Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators

Giménez, Marta Gómez; Jong, Rogier de; Peruta, Raniero Della; Keller, Armin; Schaepman, Michael E.

doi:10.1016/j.rse.2017.06.003

Cited by 63 publications

(50 citation statements)

References 50 publications

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“…Classification of PG management practices have been proposed using several SPOT and Quickbird images in [8]. RapidEye time series to classify PG management practices intensity are processed in [7]. S1 and S2 datasets are proposed for exploring correlation bewteen NDVI and σ 0 over PG in [5], and in [4] for monitoring MowEve and grazing occurrences.…”

Section: B Methodological Analysis: Related Workmentioning

confidence: 99%

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Garioud¹,

Giordano²,

Valero

et al. 2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection.

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Section: B Methodological Analysis: Related Workmentioning

confidence: 99%

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Garioud¹,

Giordano²,

Valero

et al. 2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

View full text Add to dashboard Cite

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“…Gómez Giménez [167] demonstrated the potential of multi-temporal analysis, using a series of RapidEye optical images within a growing season in the Canton of Zurich, Switzerland, to determine mowing frequency using a change detection algorithm based on image differencing. In the microwave domain, Tamm et al [168] used the interferometric coherence calculated from 12-day Sentinel-1 image pairs to analyze the relationship to mowing events on grasslands.…”

Section: Grazing Vs Mowingmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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“…Therefore, we consider the use of annual values appropriated to carry out our analysis. In particular, grasslands have a standard growing season from February to November [76].…”

Section: Gpp Response To Limiting Factors Per Land Cover Class: Statimentioning

confidence: 99%

Studying the Influence of Nitrogen Deposition, Precipitation, Temperature, and Sunshine in Remotely Sensed Gross Primary Production Response in Switzerland

et al. 2019

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Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn’t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.

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Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators

Cited by 63 publications

References 50 publications

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

Remote Sensing and Cropping Practices: A Review

Studying the Influence of Nitrogen Deposition, Precipitation, Temperature, and Sunshine in Remotely Sensed Gross Primary Production Response in Switzerland

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