scholarly journals Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 309 ◽  
Author(s):  
Isaac Kyere ◽  
Thomas Astor ◽  
Rüdiger Graß ◽  
Michael Wachendorf
Keyword(s):  
Agricultural Land ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Type Model ◽  
Periodic Field ◽  
Ground Truth Data ◽  
Agricultural Planning ◽  
Crop Type ◽  
Multi Temporal

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

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 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 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.

General Discussion

1983 ◽  
Vol 309 (1508) ◽  
pp. 283-284
Keyword(s):  
Satellite Imagery ◽  
Landsat Imagery ◽  
Ground Truth ◽  
Remotely Sensed ◽  
Individual Plant ◽  
Ground Truth Data ◽  
Remotely Sensed Imagery ◽  
Effective Evaluation ◽  
Geographical Environment ◽  
Multispectral Satellite Imagery

Monica M. Cole (Bedford College, London, U. K.). In contributing to a discussion of the use of multispectral satellite imagery in the exploration for petroleum and minerals covered by Mr Peters I wish to emphasize four points, some of which are relevant also to statements made by Dr Curran in his presentation. The first point is that remotely sensed imagery is a tool and its interpretation a technique to be used as appropriate and integrated with other techniques in mineral exploration. Mr Peters has reviewed the potential of multispectral satellite imagery and emphasized its value in initial reconnaissance studies notably for the identification of geological structures and lithologies. I would emphasize also its value at more advanced stages of exploration when reinterpretation of imagery at large scales and with reference to ground truth data can yield valuable information. My second point, which follows naturally from the first, is that effective interpretation of remotely sensed imagery requires an appreciation of the geographical environment as well as the geological environment. It is reflectances from the components of the geographical environment that produce the colours and tones seen on the colour composites generated from Landsat imagery. Except in arid areas largely devoid of plant cover, in natural terrain reflectances from vegetation dominate over those from soils and bedrock. Their contribution increases with increasing density of cover. The reflectances from different types of vegetation and from individual plant species, however, vary greatly, depending on the geometry of the canopy, the colour of foliage, the size, shape, angle, etc., of leaves, and the turgidity, water content and nutrient status of leaf cells. It is the differences in vegetation cover producing differing reflectances that permit the discrimination of lithologies and identification of structures on colour composites generated from Landsat imagery. In some areas, however, any or all of relict laterite, superficial cover, former and ephemeral drainage systems, and other physiographic features that are the legacies of geomorphological processes, complicate relations. These need to be understood for effective evaluation of imagery for geological purposes. In this context there is no substitute for field investigations, which are essential for the acquisition of ground truth data needed for effective evaluation of imagery.

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>

scholarly journals UAV-Based Mapping of Banana Land Area for Village-Level Decision-Support in Rwanda

2021 ◽  
Vol 13 (24) ◽  
pp. 4985
Author(s):  
Regina Kilwenge ◽  
Julius Adewopo ◽  
Zhanli Sun ◽  
Marc Schut
Keyword(s):  
Decision Support ◽  
Crop Production ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Support Vector ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Landcover Classification ◽  
Optimal Productivity ◽  
Data Richness

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.

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 Multi-temporal analysis of vegetation reflectance using MERIS data in the Czech Republic

2014 ◽  
Vol 18 (2) ◽  
pp. 30-34
Author(s):  
Přemysl Štych ◽  
Lucie Malíková ◽  
Jan Kříž ◽  
Lukáš Holman
Keyword(s):  
Czech Republic ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Temporal Analysis ◽  
High Temporal Resolution ◽  
The Czech Republic ◽  
Multi Temporal ◽  
Dynamic Landscape ◽  
Resolution Data

Abstract Accurate high temporal resolution data is a very important source of information for understanding processes in the landscape. High temporal and spectral resolution data enable the monitoring of dynamic landscape processes. For this reason, since 2008 a receiving station for Metosat, NOAA and Envisat data has been installed at the Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague. The aim of this study is to analyse the spectral characteristics of vegetation using MERIS data in the Czech Republic. Spectral characteristics of vegetation were examined both by analysing changes in reflectivity as well as by utilising vegetation indices. Vegetation in forests and agricultural land was evaluated. The results present the spectral characteristics of selected associations of vegetation based on MERIS data and a discussion of the methods of multitemporal classification of land cover.

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.

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