Agricultural practices in grasslands detected by spatial remote sensing

Abstract

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.

Predicting Suitable Areas for Growing Cassava Using Remote Sensing and Machine Learning Techniques: A Study in Nakhon-Phanom Thailand

“…Crop health indicators can potentially be used to determine a specific crop's health conditions during the growth cycle. Remote sensing researchers use Normalized Difference Vegetation Index (NDVI), Vegetation Index (VI), and Enhanced Vegetation Index (EVI) to investigate different crop help conditions (Dusseux, Vertès, Corpetti, Corgne, & Hubert-moy, 2014;Prasad, Chai, Singh, & Kafatos, 2006;Tang, Li, Chen, Zhu, & Liu, 2012). For example, NDVI, which represents the greenness and health of crops (Prasad et al, 2006), can be used as a variable to determine crop yield.…”

Mapping Grassland Frequency Using Decadal MODIS 250 m Time-Series: Towards a National Inventory of Semi-Natural Grasslands

“…For example, the European CORINE Land Cover layer, which is produced every 6 years at a 1:100,000 scale by visual analysis of high spatial resolution Landsat or Sentinel images [21], was used to model farms with high nature value in Europe using a 1 × 1 km grid [7] and semi-natural grasslands in France using a 5 × 5 km grid [22]. Beyond these broad-scale maps based on the CORINE Land Cover layer, many studies based on automatic and fine-scale analyses have demonstrated the contribution of multi-temporal and high-spatial-resolution satellite data in discriminating grasslands from other LULC types [16,[23][24][25], characterizing forage quality [20], identifying agricultural practices [26] and mapping floristic variation in semi-natural grasslands [27][28][29]. However, discriminating semi-natural and temporary grasslands accurately remains a concern [23,30] due to the lack of temporal depth in remote sensing time-series and because a one-year observation is insufficient to discriminate between semi-natural and temporary grasslands [31].…”

“…Beyond the characteristics of remote sensing data, the selection of the classifier and the reference data is crucial for successful LULC classification [42]. Semi-natural grasslands encompass a diversity of habitats [19] whose spectral response varies greatly according to agricultural practices [26], flood duration [43], snow duration [44], phenological stage [45] or percentage of bare soil cover [1]. Parametric classifiers, such as the maximum likelihood classifier, are generally of limited value for identifying grasslands [1].…”

Remoción mecánica del material senescente para la recuperación de pasturas

“…Villalobos-Villalobos y WingChing-Jones: Estrategias de recuperación de pasturas 823 tiempo (Montagner et al, 2012). En países de clima templado se usa el segado como estrategia para reducir la altura del pasto y la biomasa remanente del ciclo de crecimiento anterior (otoño-invierno), previo al inicio del nuevo ciclo de crecimiento (primavera-verano) (Dusseux et al, 2014). Estrategias de este tipo permiten incrementar la uniformidad en pasturas (Tälle et al, 2016) y maximizar la eficiencia de cosecha del forraje producido en sistemas de pastoreo (Simioni et al, 2014).…”