Quantifying water storage within the north of Lake Naivasha using sonar remote sensing and Landsat satellite data

2021 ◽  
Author(s):  
D. Walker ◽  
J.D. Shutler ◽  
E.H.J. Morrison ◽  
D.M. Harper ◽  
J.C.B. Hoedjes ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Water Storage ◽  
Lake Naivasha ◽  
The North ◽  
Landsat Satellite

scholarly journals A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1767
Author(s):  
Davide Cammarano ◽  
Hainie Zha ◽  
Lucy Wilson ◽  
Yue Li ◽  
William D. Batchelor ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Farming Systems ◽  
Small Scale ◽  
Sustainable Food ◽  
Environmentally Sustainable ◽  
Management Zone ◽  
The North ◽  
Landsat Satellite ◽  
The Village

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

scholarly journals MAPPING AND INDEX VEGETATION ANALYSES OF MANGROVE IN SAPARUA ISLAND, CENTRAL MOLUCCAS

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Harold J. D. Waas ◽  
Bisman Nababan
Keyword(s):  
Satellite Data ◽  
Coastal Areas ◽  
High Density ◽  
Island Area ◽  
The North ◽  
Vegetation Indexes ◽  
Mangrove Vegetation ◽  
Landsat 7 ◽  
Landsat Satellite ◽  
Very High

2TMapping and index vegetation analyses of mangrove in coastal areas of Saparua Island, Central Moluccas was conducted using Landsat 7/ETM+ satellite data acquired in April to May 2007. The results showed that the distributions of mangrove vegetation were concentrated in the north, south, and west of the region with the area of 218.88 ha (38.26%), 105.12 ha (18.38%), and 248.04 ha (43.36%), respectively. Total area of mangrove vegetation in this island was about 572.04 ha (5.72 kmP2P), or 3.49% of the island area. Vegetation indexes (NDVI) in the north, south, and west of the region were dominated by values of >0.7 (very high density).Keyword: Mangrove, NDVI, Landsat Satellite, Saparua, Central Maluku

scholarly journals Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 5369 ◽  
Author(s):  
Muhammad Sohail Memon ◽  
Zhou Jun ◽  
Chuanliang Sun ◽  
Chunxia Jiang ◽  
Weiyue Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wheat Straw ◽  
Satellite Data ◽  
Vegetation Index ◽  
Environmental Modeling ◽  
Geographical Information ◽  
Rice Grain ◽  
Landsat Satellite ◽  
Normalized Difference Index ◽  
The Impact

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.

scholarly journals Activation of the Sarychev Peak volcano in 2020–2021 (Matua Isl., the Central Kuril Islands)

2021 ◽  
Vol 5 (2) ◽  
pp. 167-171
Author(s):  
A.V. Degterev ◽  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Kuril Islands ◽  
Volcanic Cone ◽  
Effusive Eruption ◽  
The North ◽  
Flow Length ◽  
Central Kuril Islands ◽  
Sarychev Peak

This publication, based on remote sensing data, examines the features of the effusive eruption of the Sarychev Peak volcano (Matua Isl., the Central Kuril Islands), which took place from December 2020 till February 2021. On the basis of the analysis of the Sentinel satellite data, it was established that starting from December 2020, the crater of the Sarychev Peak volcano began to fill with lava. As of January 18, 2021, it was completely filled, then lava outpouring through a fissure in the north-northwest part began. A lava flow (length 2 km, width 80–90 m) descended along the bottom of the valley, which cuts the northwestern slope of the volcanic cone. The outpouring of lava was completed by February 7, 2021. The effusive eruption of the Sarychev Peak volcano in 2020–2021 is atypical for the modern stage of eruptive history, characterized mainly by explosive and explosive-effusive type of eruptions.

scholarly journals Yield variability prediction by remote sensing sensors with different spatial resolution

2017 ◽  
Vol 31 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Jitka Kumhálová ◽  
Štěpánka Matějková
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Data Availability ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Very High

Abstract Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

scholarly journals MAPPING AND INDEX VEGETATION ANALYSES OF MANGROVE IN SAPARUA ISLAND, CENTRAL MOLUCCAS

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Harold J. D. Waas ◽  
Bisman Nababan
Keyword(s):  
Satellite Data ◽  
Coastal Areas ◽  
High Density ◽  
Island Area ◽  
The North ◽  
Vegetation Indexes ◽  
Mangrove Vegetation ◽  
Landsat 7 ◽  
Landsat Satellite ◽  
Very High

<p>2TMapping and index vegetation analyses of mangrove in coastal areas of Saparua Island, Central Moluccas was conducted using Landsat 7/ETM+ satellite data acquired in April to May 2007. The results showed that the distributions of mangrove vegetation were concentrated in the north, south, and west of the region with the area of 218.88 ha (38.26%), 105.12 ha (18.38%), and 248.04 ha (43.36%), respectively. Total area of mangrove vegetation in this island was about 572.04 ha (5.72 kmP2P), or 3.49% of the island area. Vegetation indexes (NDVI) in the north, south, and west of the region were dominated by values of &gt;0.7 (very high density).</p><p>Keyword: Mangrove, NDVI, Landsat Satellite, Saparua, Central Maluku</p>

Distributed access and search for the satellite data of remote sensing

2007 ◽  
Vol 13 (1s) ◽  
pp. 80-85
Author(s):  
E.B. Kudashev ◽  
◽  
A.N. Filonov ◽  
Keyword(s):  
Remote Sensing ◽  
Satellite Data

Evaluation of Capacity of Salandi Reservoir Using Remote Sensing Data Along With Qgis Software

2018 ◽  
Vol 6 (8) ◽  
pp. 339
Author(s):  
Rupali Dhal ◽  
D. P. Satapathy
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Project Work ◽  
Space Technology ◽  
Reservoir Water ◽  
Sediment Distribution ◽  
Sensing Data ◽  
Spread Area ◽  
Water Spread

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.

Sign in / Sign up

Export Citation Format

Share Document