Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

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.

Download Full-text

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

Sustainability ◽

10.3390/su13095274 ◽

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.

Download Full-text

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

10.5194/se-2016-31 ◽

2016 ◽

Cited By ~ 1

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.

Download Full-text

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

10.5194/egusphere-egu2020-11855 ◽

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

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.To achieve our goal, we used two types of data: remote sensing and ground truth data.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 &#160;using vegetation index NDVI.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.Ground truth data were collected in 2019 during the dry growing season (January &#8211; 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.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.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.

Download Full-text

Quality of Digital Elevation Models obtained from Unmanned Aerial Vehicles for Precision Viticulture

OENO One ◽

10.20870/oeno-one.2016.50.4.1177 ◽

2016 ◽

Vol 50 (3) ◽

Author(s):

Léo Pichon ◽

Arnaud Ducanchez ◽

Hélène Fonta ◽

Bruno Tisseyre

Keyword(s):

Low Cost ◽

Wine Industry ◽

Image Capture ◽

Digital Elevation ◽

Elevation Data ◽

Aerial Vehicle ◽

Elevation Model ◽

Uav Images ◽

Data Acquisition And Processing

Aims: This work aims to study the quality of low cost Digital Surface Models (DSMs) obtained with Unmanned Aerial Vehicle (UAV) images and to test whether these DSMs meet common requirements of the wine industry.Methods and results: Experiments were carried out on a 4-ha vineyard located 10 km north of Beziers (France). The experimental site presents slope and aspect variations representative of mechanised commercial vineyards in Languedoc Roussillon. DSMs were provided by three UAV companies selected for the diversity of their solutions in terms of image capture altitude, type of UAV and image processing software. DSMs were obtained by photogrammetry and correspond to commercial products usually delivered by UAV companies. DSMs from UAV were compared to a reference Digital Elevation Model (DEM) acquired by a laser tachymeter. Four indicators were used to test the quality of DSMs: the mean error and its dispersion in the XY plane and in elevation Z. Results show a good georeferencing of the DSMs (MeanErrorXY<10 cm) and a similar quality in elevation (MeanErrorZ<10 cm) estimation. Results also show that the error in elevation is highly spatially structured. The spatial patterns observed did not depend on the elevation and could be related to algorithms used to compute the DSMs.Conclusion: Data acquisition and processing methods have an impact on the quality of the DSMs provided by the UAV companies. DSM qualities are good enough to meet commercial vineyard requirements. The tested DSMs fit the requirements to assess field characteristics (elevation, slope, aspects) which may be important for terroir characterisation purposes.Significance and impact of the study: This study proves that elevation data derived from UAV present an accuracy equivalent to the reference system used in this study. The rapidity, the low cost and the high spatial resolution of these data offer significant opportunities for the development of new services for the wine industry for field characterisation.

Download Full-text

Beyond 2D inventories : synoptic 3D landslide volume calculation from repeat LiDAR data

10.5194/esurf-2020-73 ◽

2020 ◽

Cited By ~ 1

Author(s):

Thomas G. Bernard ◽

Dimitri Lague ◽

Philippe Steer

Keyword(s):

Point Cloud ◽

Volume Estimation ◽

Airborne Lidar ◽

Aerial Images ◽

3D Point Cloud ◽

Volume Calculation ◽

Landslide Volume ◽

Digital Elevation ◽

Multi Temporal ◽

Areal Mapping

Abstract. Efficient and robust landslide mapping and volume estimation is essential to rapidly infer landslide spatial distribution, to quantify the role of triggering events on landscape changes and to assess direct and secondary landslide-related geomorphic hazards. Many efforts have been made during the last decades to develop landslide areal mapping methods, based on 2D satellite or aerial images, and to constrain empirical volume-area (V-A) allowing in turn to offer indirect estimates of landslide volume. Despite these efforts, some major issues remain including the uncertainty of the V-A scaling, landslide amalgamation and the under-detection of reactivated landslides. To address these issues, we propose a new semi-automatic 3D point cloud differencing method to detect geomorphic changes, obtain robust landslide inventories and directly measure the volume and geometric properties of landslides. This method is based on the M3C2 algorithm and was applied to a multi-temporal airborne LiDAR dataset of the Kaikoura region, New Zealand, following the Mw 7.8 earthquake of 14 November 2016. We demonstrate that 3D point cloud differencing offers a greater sensitivity to detect small changes than a classical difference of DEMs (digital elevation models). In a small 5 km2 area, prone to landslide reactivation and amalgamation, where a previous study identified 27 landslides, our method is able to detect 1431 landslide sources and 853 deposits with a total volume of 908,055 ± 215,640 m3 and 1,008,626 ± 172,745 m3, respectively. This high number of landslides is set by the ability of our method to detect subtle changes and therefore small landslides with a carefully constrained lower limit of 20 m2 (90 % with A

Download Full-text

A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

Journal of Applied and Natural Science ◽

10.31018/jans.v12i1.2209 ◽

2020 ◽

Vol 12 (1) ◽

pp. 9-12

Author(s):

Arjun G. Koppad ◽

Syeda Sarfin ◽

Anup Kumar Das

Keyword(s):

Land Use ◽

Land Cover ◽

Accuracy Assessment ◽

Ground Truth ◽

Land Cover Classification ◽

Data Sets ◽

Radiometric Calibration ◽

Ground Truth Data ◽

Dense Forest ◽

Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha). Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76. Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

Download Full-text

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

Journal of Forest Planning ◽

10.20659/jfp.18.1_77 ◽

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

Download Full-text

Applying remote sensing techniques to monitor green areas in Tashkent Uzbekistan

E3S Web of Conferences ◽

10.1051/e3sconf/202125804012 ◽

2021 ◽

Vol 258 ◽

pp. 04012

Author(s):

Ilhomjon Aslanov ◽

Uzbekkhon Mukhtorov ◽

Rahimjon Mahsudov ◽

Umida Makhmudova ◽

Saida Alimova ◽

...

Keyword(s):

Accuracy Assessment ◽

Ground Truth ◽

Population Increase ◽

Visual Interpretation ◽

Ground Truth Data ◽

Capital Cities ◽

Green Area ◽

Urban Green Area ◽

Green Areas ◽

Remote Sensing Techniques

Land use and land cover (LULC) change are one of the most important signals of regional environmental monitoring and study. Recently, the pull of capital cities has snowballed, an increasing number of people moving to the cities, especially in developing countries. Consequently, as more people arrive at cities, the more pressure will be on land. Land price getting high and constructions try using open green areas. A wide variety of green areas of different sizes will be solve many urban diseases and ecological problems at the same time improve the quality and life of urban residents, as urban green area provides various ecosystem services. The green area includes parks, woodlands, nature reserves and bare lands. With the population increase and expansion of cities, an increasing amount of open area, woodland and bare land has been converted into construction land, buildings due to the increasing demands and residential land. For the accuracy assessment, we applied an automatically supervised classification using the software QGIS 3.18. The reference values were based on ground truth data and visual interpretation.

Download Full-text

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Remote Sensing ◽

10.3390/rs12071099 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1099 ◽

Cited By ~ 2

Author(s):

Ahram Song ◽

Yongil Kim

Keyword(s):

Unmanned Aerial Vehicle ◽

Three Dimensional ◽

Ground Truth ◽

Training Data ◽

Fine Tuning ◽

Target Domain ◽

Ground Truth Data ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Aerial Vehicle

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

Download Full-text

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

Remote Sensing ◽

10.3390/rs12183032 ◽

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.

Download Full-text