scholarly journals Monitoring of Spectral Signatures of Maize Crop using Temporal SAR and Optical Remote Sensing data

2021 ◽  
Vol 12 (6) ◽  
pp. 745-750
Author(s):  
D. Anil Kumar ◽  
◽  
P. Srikanth ◽  
T. L. Neelima ◽  
M. Uma Devi ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Plant Density ◽  
Optical Data ◽  
Maturity Stage ◽  
Optical Remote Sensing ◽  
Maize Crop ◽  
Ground Truth Data ◽  
Backscatter Intensity ◽  
Multi Temporal ◽  
Sentinel 2A

A study was carried out using the temporal Sentinel-1B microwave data (June to November at 12 days interval) and Sentinel-2A/2B optical data (June to November) to discriminate the maize crop from other competing crops rice and cotton in Siddipet district, Telangana state, India during kharif, 2019 (June to November). The study utilized the data from multiple sources such as Multi-temporal VH backscatter intensity from Sentinel-1B SAR and NDVI values from Sentinel-2A/2B in combination with field data to discriminate the maize crop. Synchronous to satellite pass, ground truth data on crop parameters viz., crop stage, crop vigour, biomass, plant height, plant density, soil moisture, LAI and chlorophyll content were collected. Multi-temporal VH backscatter intensity and Normalized Difference Vegetation Index (NDVI) data were used to characterize backscatter and greenness behaviour of the maize crop. The backscatter intensity (dB) for maize crop ranged from -21.83 (the lowest backscatter values) at planting to -12.52 (the highest backscatter values) at peak growth stage. The NDVI values during vegetative and reproductive stages (August and September) were >0.6 and during senescence to harvesting the values were less than or equal to 0.52. The increase in backscatter intensity values from initial vegetative stage to peak stage was due to increased volume scattering of the maize crop canopy and a continuous decline in backscatter intensity values of VH band at maturity stage, was due to decrease in greenness and moisture content in leaves of the maize crop helped in maize crop discrimination from other dominant kharif crops in the study area.

scholarly journals MULTI-TEMPORAL FEATURE EXTRACTION FOR PRECISE MAIZE AREA MAPPING USING TIME-SERIES SENTINEL 1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 169-173
Author(s):  
M. Venkatesan ◽  
S. Pazhanivelan ◽  
N. S. Sudarmanian
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Growth Period ◽  
Maturity Stage ◽  
Kappa Score ◽  
Maximum Increase ◽  
Maize Crop ◽  
Temporal Features ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. Multi-temporal Sentinel 1A GRD data at VV and VH polarizations and SLC products were acquired for the study area at 12 days interval and processed using MAPscape-RICE software. Multi-temporal Sentinel 1A data was used to identify the backscattering dB curve of maize crop. Analysis of temporal signatures of the crop showed minimum values at sowing period and maximum during the tasseling stage, which decreased during maturity stage of the crop. The maximum increase in the signature was observed during seedling to vegetative growth period. The signature derived from dB values for maize crop expressed a significant temporal behavior with the range of &amp;minus;21.26 to &amp;minus;13.18 in VH polarization and &amp;minus;14.05 to &amp;minus;6.54 in VV polarization. Considering the accuracy of SAR data to phenological variations of maize growing period, Multi-Temporal Features were extracted from multi-temporal dB images of VV and VH polarization and coherence images. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations of Sentinel 1A GRD and SLC data to classify maize pixels in the study area using parameterized classification approach. The overall classification accuracy was 91 percent with the kappa score of 0.82.</p>

scholarly journals Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands

2021 ◽  
Vol 227 ◽  
pp. 03001
Author(s):  
Zokhid Mamatkulov ◽  
Eshkobil Safarov ◽  
Rustam Oymatov ◽  
Ilhom Abdurahmanov ◽  
Maksud Rajapbaev
Keyword(s):  
Real Time ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Vegetation Development ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
Gis And Rs ◽  
Yield Forecasting ◽  
Cotton Fields

Badland reclamation and low productive farmlands always have been one of the most detrimental effects on the national economy, typically in agricultural sector of Uzbekistan. Nonetheless, such kind of lands has been used extensively for major crops like cotton and winter wheat. However, it is difficult to assessing real productivity of them. Advanced technologies as GIS and RS are vital tool for geospatially analysing and making decisions on this type of fields. This research was carried out for real-time crop monitoring and yield forecasting in case of low productive (3.5 ha) and high productive (8.3 ha) cotton areas of Jarkurgan district (Surkhandarya region, Uzbekistan) based on geospatial analyses of multi-temporal satellite images, condition of groundwater, soil salinity, and ground truth data. For monitoring vegetation phenology of cotton and forecasting its harvest, False Colour, NDVI (Normalized Difference Vegetation Index) and SI (Salinity Index) analyses of areas were carried out by using 6 temporal windows of multi-temporal Sentinel 2 from April through August 2019. Besides, groundwater condition data which was obtained from observation wells these located in massives consists of both cotton fields was analysed by IDW (Inverse Distance Weighting) interpolation algorithm method to determine groundwater’s effect to vegetation development and yield.

scholarly journals Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

2020 ◽  
Vol 4 (4) ◽  
pp. 813-826
Author(s):  
Mohamed Elhag ◽  
Nese Yimaz ◽  
Jarbou Bahrawi ◽  
Silvena Boteva
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Mediterranean Forests ◽  
Burned Areas ◽  
Multi Temporal

AbstractForest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.

scholarly journals AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA &amp; OPTICAL DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 137-140
Author(s):  
K. Ramalingam ◽  
A. B. Ramathilagam ◽  
P. Murugesan
Keyword(s):  
Tamil Nadu ◽  
Unsupervised Classification ◽  
Optical Data ◽  
Landsat 8 ◽  
Maize Crop ◽  
Classification Approach ◽  
Area Estimation ◽  
Crop Calendar ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> This study was carried out to estimate the area of cotton and maize crops in Permabalur district of Tamil Nadu using microwave and optical data. Permabalur was selected as the study area, as it is the largest cotton and maize producing district in Tamil Nadu. The multi-temporal Sentinel-1A SAR data was acquired from 09th July, 2016 to 17th January, 2017 as it coincides with the crop calendar of these crops. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data were compared. The cloud free Landsat 8 data acquired on 7th October 2016 was fused with the Vertical–Vertical (VV) and Vertical-Horizontal (VH) polarized data of 13th October and classified. Unsupervised classification approach was adopted to classify the cotton and maize pixels. The highest accuracy of 72.73% and 76.24% were achieved in VV polarization + Landsat 8 data and VH polarization + Landsat 8 data respectively. The cotton and maize areas were estimated to be 20,218&amp;thinsp;ha and 28,032&amp;thinsp;ha respectively. It is also evident that VH polarization fused with optical data is better in discriminating the cotton and maize crop than VV polarization fused with optical data.</p>

scholarly journals INVENTORY AND ASSESSMENT OF CORIANDER CROP IN THE STATE OF RAJASTHAN USING MULTITEMPORAL REMOTE SENSING DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 315-320
Author(s):  
S. Roy ◽  
N. Singh ◽  
P. Kumar ◽  
M. M. Kimothi ◽  
S. Mamatha
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
State Level ◽  
Optical Data ◽  
Mature Stage ◽  
Tonal Pattern ◽  
Remote Sensing Technique ◽  
Multi Temporal ◽  
Multitemporal Remote Sensing ◽  
Sentinel 2A

<p><strong>Abstract.</strong> The present study aims to develop the methodology for inventory and assessment of coriander crop in Rajasthan using remote sensing technique. Sentinel-2A optical data having a spatial resolution of 10&amp;thinsp;m, from January&amp;ndash;March, 2017 were considered for this study keeping in mind the crop calendar. It was found that coriander at its flowering stage gives a distinct light pink colour which helps it to differentiate from other crops. However it is difficult to separate other stages of coriander (early vegetative, mature stage) owing to its similarity in tonal pattern with mustard. The overall accuracy of single date image was found to be 63.29% and Kappa (K^) Coefficient as 0.5532. With the inclusion of multiple dates accuracy increased to 91.14% and Kappa (K^) Coefficient to 0.7436. This was because increase in information increases the possibility to separate crops from each other. This study demonstrates the feasibility of multi-temporal satellite data for accurate coriander crop mapping area estimation in multi-crop scenario with reasonable accuracy at the Block/district level and State level.</p>

scholarly journals Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

2020 ◽  
Vol 9 (4) ◽  
pp. 248
Author(s):  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Adam Salach ◽  
Zdzisław Kurczyński
Keyword(s):  
Laser Scanning ◽  
Vegetation Index ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Optical Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Levee Failure ◽  
Multi Temporal

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods.

scholarly journals ESTIMATION OF COTTON AND MAIZE CROP AREA IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI-DATE SENTINEL-1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 67-71
Author(s):  
M. Ashmitha Nihar ◽  
J. Mohammed Ahamed ◽  
S. Pazhanivelan ◽  
R. Kumaraperumal ◽  
K. Ganesha Raj
Keyword(s):  
Decision Tree ◽  
Tamil Nadu ◽  
Ground Truth ◽  
Decision Tree Classifier ◽  
Maize Crop ◽  
Ground Truth Data ◽  
Tree Classifier ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

<p><strong>Abstract.</strong> Crop classification is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data has an advantage in crop classification because of its all-weather imaging capabilities. The objective of this study was to investigate the capability of SAR data for estimation of cotton and maize area in Perambalur district of Tamil Nadu. The multi-temporal Sentinel-1 SAR data was acquired from 2nd September, 2017 to 24th January, 2018. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data was used. Ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. Sixty per cent of the ground truth data were used for training and remaining forty per cent were utilized for validation. The temporal backscattering coefficient (&amp;sigma;0) for cotton and maize were extracted using the training datasets.. The mean backscattering values for cotton crop during the entire cropping period had a range from &amp;minus;11.729&amp;thinsp;dB to &amp;minus;8.827&amp;thinsp;dB and from &amp;minus;19.167&amp;thinsp;dB to &amp;minus;14.186 dB for VV and VH polarization respectively. For maize crop it ranged from &amp;minus;11.248&amp;thinsp;dB to &amp;minus;8.878&amp;thinsp;dB and from &amp;minus;19.043 dB to &amp;minus;14.753&amp;thinsp;dB for VV and VH polarized data respectively. The Spectral Angle Mapper (SAM) and Decision Tree classifier (DT) methods were adopted for cotton and maize area estimation. SAM classified 73259 and 51489 hectares (ha) as cotton and maize respectively in VV polarization. DT classified the area of 61501 and 64530&amp;thinsp;ha for cotton and maize respectively in VH polarization. The accuracy measures, such as overall accuracy, producer’s accuracy and user’s accuracy and kappa coefficient were estimated. SAM classifier exhibits the overall accuracy of 73.3% for VV Decision tree classifier reported the overall accuracy of 75.0% for VH. It is evident from the present study, that the multi-temporal Sentinel-1 SAR sensor can be well used for the discrimination of cotton and maize crops because of its high temporal resolution which captures the complete phenology of the crops during the cropping period.</p>

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li
Keyword(s):  
Standard Deviation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Dynamic Change ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Fractional Vegetation Cover ◽  
Dynamic Mapping ◽  
Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

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.

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