scholarly journals Identification of Mung Bean in a Smallholder Farming Setting of Coastal South Asia Using Manned Aircraft Photography and Sentinel-2 Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3688
Author(s):  
Mustafa Kamal ◽  
Urs Schulthess ◽  
Timothy J. Krupnik
Keyword(s):  
South Asia ◽  
Satellite Data ◽  
Mung Bean ◽  
Aerial Photographs ◽  
Farm Size ◽  
Smallholder Farming ◽  
Data Set ◽  
Crop Type ◽  
Tree Classifier ◽  
Sentinel 2

Mung bean (Vigna radiata) plays an important role providing protein in the rice-based diet of the people in Bangladesh. In the coastal division of Barisal, our study area, the average farm size is less than 0.5 ha and individual fields measure about 0.10 ha. The availability of free Sentinel-2 optical satellite data acquired at a 10 m ground sampling distance (GSD) may offer an opportunity to generate crop area estimates in smallholder farming settings in South Asia. We combined different sources of in situ data, such as aerial photographs taken from a low flying manned aircraft, data collected on the ground, and data derived from satellite images to create a data set for a segment based classification of mung bean. User’s accuracy for mung bean was 0.98 and producer’s accuracy was 0.99. Hence, the accuracy metrics indicate that the random tree classifier was able to identify mung bean based on 10 m GSD data, despite the small size of individual fields. We estimated the mung bean area for 2019 at 109,416 ha, which is about 40% lower than the Department of Agricultural Extension estimates (183,480 ha), but more than four times higher than the 2019 data reported by the Bangladesh Bureau of Statistics (26,612 ha). Further analysis revealed that crop production tends to be clustered in the landscape by crop type. After merging adjacent segments by crop type, the following average cluster sizes resulted: 1.62 ha for mung bean, 0.74 ha for rice (Oryza sativa), 0.68 ha for weedy fallow and 0.40 ha for a category of other crops. This explains why 10 m GSD satellite data can be used for the identification of predominant crops grown in specific regions of South Asia.

scholarly journals Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

2021 ◽  
Vol 13 (4) ◽  
pp. 700
Author(s):  
Daniel Kpienbaareh ◽  
Xiaoxuan Sun ◽  
Jinfei Wang ◽  
Isaac Luginaah ◽  
Rachel Bezner Kerr ◽  
...  
Keyword(s):  
Land Cover ◽  
Temporal Resolution ◽  
Satellite Data ◽  
Radar Data ◽  
Agricultural Landscapes ◽  
Optical Data ◽  
Smallholder Farming ◽  
Crop Type ◽  
Crop Types ◽  
Sentinel 2

Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.

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 Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany)

2021 ◽  
Vol 4 ◽  
Author(s):  
Carsten Montzka ◽  
Bagher Bayat ◽  
Andreas Tewes ◽  
David Mengen ◽  
Harry Vereecken
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Google Earth ◽  
Training Data ◽  
Cumulative Distribution ◽  
Environmental Drivers ◽  
Summer Drought ◽  
Data Set ◽  
Crown Transparency ◽  
Sentinel 2

Droughts in recent years weaken the forest stands in Central Europe, where especially the spruce suffers from an increase in defoliation and mortality. Forest surveys monitor this trend based on sample trees at the local scale, whereas earth observation is able to provide area-wide information. With freely available cloud computing infrastructures such as Google Earth Engine, access to satellite data and high-performance computing resources has become straightforward. In this study, a simple approach for supporting the spruce monitoring by Sentinel-2 satellite data is developed. Based on forest statistics and the spruce NDVI cumulative distribution function of a reference year, a training data set is obtained to classify the satellite data of a target year. This provides insights into the changes in tree crown transparency levels. For the Northern Eifel region, Germany, the evaluation shows an increase in damaged trees from 2018 to 2020, which is in line with the forest inventory of North Rhine-Westphalia. An analysis of tree damages according to precipitation, land surface temperature, elevation, aspect, and slope provides insights into vulnerable spruce habitats of the region and enables to identify locations where the forest management may focus on a transformation from spruce monocultures to mixed forests with higher biodiversity and resilience to further changes in the climate system.

scholarly journals Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

2020 ◽  
pp. 1-17
Author(s):  
Murali Krishna Gumma ◽  
Kimeera Tummala ◽  
Sreenath Dixit ◽  
Francesco Collivignarelli ◽  
Francesco Holecz ◽  
...  
Keyword(s):  
Satellite Data ◽  
Spatial Mapping ◽  
Field Level ◽  
Crop Type ◽  
Level Information ◽  
Sentinel 2

scholarly journals Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3004
Author(s):  
Antonia Ivanda ◽  
Ljiljana Šerić ◽  
Marin Bugarić ◽  
Maja Braović
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Optical Parameter ◽  
Data Set ◽  
Chl A ◽  
Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

scholarly journals An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping

2021 ◽  
Vol 3 ◽  
pp. 100018 ◽  
Author(s):  
Xiao-Peng Song ◽  
Wenli Huang ◽  
Matthew C. Hansen ◽  
Peter Potapov
Keyword(s):  
Modis Data ◽  
Type Mapping ◽  
Crop Type ◽  
Sentinel 2

scholarly journals Poultry farmer response to disease outbreaks in smallholder farming systems

2020 ◽  
Author(s):  
Alexis Delabouglise ◽  
Nguyen Thi Le Thanh ◽  
Huynh Thi Ai Xuyen ◽  
Benjamin Nguyen-Van-Yen ◽  
Phung Ngoc Tuyet ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Low Income ◽  
Low Income Countries ◽  
Farm Size ◽  
Small Scale ◽  
Flock Size ◽  
Smallholder Farming ◽  
Southern Vietnam ◽  
Poultry Farms ◽  
The Impact

AbstractAvian influenza outbreaks have been occurring on smallholder poultry farms in Asia for two decades. Farmer responses to these outbreaks can slow down or accelerate virus transmission. We used a longitudinal survey of 53 small-scale chicken farms in southern Vietnam to investigate the impact of outbreaks with disease-induced mortality on harvest rate, vaccination, and disinfection behaviors. We found that in small broiler flocks (≤16 birds/flock) the estimated probability of harvest was 56% higher when an outbreak occurred, and 214% higher if an outbreak with sudden deaths occurred in the same month. Vaccination and disinfection were strongly positively correlated with flock size and farm size, respectively. Small-scale farmers – the overwhelming majority of poultry producers in low-income countries – tend to rely on rapid sale of birds to mitigate losses from diseases. As depopulated birds are sent to markets or trading networks, this reactive behavior has the potential to enhance onward transmission.One sentence summaryA cohort study of fifty three small-scale poultry farms in southern Vietnam reveals that when outbreaks occur with symptoms similar to highly pathogenic avian influenza, farmers respond by sending their chickens to market early, potentially exacerbating the effects of the outbreak.

scholarly journals Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3956
Author(s):  
Shan He ◽  
Huaiyong Shao ◽  
Wei Xian ◽  
Shuhui Zhang ◽  
Jialong Zhong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Single Type ◽  
Support Vector ◽  
Svm Classifier ◽  
Cultivated Land ◽  
Remote Sensing Images ◽  
Data Set ◽  
Abandoned Land ◽  
Spatio Temporal ◽  
Sentinel 2

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.

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