were compared to on-site surveys. We generated forecasting scenarios of the same farms under different climatic conditions. Results show that PaturMata predicts a 70 % decrease in grass consumption, a 50 % decrease in the number of annual grazing periods, and a 60 % increase in the amount of conserved forage consumed, when conditions are unfavorable to grass growth. We conclude that the PaturMata model can help design farms and management strategies capable of coping with a wide range of conditions.