Assessment of Multiplatform Satellite Image Frequency for Crop Health Monitoring

2020 ◽  
Author(s):  
Valeriy Kovalskyy ◽  
Xiaoyuan Yang
Keyword(s):  
Satellite Imagery ◽  
Health Monitoring ◽  
Critical Role ◽  
Satellite Image ◽  
Data Sources ◽  
Percent Cover ◽  
Digital Agriculture ◽  
Crop Health ◽  
Image Frequency ◽  
Imagery Data

<p>Imagery products are critical for digital agriculture as they help delivering value and insights to growers. Use of publicly available satellite data feeds by digital agriculture companies helps keeping imagery services affordable for broader base of farmers. Optimal use of public and private imagery data sources plays a critical role in the success of image based services for agriculture. </p><p>At the Climate Corporation we have established a program focused on intelligence about satellite image coverage and frequency expected in different geographies and times of the year which is becoming critical for global expansion of the company. In this talk we report the results of our analysis on publicly available imagery data sources for key agricultural regions of the globe. Also, we demonstrate how these results can guide commercial imagery acquisition decisions on the case study in Brazil, where some growers run the risk of going through the growing season without receiving imagery from one satellite if relying on a single source of satellite imagery. The study clearly shows the validity of approaches taken as the results matched with factual image deliveries to single digits of percent cover on regional level. Also, our analysis clearly captured realistic temporal and spatial details of chances in image frequency from addition of alternative satellite imagery sources to the production stream. The optimization in imagery acquisitions enables filling data gaps for research and development. In the meantime, it contributes to delivering greater value for growers in Crop Health Monitoring and other image based service. </p>

scholarly journals DETEKSI KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN CITRA SATELIT HIMAWARI-8 DI KALIMANTAN TENGAH

2020 ◽  
Vol 20 (2) ◽  
pp. 79-89
Author(s):  
Alpon Sepriando ◽  
Hartono Hartono ◽  
Retnadi Heru Jatmiko
Keyword(s):  
Temporal Resolution ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Observation Time ◽  
Omission Error ◽  
Commission Error ◽  
Fire Monitoring ◽  
Active Fire ◽  
Imagery Data

IntisariKebakaran hutan dan lahan terjadi hampir setiap tahun di Indonesia, terutama di wilayah Sumatera dan Kalimantan saat musim kemarau. Deteksi kebakaran hutan dan lahan dengan citra satelit menggunakan indikator yang disebut titik panas. Titik panas yang digunakan saat ini di Indonesia diperoleh dari pengolahan data citra satelit berorbit polar (MODIS dan VIIRS) dengan resolusi temporal yang rendah, yaitu hanya 6 kali dalam sehari. Tujuan dari penelitian ini adalah memanfaatkan data citra satelit Himawari-8 untuk deteksi kebakaran hutan dan lahan yang menghasilkan titik panas dengan resolusi temporal 10 menit, dimana hasilnya di validasi dengan citra polar dan data kebakaran lapangan. Lokasi penelitian berada di Provinsi Kalimantan Tengah dan waktu penelitian adalah bulan September 2019. Data yang digunakan untuk pengolahan adalah 5 saluran Advanced Himawari Imager, peta batas administrasi dan tutupan lahan. Pemrosesan data citra satelit mencakup pemilihan piksel penutup lahan dan batas administrasi, penentuan waktu pengamatan, eliminasi piksel awan, Algoritma Pemantau Kebakaran Aktif, dan validasi hasil. Data citra Himawari-8 dapat diolah menjadi titik panas dengan temporal 10 menit. Validasi terhadap citra polar memiliki tingkat akurasi 66,2%-75,4%, comission error 28,2-46,9% dan omission error 24,6-33,8%. Tingginya comision error terhadap citra VIIRS dikarenakan citra VIIRS memiliki resolusi spasial yang jauh lebih tinggi dibandingkan dengan citra Himawari-8.  AbstractForest and land fires occur almost every year in Indonesia, especially in Sumatra and Kalimantan during the dry season. Detection of forest and land fires with satellite imagery uses an indicator called a hotspot. The hotspots used today in Indonesia are obtained from the processing of polar orbital satellite image data (MODIS and VIIRS) with a low temporal resolution, which is only six times a day. The purpose of this study is to utilize Himawari-8 satellite imagery data for the detection of forest and land fires that produce hotspots with a temporal resolution of 10 minutes, where the results are validated with polar imagery and field fire data. The research location is in Central Kalimantan Province, and the time of the study is September 2019. Data used for processing are 5 Advanced Himawari Imager channels, administrative boundary maps, and land cover. Processing of satellite imagery data includes the selection of cover pixels and administrative boundaries, determination of observation time, elimination of cloud pixels, Active Fire Monitoring Algorithm, and validation of results. Himawari-8 image data can be processed into hotspots with a temporal 10 minutes. Validation of polar images has an accuracy rate of 66.2% -75.4%, commission error 28.2-46.9% and omission error 24.6-33.8%. The high commission error on the VIIRS image is because the VIIRS image has a much higher spatial resolution compared to the Himawari-8 image. 

scholarly journals The Utilization of Satellite Imagery Data to Predict Hydrology Characteristics in Dodokan Watershed

2016 ◽  
Vol 33 ◽  
pp. 36-43 ◽  
Author(s):  
Sri Malahayati Yusuf ◽  
Kukuh Murtilaksono ◽  
Mahendra Harjianto ◽  
Endah Herlina
Keyword(s):  
Satellite Imagery ◽  
Imagery Data

Building Detection in Earthquake Prone Urban Area with Satellite Imagery Data Using Deep Learning

2021 ◽  
Author(s):  
Edy Irwansyah ◽  
Alexander A. Santoso. Gunawan ◽  
Calvin Surya ◽  
Dewa Ayu Defina Audrey Nathania
Keyword(s):  
Deep Learning ◽  
Urban Area ◽  
Satellite Imagery ◽  
Building Detection ◽  
Imagery Data

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

Resolving pre-collapse slope motion at the February 2021 Chamoli rock-ice avalanche via feature tracking of optical satellite imagery

2021 ◽  
Author(s):  
Maximillian Van Wyk de Vries ◽  
Shashank Bhushan ◽  
David Shean ◽  
Etienne Berthier ◽  
César Deschamps-Berger ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Feature Tracking ◽  
Satellite Image ◽  
Image Feature ◽  
Sampling Distance ◽  
Velocity Changes ◽  
The Stability ◽  
Slide Area ◽  
Ice Avalanche

<p>On the 7<sup>th</sup> of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, resulting in over 200 dead or missing and widespread infrastructure damage. The rock-ice avalanche originated from a steep, glacierized north-facing slope with a history of instability, most recently a 2016 ice avalanche. In this work, we assess whether the slope exhibited any precursory displacement prior to collapse. We evaluate monthly slope motion over the 2015 and 2021 period through feature tracking of high-resolution optical satellite imagery from Sentinel-2 (10 m Ground Sampling Distance) and PlanetScope (3-4 m Ground Sampling Distance). Assessing slope displacement of the underlying rock is complicated by the presence of glaciers over a portion of the collapse area, which display surface displacements due to internal ice deformation. We overcome this through tracking the motion over ice-free portions of the slide area, and evaluating the spatial pattern of velocity changes in glaciated areas. Preliminary results show that the rock-ice avalanche bloc slipped over 10 m in the 5 years prior to collapse, with particularly rapid slip occurring in the summer of 2017 and 2018. These results provide insight into the precursory conditions of the deadly rock-ice avalanche, and highlight the potential of high-resolution optical satellite image feature tracking for monitoring the stability of high-risk slopes.</p>

scholarly journals Similar understorey structure in spite of edaphic and floristic dissimilarity in Amazonian forests

2015 ◽  
Vol 45 (4) ◽  
pp. 393-404 ◽  
Author(s):  
Lassi SUOMINEN ◽  
Kalle RUOKOLAINEN ◽  
Timo PITKÄNEN ◽  
Hanna TUOMISTO
Keyword(s):  
Satellite Imagery ◽  
Forest Structure ◽  
Satellite Image ◽  
Light Availability ◽  
Floristic Composition ◽  
Structural Features ◽  
Canopy Openness ◽  
Understorey Plants ◽  
Amazonian Forests ◽  
Geological Formations

Forest structure determines light availability for understorey plants. The structure of lowland Amazonian forests is known to vary over long edaphic gradients, but whether more subtle edaphic variation also affects forest structure has not beenresolved. In western Amazonia, the majority of non-flooded forests grow on soils derived either from relatively fertile sediments of the Pebas Formation or from poorer sediments of the Nauta Formation. The objective of this study was to compare structure and light availability in the understorey of forests growing on these two geological formations. We measured canopy openness and tree stem densities in three size classes in northeastern Peru in a total of 275 study points in old-growth terra firme forests representing the two geological formations. We also documented variation in floristic composition (ferns, lycophytes and the palm Iriartea deltoidea) and used Landsat TM satellite image information to model the forest structural and floristic features over a larger area. The floristic compositions of forests on the two formations were clearly different, and this could also be modelled with the satellite imagery. In contrast, the field observations of forest structure gave only a weak indication that forests on the Nauta Formation might be denser than those on the Pebas Formation. The modelling of forest structural features with satellite imagery did not support this result. Our results indicate that the structure of forest understorey varies much less than floristic composition does over the studied edaphic difference.

scholarly journals Drought risk in the Bolivian Altiplano associated with El Niño Southern Oscillation using satellite imagery data

2019 ◽  
Author(s):  
Claudia Canedo-Rosso ◽  
Stefan Hochrainer-Stigler ◽  
Georg Pflug ◽  
Bruno Condori ◽  
Ronny Berndtsson
Keyword(s):  
Satellite Imagery ◽  
El Niño ◽  
Crop Production ◽  
Vegetation Index ◽  
Crop Yields ◽  
El Nino ◽  
Southern Oscillation ◽  
Drought Risk ◽  
Bolivian Altiplano ◽  
Imagery Data

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large losses to farmers, especially during positive ENSO phases. However, empirical data for drought risk estimation purposes are scarce and spatially uneven distributed. Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. Consequently, the satellite climate data were associated with the Normalized Difference Vegetation Index (NDVI) in order to evaluate the crop production variability. Moreover, NDVI was used to target specific drought hotspot regions. Furthermore, during positive ENSO phase (El Niño years), a significant decrease in crop yields can be expected and we indicate areas where losses will be most pronounced. The results can be used for emergency response operations and enable a pro-active approach to disaster risk management against droughts. This includes economic-related and risk reduction strategies such as insurance and irrigation.

scholarly journals Monitoring scrub weed change in the Canterbury region using satellite imagery

2007 ◽  
Vol 60 ◽  
pp. 137-140 ◽  
Author(s):  
J.D. Shepherd ◽  
J.R. Dymond ◽  
J.R.I. Cuff
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Satellite Image ◽  
Visual Assessment ◽  
Woody Vegetation ◽  
Spatial Change ◽  
Valid Data ◽  
Woody Cover ◽  
Landsat Satellite ◽  
Thematic Data

The spatial change of woody vegetation in the Canterbury region was automatically mapped between 1990 and 2001 using Landsat satellite image mosaics The intersection of valid data from these mosaics gave coverage of 84 of the Canterbury region Changes in woody cover greater than 5 ha were identified Of the 5 ha areas of woody change only those that were likely to have been a scrub change were selected using ancillary thematic data for current vegetation cover (eg afforestation and deforestation were excluded) This resulted in 2466 polygons of potential scrub change These polygons were rapidly checked by visual assessment of the satellite imagery and assigned to exotic or indigenous scrub change categories Between 1990 and 2001 the total scrub weed area in the Canterbury region increased by 3600 400 ha and indigenous scrub increased by 2300 400 ha

scholarly journals Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

2020 ◽  
Vol 1 (4) ◽  
pp. 125-134
Author(s):  
Pawan Rachee
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Object Segmentation ◽  
Satellite Image ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Saliency Map ◽  
Enhancement Technique ◽  
Map Segmentation ◽  
Quantitative Manner

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed   Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

scholarly journals Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

2020 ◽  
Author(s):  
Carrie Manore ◽  
Geoffrey Fairchild ◽  
Amanda Ziemann ◽  
Nidhi Parikh ◽  
Katherine Kempfert ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Data Streams ◽  
Cumulative Risk ◽  
State Level ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Global Dynamics ◽  
Flexible System ◽  
Clinical Surveillance ◽  
Spatio Temporal

ABSTRACTPredicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil’s 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models. One size does not fit all, particularly when considering state-level predictions as opposed to the whole country. Our first-of-its-kind high resolution flexible system for predicting dengue incidence with heterogeneous (and still sometimes sparse) data can be extended to multiple applications and regions.

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