scholarly journals Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1-7 ◽  
Author(s):  
H. Bach ◽  
P. Klug ◽  
T. Ruf ◽  
S. Migdall ◽  
F. Schlenz ◽  
...  
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Crop Growth ◽  
Optical Data ◽  
Current Status ◽  
Management Decisions ◽  
Area Index ◽  
Crop Type ◽  
Multi Temporal ◽  
Information Products

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. <br><br> M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. <br><br> This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is well suited for the use of Sentinel-2’s continuous and manifold data stream.

scholarly journals Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China

2019 ◽  
Vol 11 (7) ◽  
pp. 809 ◽  
Author(s):  
Lijuan Wang ◽  
Guimin Zhang ◽  
Ziyi Wang ◽  
Jiangui Liu ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Crop Growth ◽  
Future Trend ◽  
Current Status ◽  
Research Trend ◽  
Growth Monitoring ◽  
Knowledge Mapping ◽  
Area Index ◽  
Quantitative Visualization

Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as “vegetation index”, “crop growth”, “winter wheat”, “leaf area index (LAI)”, and “model” in the field of crop growth monitoring, and “LAI” and “unmanned aerial vehicle (UAV)”, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.

scholarly journals STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1099-1102
Author(s):  
S. Liu ◽  
H. Li ◽  
X. Wang ◽  
L. Guo ◽  
R. Wang
Keyword(s):  
Information Entropy ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Texture Features ◽  
Color Difference ◽  
Large Area ◽  
Color Mapping ◽  
Multi Temporal ◽  
Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95&amp;thinsp;% and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

scholarly journals Pemetaan Mangrove Kepulauan Lease, Provinsi Maluku Menggunakan Data Multi-Temporal Dan Multi-Sensor Citra Satelit Landsat

2018 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sam Wouthuyzen ◽  
Fasmi Ahmad
Keyword(s):  
Cloud Cover ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Multi Temporal ◽  
The Mean ◽  
Landsat 5 Tm ◽  
Landsat Satellite ◽  
The Cost ◽  
Near Future

<strong>Mangrove Mapping of The Lease Islands, Maluku Province Using Multi-Temporal And Multi-Sensor Of Landsat Satellite Images.</strong> Mangrove mapping in the Lease Islands, Maluku Province has been done, but using only a single date satellite image. Therefore, it is difficult to know the dynamics of their changes.  The aim of this study is to map mangroves every 5 year (1985-2015) using multi-sensors (MSS, TM, ETM+ and OLI) of Landsat and field data. Supervised classification using maximum likelihood was used for classifying mangrove and other habitats, and counting their areas. Results showed that mangrove in the Saparua and Nusalaut Islands, consisted of 22 and 13 species, respectively, with the longest distribution along the cost line of Tuhaha Bay due to freshwater supplay from the surrounding river, while the rest are grown in the hardy reef flat substrates. The mean overall acurracies of the maps was good enough (74.7%), except for one Landsat-5 TM and Landat-8 OLI because of the influences of cloud cover or haze.  During 30 years, the areas of mangrove are relatively stable since they are protected by local wisdom called "Kewang". The highest bias of 11.4% that made the areas of mangrove increase or decrease was not due to the utilization or conversion of mangrove, but mainly due to the influences of cloud cover/haze and the geometric differences among Landsat sensors. In the near future, the OBIA method should be try, because it seems to be able to produce mangrove maps with better accuracy.

scholarly journals Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2401 ◽  
Author(s):  
Chuanliang Sun ◽  
Yan Bian ◽  
Tao Zhou ◽  
Jianjun Pan
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Landsat 8 ◽  
Growth Status ◽  
Sensing Data ◽  
Crop Type ◽  
Multi Temporal ◽  
Crop Types ◽  
Sentinel 2

Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

scholarly journals TOWARDS POST-DISASTER DEBRIS IDENTIFICATION FOR PRECISE DAMAGE AND RECOVERY ASSESSMENTS FROM UAV AND SATELLITE IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 297-302 ◽  
Author(s):  
S. Ghaffarian ◽  
N. Kerle
Keyword(s):  
Remote Sensing ◽  
Disaster Response ◽  
Damage Assessment ◽  
Satellite Images ◽  
Satellite Image ◽  
Removal Time ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Uav Images ◽  
Disaster Debris

<p><strong>Abstract.</strong> Often disasters cause structural damages and produce rubble and debris, depending on their magnitude and type. The initial disaster response activity is evaluation of the damages, i.e. creation of a detailed damage estimation for different object types throughout the affected area. First responders and government stakeholders require the damage information to plan rescue operations and later on to guide the recovery process. Remote sensing, due to its agile data acquisition capability, synoptic coverage and low cost, has long been used as a vital tool to collect information after a disaster and conduct damage assessment. To detect damages from remote sensing imagery (both UAV and satellite images) structural rubble/debris has been employed as a proxy to detect damaged buildings/areas. However, disaster debris often includes vegetation, sediments and relocated personal property in addition to structural rubble, i.e. items that are wind- or waterborne and not necessarily associated with the closest building. Traditionally, land cover classification-based damage detection has been categorizing debris as damaged areas. However, in particular in waterborne disaster such as tsunamis or storm surges, vast areas end up being debris covered, effectively hindering actual building damage to be detected, and leading to an overestimation of damaged area. Therefore, to perform a precise damage assessment, and consequently recovery assessment that relies on a clear damage benchmark, it is crucial to separate actual structural rubble from ephemeral debris. In this study two approaches were investigated for two types of data (i.e., UAV images, and multi-temporal satellite images). To do so, three textural analysis, i.e., Gabor filters, Local Binary Pattern (LBP), and Histogram of the Oriented Gradients (HOG), were implemented on mosaic UAV images, and the relation between debris type and their time of removal was investigated using very high-resolution satellite images. The results showed that the HOG features, among other texture features, have the potential to be used for debris identification. In addition, multi-temporal satellite image analysis showed that debris removal time needs to be investigated using daily images, because the removal time of debris may change based on the type of disaster and its location.</p>

scholarly journals CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL

2016 ◽  
Vol XLI-B2 ◽  
pp. 95-100 ◽  
Author(s):  
D. S. Candra ◽  
S. Phinn ◽  
P. Scarth
Keyword(s):  
Satellite Images ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Landsat 8 ◽  
Qualitative And Quantitative ◽  
Algorithm Comparison ◽  
Multi Temporal ◽  
The Difference ◽  
Very High ◽  
Better Than

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

scholarly journals CLOUD AND CLOUD SHADOW MASKING USING MULTI-TEMPORAL CLOUD MASKING ALGORITHM IN TROPICAL ENVIRONMENTAL

2016 ◽  
Vol XLI-B2 ◽  
pp. 95-100 ◽  
Author(s):  
D. S. Candra ◽  
S. Phinn ◽  
P. Scarth
Keyword(s):  
Satellite Images ◽  
Confusion Matrix ◽  
Satellite Image ◽  
Landsat 8 ◽  
Qualitative And Quantitative ◽  
Algorithm Comparison ◽  
Multi Temporal ◽  
The Difference ◽  
Very High ◽  
Better Than

A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.

scholarly journals Generating Red-Edge Images at 3 M Spatial Resolution by Fusing Sentinel-2 and Planet Satellite Products

2019 ◽  
Vol 11 (12) ◽  
pp. 1422 ◽  
Author(s):  
Wei Li ◽  
Jiale Jiang ◽  
Tai Guo ◽  
Meng Zhou ◽  
Yining Tang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Area Index ◽  
Red Edge ◽  
Quantitative Remote Sensing ◽  
High Resolution Satellite Images ◽  
Fused Image ◽  
Sentinel 2

High-resolution satellite images can be used to some extent to mitigate the mixed-pixel problem caused by the lack of intensive production, farmland fragmentation, and the uneven growth of field crops in developing countries. Specifically, red-edge (RE) satellite images can be used in this context to reduce the influence of soil background at early stages as well as saturation due to crop leaf area index (LAI) at later stages. However, the availability of high-resolution RE satellite image products for research and application globally remains limited. This study uses the weight-and-unmixing algorithm as well as the SUPer-REsolution for multi-spectral Multi-resolution Estimation (Wu-SupReME) approach to combine the advantages of Sentinel-2 spectral and Planet spatial resolution and generate a high-resolution RE product. The resultant fused image is highly correlated (R2 > 0.98) with Sentinel-2 image and clearly illustrates the persistent advantages of such products. This fused image was significantly more accurate than the originals when used to predict heterogeneous wheat LAI and therefore clearly illustrated the persistence of Sentinel-2 spectral and Planet spatial advantage, which indirectly proved that the fusion methodology of generating high-resolution red-edge products from Planet and Sentinel-2 images is possible. This study provided method reference for multi-source data fusion and image product for accurate parameter inversion in quantitative remote sensing of vegetation.

scholarly journals Hymenopteran Parasitoids of Aphid Pests within Australian Grain Production Landscapes

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Samantha E. Ward ◽  
Paul A. Umina ◽  
Sarina Macfadyen ◽  
Ary A. Hoffmann
Keyword(s):  
Aphid Species ◽  
Crop Growth ◽  
Diaeretiella Rapae ◽  
Field Variation ◽  
Grain Crops ◽  
Growing Seasons ◽  
Crop Type ◽  
Eastern Australia ◽  
Crop Growth Stage ◽  
Hymenopteran Parasitoids

In grain crops, aphids are important pests, but they can be suppressed by hymenopteran parasitoids. A challenge in incorporating parasitoids into Integrated Pest Management (IPM) programs, however, is that parasitoid numbers can be low during periods within the season when aphids are most damaging. Understanding the population dynamics of key aphid species and their parasitoids is central to ameliorating this problem. To examine the composition and seasonal trends of both aphid and parasitoid populations in south-eastern Australia, samples were taken throughout the winter growing seasons of 2017 and 2018 in 28 fields of wheat and canola. Myzus persicae (Sulzer) was the most abundant aphid species, particularly within canola crops. Across all fields, aphid populations remained relatively low during the early stages of crop growth and increased as the season progressed. Seasonal patterns were consistent across sites, due to climate, crop growth stage, and interactions between these factors. For canola, field edges did not appear to act as reservoirs for either aphids or parasitoids, as there was little overlap in the community composition of either, but for wheat there was much similarity. This is likely due to the presence of similar host plants within field edges and the neighbouring crop, enabling the same aphid species to persist within both areas. Diaeretiella rapae (M’Intosh) was the most common parasitoid across our study, particularly in canola, yet was present only in low abundance at field edges. The most common parasitoid in wheat fields was Aphidius matricariae (Haliday), with field edges likely acting as a reservoir for this species. Secondary parasitoid numbers were consistently low across our study. Differences in parasitoid species composition are discussed in relation to crop type, inter-field variation, and aphid host. The results highlight potential focal management areas and parasitoids that could help control aphid pests within grain crops.

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