scholarly journals ON THE JOINT EXPLOITATION OF OPTICAL AND SAR SATELLITE IMAGERY FOR GRASSLAND MONITORING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 591-598
Author(s):  
A. Garioud ◽  
S. Valero ◽  
S. Giordano ◽  
C. Mallet
Keyword(s):  
Temporal Dynamics ◽  
Agricultural Practices ◽  
Physical Nature ◽  
Multimodal Fusion ◽  
Contextual Knowledge ◽  
Essential Information ◽  
Object Based ◽  
Frequent Monitoring ◽  
Operational Solution ◽  
Sentinel 2

Abstract. Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R2 = 0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution.

scholarly journals Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1820 ◽  
Author(s):  
Lizhen Lu ◽  
Yuan Tao ◽  
Liping Di
Keyword(s):  
Land Cover ◽  
Crop Yields ◽  
Classification And Regression Tree ◽  
Optical Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Essential Information ◽  
Object Based ◽  
Plastic Mulching ◽  
Sentinel 2

Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%.

scholarly journals Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2123
Author(s):  
Aaron Aeberli ◽  
Kasper Johansen ◽  
Andrew Robson ◽  
David Lamb ◽  
Stuart Phinn
Keyword(s):  
Template Matching ◽  
Crop Production ◽  
Local Maximum ◽  
Banana Plant ◽  
Essential Information ◽  
Object Based ◽  
Agricultural Applications ◽  
Multi Temporal ◽  
Rule Set ◽  
Geographic Object

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

scholarly journals Sediment Transport Dynamism in the Confluence Area of Two Rivers Transporting Mainly Suspended Sediment Based on Sentinel-2 Satellite Images

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3132
Author(s):  
Ahmed Mohsen ◽  
Ferenc Kovács ◽  
Gábor Mezősi ◽  
Tímea Kiss
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Temporal Dynamics ◽  
Pearson Correlation ◽  
Unsupervised Classification ◽  
Mixing Process ◽  
Hydrological Parameters ◽  
Flow And Sediment Transport ◽  
Spatio Temporal ◽  
Sentinel 2

Downstream of the confluence of rivers, complex hydrological and morphological processes control the flow and sediment transport. This study aimed to analyze the spatio-temporal dynamics of suspended sediment in the confluence area of the Tisza and its main tributary Maros River using Sentinel-2 images and to reveal the correlation between the hydrological parameters and the mixing process through a relatively long period (2015–2021). The surficial suspended sediment dynamism was analyzed by applying K-means unsupervised classification algorithm on 143 images. The percentages of the Tisza (TW) and Maros (MW) waters and their mixture (MIX) were calculated and compared with the hydrological parameters in both rivers. The main results revealed that the areal, lateral, and longitudinal extensions of TW and MIX have a better correlation with the hydrological parameters than the MW. The Pearson correlation matrix revealed that the discharge ratio between the rivers controls the mixing process significantly. Altogether, 11 mixing patterns were identified in the confluence area throughout the studied period. The TW usually dominates the confluence in November and January, MW in June and July, and MIX in August and September. Predictive equations for the areal distribution of the three classes were derived to support future water sampling in the confluence area.

scholarly journals MANAGEMENT EFFECTS ON GROUND COVER <q>CLUMPINESS</q>: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 183-188 ◽  
Author(s):  
P. Scarth ◽  
R. Trevithick
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Field Data ◽  
Temporal Dynamics ◽  
Management Practice ◽  
Spatial Arrangement ◽  
Practice Change ◽  
Grazing Land ◽  
Land Condition ◽  
Sentinel 2

Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20&amp;thinsp;cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Group compositions: the politics of technology implemented in smallholder farming

2018 ◽  
Vol 25 (1) ◽  
pp. 703
Author(s):  
Harro Maat
Keyword(s):  
Oil Palm ◽  
Technology Implementation ◽  
Group Performance ◽  
Group Formation ◽  
Agricultural Practices ◽  
System Of Rice Intensification ◽  
Farm Labor ◽  
Smallholder Farming ◽  
Essential Information ◽  
Governmental Organization

This article investigates the connection between performance, group, and society. The argument is that group formation around particular farm operations and the details of the activities they engage in are an expression of the preferred way of technology implementation. The argument is developed using Paul Richards' notion of agriculture as performance. Two cases are presented. The first is the composition of a spraying team for weed control in smallholder oil palm production in Sumatra, connected to a global agreement on sustainable oil palm production, known as the Roundtable on Sustainable Palm Oil (RSPO). The second case is about a team of women transplanting young rice seedlings on prepared paddy fields in a village in Uttarakhand, India. A new way of rice transplanting was introduced by a local non-governmental organization, known as the System of Rice Intensification (SRI). The analysis shows that group performances provide essential information about how introduced plans, regulations and material designs are reworked and turned into meaningful and effective changes to agricultural practices. The article concludes that these activities are not merely technical adjustments but in themselves express arguments about the preferred way of organising farming, farm labor, and payments. Performing groups thus exert a form of bargaining power against development actors.Keywords: Group performance, smallholder farming, technology transfer, political acts

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