scholarly journals Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

2021 ◽  
Vol 13 (9) ◽  
pp. 5274
Author(s):  
Xinyang Yu ◽  
Younggu Her ◽  
Xicun Zhu ◽  
Changhe Lu ◽  
Xuefei Li
Keyword(s):  
Arable Land ◽  
Ground Truth ◽  
Northwest China ◽  
Hexi Corridor ◽  
Ground Truth Data ◽  
Land Protection ◽  
Promising Tool ◽  
Study Results ◽  
Multi Temporal ◽  
The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

scholarly journals Sentinel 1A SAR Backscattering Signature of Maize and Cotton Crops

2017 ◽  
Vol 104 (.1-.4) ◽  
Author(s):  
Kumaraperumal R ◽  
◽  
Shama M ◽  
Balaji Kannan ◽  
Ragunath K P ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Ground Truth ◽  
Backscattering Coefficient ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
The Mean ◽  
Sar Data ◽  
Remote Sensing Techniques ◽  
Crop Discrimination

Crop discrimination is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data are advantageous for crop monitoring and classification because of their all-weather imaging capabilities. The multi-temporal Sentinel 1A SAR data was acquired from 08th August, 2015 to 23rd January, 2016 at 12 days interval covering the extent of Perambalur district of Tamil Nadu. Both the Vertical - Vertical (VV) and Vertical-Horizontal (VH) polarized data are compared. The ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. The temporal backscattering coefficient (σ0 ) for cotton and maize are extracted using the training datasets. The mean backscattering values for cotton during the entire cropping period ranges from -10.58 dB to -6.28 dB and -20.59 dB to -14.53 dB for VV and VH polarized data respectively, and for maize it ranges from -11.08 dB to -7.07 dB and -19.85 dB to -14.14 dB for VV and VH polarized data respectively.

scholarly journals Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1907 ◽  
Author(s):  
Luís Pádua ◽  
Pedro Marques ◽  
Jonáš Hruška ◽  
Telmo Adão ◽  
Emanuel Peres ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Low Cost ◽  
Ground Truth ◽  
Volume Estimation ◽  
Height Range ◽  
Ground Truth Data ◽  
Operational Technique ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

scholarly journals Vegetation Cover Change Detection and Assessment in Arid Environment Using Multi-temporal Remote Sensing images and Ecosystem Management Approach

2016 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa
Keyword(s):  
Remote Sensing ◽  
Ecosystem Management ◽  
Vegetation Cover ◽  
Secondary Data ◽  
Ground Truth ◽  
Arid Environment ◽  
Management Approach ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Multi Temporal

Abstract. Vegetation cover (VC) changes detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the centre of Saudi Arabia. Characteristics and dynamics of VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images; Landsat4 TM 1987, Landsat7 ETM+ 2000, and Landsat8 2013. The VC pattern and changes were linked to both natural and social processes to investigate the drivers responsible for the change. The analyses of the three satellite images concluded that the surface area of the VC increased by 107.4 % between 1987 and 2000, it was decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment; while the south-western part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.

Soil salinity assessment using temporal series of Sentinel2 satellite images in irrigated paddy fields of Western Africa

2020 ◽  
Author(s):  
Moussa Issaka ◽  
Walter Christian ◽  
Michot Didier ◽  
Pichelin Pascal ◽  
Nicolas Hervé ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Ground Truth ◽  
Growing Season ◽  
Bare Soil ◽  
Atmospheric Effects ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Salinity Index ◽  
Niger River

<p>Salinization and alkalinization are worldwide among the soil degradation threats in irrigated schemes affecting soil productivity. Niger River basin irrigated schemes in the Sahel arid zone are no exception (ONAHA, 2011). The use of remote sensing for identifying and evaluating the level of these phenomena is an interesting tool. The launching of the Sentinel2 satellite constellation (2015) brings new perspectives with high spectral and temporal resolutions images. The aim of this study was to develop a methodology for detection of salt-affected soils in this climatic condition.</p><p>To achieve our goal, we used two types of data: remote sensing and ground truth data.</p><p>Two complementary approaches were used: one by observing salinity on bare soil by the use of salinity index (SI) and the other by observing the indirect effects of salinity on the vegetation during eight (8) rice growth phases  using vegetation index NDVI.</p><p>Remote sensing data were acquired from multi temporal sentinel2 images over 4 years (from 11/12/2015 to 30/11/2019). One hundred and fifty seven (157) images were downloaded (one image each 5 days) and corrected from atmospheric effects and some bands resampled to 5 m using python software. The salinity and vegetation indices were calculated. NDVI index was calculated and NDVI integral between NDVI curve and the threshold of 0.21 NDVI calculated for the eight growing cycles.</p><p>Ground truth data were collected in 2019 during the dry growing season (January – may 2019) from 24 calibration plots and 40 validation plots. One hundred and twenty (120) soil samples collected and analyzed for pH and electrical conductivity and finally forty six (46) biomass samples were collected, air dried and weighed for biomass yield and 46 grains samples collected for grain yield.</p><p>NDVI integral proved to be good indicator for yield variations and could distinguish crops behavior according to the growing period. It also makes it possible to distinguish plots which were not cultivated or with weak growth due to strong constraints of which the main one is salinity. It showed also that the effect of salinity on growth differs according to the growing season and the possibility of managing irrigation. Bare soil analysis distinguishes fields with different salinity indexes despite the low number of dates for which bare soil can be observed.</p><p>Ascending Hierarchical Classification (AHC) enabled to identify four classes of NDVI dynamics over time and bare soil salinity index. High saline soils according to direct soil measurements were related to the class characterized by high frequency of no-cultivation during the dry season and low NDVI integral during the wet season. Multi-temporal Sentinel2 images analysis enabled therefore to detect rice crop fields affected by salinity through its influence on crop behavior. This approach will be tested over the whole paddy schemes of the Niger River valley.</p>

Soil texture controls vegetation biomass and organic carbon storage in arid desert grassland in the middle of Hexi Corridor region in Northwest China

Soil Research ◽  
2015 ◽  
Vol 53 (4) ◽  
pp. 366 ◽  
Author(s):  
Yongzhong Su ◽  
Jiuqiang Wang ◽  
Rong Yang ◽  
Xiao Yang ◽  
Guiping Fan
Keyword(s):  
Organic Carbon ◽  
Soil Texture ◽  
Clay Content ◽  
Northwest China ◽  
Hexi Corridor ◽  
Desert Grassland ◽  
Vegetation Biomass ◽  
Below Ground ◽  
The Mean ◽  
Arid Desert

Soil texture plays an important role in controlling vegetation production and soil organic carbon (SOC) concentration in arid desert grassland ecosystems. However, little is known about the occurrence and extent of these textural effects in the arid desert grasslands of Northwest China. This study used 160 soil profiles taken from 32 desert grassland sites in similar topographical units (alluvial–diluvial fans) in the middle of Hexi Corridor region of Northwest China to investigate vegetation biomass, SOC storage, and soil texture of seven layers in the top 100 cm of soil. The mean aboveground biomass, below-ground biomass, and total biomass in arid desert grassland were 155.3, 95.3, and 256.3 g m–2, respectively. More than 95% of the below-ground biomass was distributed in the top 30 cm of soil. Spatially, vegetation biomass was positively related to soil clay content and silt + clay content. The mean SOC density in the top 100 cm was 2.94 kg m–2 and ~46.8% of the storage was concentrated in the top 30 cm. SOC concentrations and stocks were positively and significantly related to clay content and silt + clay content in the seven soil layers sampled from the top 100 cm. The soil silt + clay content explained 42–79% of the variation in SOC stocks in the different soil depths. In conclusion, soil texture appears to be an important control on vegetation productivity and SOC capacity in arid Hexi Corridor desert grassland soils.

scholarly journals Simple Method for Land-Cover Mapping by Combining Multi-Temporal Landsat ETM+ Images and Systematically Sampled Ground Truth Data : A Case Study in Japan

2012 ◽  
Vol 18 (1) ◽  
pp. 77-85
Author(s):  
Shinya Tanaka ◽  
Tomoaki Takahashi ◽  
Hideki Saito ◽  
Yoshio Awaya ◽  
Toshiro Iehara ◽  
...  
Keyword(s):  
Land Cover ◽  
Ground Truth ◽  
Land Cover Mapping ◽  
Simple Method ◽  
Ground Truth Data ◽  
Multi Temporal

scholarly journals Monitoring of Chestnut Trees Using Machine Learning Techniques Applied to UAV-Based Multispectral Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3032
Author(s):  
Luís Pádua ◽  
Pedro Marques ◽  
Luís Martins ◽  
António Sousa ◽  
Emanuel Peres ◽  
...  
Keyword(s):  
Management Practices ◽  
Abiotic Factors ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Temporal Analysis ◽  
Machine Learning Techniques ◽  
Ground Truth Data ◽  
Multispectral Data ◽  
Spectral Bands ◽  
Multi Temporal

Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.

scholarly journals Forest and Industrial Plantation Mapping with Multi-Temporal Dual Polarization by ALOS PALSAR Data In Riau Province

2018 ◽  
Vol 2 (1) ◽  
pp. 35
Author(s):  
Elis Molidena ◽  
Takahiro Osawa ◽  
Putu Gede Ardhana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Land Use ◽  
Ground Truth ◽  
Temporal Analysis ◽  
Alos Palsar ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Industrial Plantation ◽  
Changes Detection ◽  
Accuracy Result ◽  
Color Composite

Backscattering characteristics of land use has been analyzed using ALOS PALSAR data. The purpose of this research are mapping of land use by five categories such as forest, acacia, oil palm, open area and water, and to identify the changes of environmental. Analysis Pixel-by-pixel average of ALOS PALSAR level 1.5 backscattering used from five of category land use was to estimate the spectral characteristic of each object in difference HH and HV polarization. Ground truth data was taken from 169 locations which used for classification, 119 locations and 50 locations used for validation. Two different times of ALOS PALSAR level 1.0 2009 and 2010 data, was used for changes detection by multi temporal color composite combination. The accuracy result for classification map shows 62% of ground truth database, and multi temporal analysis showed the possibility of changes.

scholarly journals Methods for Earth-Observing Satellite Surface Reflectance Validation

2019 ◽  
Vol 11 (13) ◽  
pp. 1543
Author(s):  
Badawi ◽  
Helder ◽  
Leigh ◽  
Jing
Keyword(s):  
Solar Spectrum ◽  
Ground Truth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Initial Validation ◽  
Reliable Performance ◽  
The Mean ◽  
The U.S

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets.

scholarly journals The application of FORMOSAT-2 high-temporal- and high-spatial resolution imagery for monitoring open straw burning and carbon emission detection

2013 ◽  
Vol 13 (3) ◽  
pp. 575-582 ◽  
Author(s):  
C.-C. Liu ◽  
P.-Y. Tseng ◽  
C.-Y. Chen
Keyword(s):  
Carbon Emissions ◽  
Supervised Classification ◽  
Carbon Emission ◽  
Global Climate ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Study Results ◽  
Quantitative Results ◽  
Straw Burning ◽  
Emission Detection

Abstract. Rice is produced in more than 95 countries worldwide and is a staple food for over half of the world's population. Rice is also a major food crop of Taiwan. There are numerous rice crops planted on the western plains of Taiwan, and, after the harvest season, the left-over straw is often burned on-site. The air pollutants from the burning emissions include CO2, CO, CH4 and other suspended particles, most of these being the greenhouse gases which cause global climate change. In this study FORMOSAT-2 satellite images and ground-truth data from 2008 and 2009 are used to conduct supervised classification and calculate the extent of the straw burning areas. It was found that 10% of the paddies in the study area were burned after harvest during this 2-yr period. On this pro rata basis, we calculated the overall carbon emissions from the burning of the straw. The findings showed that these few farmers produced up to 34 000 tons of carbon emissions in 2008, and 40 000 tons in 2009. The study results indicate that remotely sensed images can be used to efficiently evaluate the important characteristics for carbon emission detection. It also provides quantitative results that are relevant to tracking sources of transport pollution, postharvest burning, and Asian dust in Taiwan.

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