scholarly journals Remote Sensing of Grassland Production and Management—A Review

2020 ◽  
Vol 12 (12) ◽  
pp. 1949
Author(s):  
Sophie Reinermann ◽  
Sarah Asam ◽  
Claudia Kuenzer

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 600
Author(s):  
Darren J. Murphy ◽  
Michael D. Murphy ◽  
Bernadette O’Brien ◽  
Michael O’Donovan

The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.


Geography ◽  
2020 ◽  
Author(s):  
Yingkui Li

Geomorphology is the science of studying landforms, landscapes, and their related processes, including the description, materials, classification, origin, evolution, and history of earth/planetary surfaces. Geographic information system (GIS) is a computer-based system used for collection, maintenance, storage, retrieval, analysis, and distribution of geographic data and information. A closely related technique to GIS is remote sensing (RS), the noncontact recording of electromagnetic spectrum of earth/planetary surfaces based on satellite-, aircraft-, or ground-based sensors to measure, detect, and classify ground objects. GIS and remote sensing have been integrated in many geomorphological studies to quantify surface processes and landforms. GIS/RS has been strongly linked with the methodology and concepts in geomorphology since its initial development. With the continual development of GIS and RS techniques, GIS/RS has been widely used to classify landform and landscape units, extract specific landform features, quantify process-landform relationships, and detect geomorphic changes. In particular, the combination of GIS/RS with digital elevation models (DEMs) has become one of the most common approaches for geomorphological research, especially with the early-21st-century progress in using LiDAR (light detection and ranging) and UAS (unmanned aircraft systems) to obtain high-resolution DEMs. A new discipline, geomorphometry, have been developed to quantify landforms and topography at various spatial scales on the basis of mathematical, statistical, and image-processing techniques. This article first includes a section focusing on the use of GIS/RS in general landform and landscape classification and then categorizes literature into a variety of subfields of geomorphology in which GIS/RS has been applied to solve geomorphological issues. These subfields include Glacial Geomorphology, Watershed and Fluvial Geomorphology, Hillslope Processes and Landslides, Coastal Geomorphology, Karst Geomorphology, Aeolian Geomorphology, and Tectonic Geomorphology. Some subfields, such as volcanic geomorphology and planetary geomorphology, are not included, but the methods and principles summarized in this article can be applied to these subfields.


2003 ◽  
Vol 27 (1) ◽  
pp. 44-68 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul Treitz

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.


2017 ◽  
Vol 43 (3) ◽  
pp. 1486
Author(s):  
K. Nikolakopoulos ◽  
P. Tsompos

In the frame of the “Urban Geology” project of IGME a lot of remote sensing applications were carried out: DSMs creation and accuracy verification, orthorectification of very high resolution satellite data, data fusion, multitemporal and multisensor image analysis, land cover and land use change detection e.t.c. The applications that took place in the pilot case of Nafplio are presented in this study


2007 ◽  
Author(s):  
Corina Alecu ◽  
Nektarios Chrysoulakis ◽  
Simona Oancea ◽  
Gheorghe Stancalie

2021 ◽  
pp. 019
Author(s):  
Philippe Dubuisson ◽  
Adrien Deschamps

Le Cnes, en collaboration avec le CNRS/Insu et Météo-France, organisait en janvier 2020 à Toulouse la troisième édition de l'atelier Trattoria (Transfert radiatif dans les atmosphères terrestres pour les observations spatiales). Cet atelier est principalement consacré aux codes de transfert radiatif dans l'atmosphère terrestre pour les applications de télédétection spatiale, opérant sur l'ensemble de la gamme des longueurs d'onde de l'ultraviolet aux micro-ondes. Ces codes numériques sont fondamentaux pour la préparation des instruments de télédétection, ainsi que pour le traitement et l'exploitation des données satellitaires. Cet atelier était ouvert à tous les chercheurs, ingénieurs, post-doctorants et doctorants du domaine. Les résultats et recommandations de l'atelier doivent servir de guide au Cnes et aux divers participants et utilisateurs français et européens de codes de transfert radiatif. The CNES, in collaboration with the CNRS/INSU and Météo-France, organized in January 2020 in Toulouse the third edition of the Trattoria workshop (Transfert radiatif dans les atmosphères terrestres pour les observations spatiales). This workshop is mainly devoted to radiation transfer codes in the Earth's atmosphere for space remote sensing applications, operating over the entire wavelength range from ultraviolet to microwaves. These numerical codes are fundamental for the preparation of remote sensing instruments, as well as for the processing and exploitation of satellite data. This workshop was open to all researchers, engineers, post-doctoral and doctoral students in the field. The results and recommendations of the workshop should serve as a "guide" for CNES and the various French and European participants and users of radiative transfer codes.


Author(s):  
Yenni Vetrita ◽  
Indah Prasasti ◽  
Nanik Suryo Haryani ◽  
M Priyatna ◽  
M Rokhis Khomarudin

This study evaluated two parameters of fire danger rating system (FDRS) using remote sensing data i.e. drought code (DC) and fine fuel moisture code (FFMC) as an early warning program for forest/land fire in Indonesia. Using the reference DC and FFMC from observation data, we calculated the accuracy, bias, and error. The results showed that FFMC from satellite data had a fairly good correlation with FFMC observations (r=0.68, bias=7.6, and RMSE=15.7), while DC from satellite data had a better correlation with FFMC observations (r=0.88, bias=49.91, and RMSE=80.22). Both FFMC and DC from satellite and observation were comparable. Nevertheless, FFMC and DC satellite data showed an overestimation values than that observation data, particularly during dry season. This study also indicated that DC and FFMC could describe fire occurrence within a period of 3 months before fire occur, particularly for DC. These results demonstrated that remote sensing data can be used for monitoring and early warning fire in Indonesia.


2018 ◽  
Vol 10 (1) ◽  
pp. 525-548 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius

Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage  ≤  25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).


Author(s):  
R. Goyal ◽  
T. Jayasudha ◽  
P. Pandey ◽  
D. Rama Devi ◽  
A. Rebecca ◽  
...  

In recent years, the use of satellite data for geospatial applications has multiplied and contributed significantly towards development of the society. Satellite data requirements, in terms of spatial and spectral resolution, periodicity of data, level of correction and other parameters, vary for different applications. For major applications, remote sensing data alone may not suffice and may require additional data like field data. An application user, even though being versatile in his application, may not know which satellite data is best suited for his application, how to use the data and what information can be derived from the data. Remote sensing domain experts have the proficiency of using appropriate data for remote sensing applications. <br><br> Entrenching domain expertise into the system and building a knowledge base system for satellite data product selection is vital. Non specialist data users need a user-friendly software which guides them to the most suitable satellite data product on the basis of their application. Such tool will aid the usage for apt remote sensed data for various sectors of application users. Additionally, the consumers will be less concerned about the technical particulars of the platforms that provide satellite data, instead focusing on the content and values in the data product, meeting the timelines and ease of access. Embedding knowledge is a popular and effective means of increasing the power of using a system. This paper describes a system, driven by the built-in knowledge of domain experts, for satellite data products selection for geospatial applications.


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