scholarly journals Comparison of Remote Sensing based Multi-Source ET Models over Cropland in a Semi-Humid Region of China

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 325
Author(s):  
Qifeng Zhuang ◽  
Hao Wang ◽  
Yuqi Xu
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Moisture Stress ◽  
Balance Model ◽  
Maturation Stage ◽  
Growth Stages ◽  
Landsat 8 ◽  
Humid Region ◽  
Crop Types

The estimation of cropland evapotranspiration (ET) is essential for agriculture water management, drought monitoring, and yield forecast. Remote sensing-based multi-source ET models have been widely applied and validated in the semi-arid region of China. However, careful investigation of the models’ performances for different crop types (winter wheat and summer maize) over the semi-humid region is still necessary. This study used remote sensing data (Landsat 8 and ASTER) and compared three mainstream multi-source ET models: (i) the two-source energy balance model, i.e., TSEB; (ii) the Penman-Monteith based four-source model, i.e., 4s-PM; (iii) the Priestley Taylor-Jet Propulsion Laboratory ET algorithm, i.e., PT-JPL. The measurements of the eddy-covariance (EC) flux tower located in Guantao county of North China were used to validate the models. The results showed that the TSEB model performed the best in estimating latent heat flux (LE) of maize, with an RMSE of 75.0 W/m2 and an R2 of 0.9, and the 4s-PM model had the highest accuracy of LE estimation for wheat, with an RMSE of 61.0 W/m2 and an R2 of 0.91. The LE spatial distribution comparison indicated that the PT-JPL model had more capacity to exhibit crop ET heterogeneity. The major environmental factors affecting ET varied with crop types and crop growth stages. Without taking soil moisture into account, the 4s-PM and TSEB models overestimated LE under water deficit in the maturation stage of wheat. The plant moisture stress based on vegetation index in the PT-JPL model underestimated the evaporation in the maturation stage while the cropland was still wet.

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.

Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

2022 ◽  
Vol 88 (1) ◽  
pp. 47-53
Author(s):  
Muhammad Nasar Ahmad ◽  
Zhenfeng Shao ◽  
Orhan Altan
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Abrupt Decrease ◽  
Computing Platform ◽  
Remote Sensing Techniques ◽  
Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

scholarly journals IDENTIFIKASI STRUKTUR GEOLOGI DAN PENGARUHNYA TERHADAP SUHU PERMUKAAN TANAH BERDASARKAN DATA LANDSAT 8 DI LAPANGAN PANASBUMI BLAWAN

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Anjar Pranggawan Azhari ◽  
Sukir Maryanto ◽  
Arief Rachmansyah
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hot Springs ◽  
Remote Sensing Data ◽  
Geological Structure ◽  
Geothermal Field ◽  
Geothermal System ◽  
Landsat 8

This paper presented used remote sensing method for identification geological structure on Blawan-Ijengeothermal field and its system. Remote sensing data, specifically Landsat 8 and DEM SRTM, provide lineaments from the 753 multispectral band and the land surface temperature (LST) from single thermal infra red band using a retrieval method. Surface emissivity was determined based on Normalized Difference Vegetation Index (NDVI) of study area. Remote sensing analysis is good approach to identification of geological structure from surface that control thermal manifestation in Blawan geothermal field. It shows Blawan fault is the main structure in geothermal field which associated with high LST and hot springs. Interpretation indicated reservoir of Blawan-Ijen geothermal system spread from Plalangan to southwest area. Abstrak Penelitian ini bertujuan untuk mengidentifikasi struktur geologi dan gambaran sistem panasbumi Blawan-Ijen dengan aplikasi penginderaan jauh. Data penginderaan jauh khususnya citra multispektral komposit 753 Landsat 8 dan DEM SRTM digunakan sebagai data untuk mendelineasi struktur patahan di permukaan. Suhu permukaan tanah diperoleh dari pengolahan citra thermal inframerah Landsat 8 dengan bantuan metode semi empiris. Emisivitas permukaan diperoleh berdasarkan klasifikasi indeks vegetasi NDVI daerah penelitian. Analisis data penginderaan jauh merupakan pendekatan yang cukup baik dalam mengidentifikasi struktur geologi yang mengontrol manifestasi panasbumi Blawan. Hasil interpretasi menunjukkan patahan Blawan adalah struktur utama di daerah geothermal Blawan yang berasosiasi dengan suhu permukaan tanah yang tinggi dan deretan mata air panas. Interpretasi mengindikasikan reservoir sistem panasbumi Blawan berada di bawah permukaan Plalangan dan menerus dari Plalangan menuju arah barat daya daerah penelitian.

scholarly journals Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2363
Author(s):  
Javier Campos ◽  
Francisco García-Ruíz ◽  
Emilio Gil
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Growth Stages ◽  
Application Process ◽  
Vegetation Growth ◽  
Wall Area ◽  
Volume Rate ◽  
Point Data

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.

scholarly journals Assessing Vegetation Decline due to Pollution from Waste Cycle Activities by a Multitemporal Remote-Sensing Approach

2021 ◽  
Author(s):  
Giuseppe Mancino ◽  
Rodolfo Console ◽  
Michele Greco ◽  
Chiara Iacovino ◽  
Maria Lucia Trivigno ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Kendall Test ◽  
Landsat 8 ◽  
Polluted Sites ◽  
Exogenous Factors ◽  
Multitemporal Remote Sensing

The work consisted in identifying possible effects from heavy metals (HMs) pollution due to waste disposal activities in three potentially polluted sites located in Basilicata (Italy), where a release of pollutants with values over the thresholds allowed by the Italian legislation was detected. The potential variations in the physiological efficiency of vegetation have been analyzed through the multitemporal processing of satellite images. In detail, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the Normalized Difference Vegetation Index (NDVI) trend over the years. Then, the multitemporal trends were analyzed using the median of Theil-Sen, a non-parametric estimator particularly suitable for the treatment of remote sensing data, being able to minimize the outlier effects due to exogenous factors. Finally, the subsequent application of the Mann-Kendall test on the trends identified by Theil-Sen slope allowed the evaluation of trends significance and, therefore, the areas characterized by the effects of contamination on vegetation. The application of the procedure to the three survey sites led to the exclusion of the presence of significant effects of HMs contamination on the vegetation surrounding the sites during the years of waste disposal activities.

scholarly journals The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

2021 ◽  
Vol 873 (1) ◽  
pp. 012015
Author(s):  
Zahrah Athirah ◽  
Muhammad Dhery Mahendra
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Volcanic Complex ◽  
Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

scholarly journals Using Landsat 8 Images Along with Google Earth Engine for Investigation the Location of the Crashed Aircrafts

2018 ◽  
Author(s):  
Azad Rasul
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat Images ◽  
Green Area ◽  
Google Earth Engine ◽  
Index Value ◽  
Research Period

Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.

scholarly journals Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

2021 ◽  
Vol 24 (3) ◽  
pp. 393-401
Author(s):  
Tengku Zia Ulqodry ◽  
Andreas Eko Aprianto ◽  
Andi Agussalim ◽  
Riris Aryawati ◽  
Afan Absori
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Area Index ◽  
Sensing Data ◽  
Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

scholarly journals A Preliminary Study on Heavy Oil Location in Central Sumatra using Remote Sensing and Geographic Information Sytem

2021 ◽  
Vol 44 (1) ◽  
pp. 39-54
Author(s):  
Suliantara Suliantara ◽  
Tri Muji Susantoro ◽  
Herru Lastiadi Setiawan ◽  
Nurus Firdaus
Keyword(s):  
Remote Sensing ◽  
Heavy Oil ◽  
Vegetation Index ◽  
Oil And Gas ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Landsat 8 ◽  
Sensing Data ◽  
Central Sumatra

Heavy oil which is classifi ed as non conventional oil is the target of exploration in the world. In Indonesia, the potential for heavy oil exploration is quite large, especially in the Central Sumatra basin. This study aims to map the location of potential heavy oil based on remote sensing data and regional gravity data supported by a geographic information system. Landsat 8 OLI satellite data is processed to produce 567 (RGB) color composite images, then further processing is carried out with DEM data to produce fusion images; mapping the vegetation index, clay mineral index, iron oxide index, surface temperature. The gravity data is used for mapping subsurface geological structures. Overlay analysis is carried out on the results of remote sensing data processing and interpretation of surface and subsurface geology. Based on the analysis, it shows that heavy oil fi elds are generally found on the surface and subsurface structures which are relatively identical and located on the edge of the basement high. Based on this analysis, the locations that have the potential for heavy oil and gas traps are on the northeast edge, Dalu-dalu High, the edge of Kampar High, the west edge of Kuantan High, the southwest edge of the Beruk High, the southwest edge of the Sembilan High.

scholarly journals Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Environments ◽  
2019 ◽  
Vol 6 (7) ◽  
pp. 85 ◽  
Author(s):  
Cesar I. Alvarez-Mendoza ◽  
Ana Claudia Teodoro ◽  
Nelly Torres ◽  
Valeria Vivanco
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Pm10 Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Terra Modis

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Sign in / Sign up

Export Citation Format

Share Document