scholarly journals Pengaruh Tingkat Kompresi Citra ALOS AVNIR-2 terhadap Akurasi Hasil Transformasi Indeks Vegetasi dan Klasifikasi Penutup Lahan Wilayah Salatiga dan Ambarawa, Jawa Tengah

2020 ◽  
Vol 34 (2) ◽  
pp. 130
Author(s):  
Projo Danoedoro
Keyword(s):  
Image Compression ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Original Data ◽  
Data Availability ◽  
Classification Methods ◽  
Quality Of Data ◽  
Pixel Classification ◽  
Central Java ◽  
Object Based

Abstrak Penggunaan teknik kompresi untuk menghemat ukuran penyimpanan citra digital telah banyak dijumpai dalam aplikasi keseharian. Di sisi lain, kompresi citra juga dapat memberikan konsekuensi berupa kehilangan detil data, yang akan berpengaruh pada integritas data. dan secara teoretis juga akan berpengaruh pada kualitas turunan data.  Penelitian ini mengkaji pengaruh tingkat kompresi citra digital multispektral ALOS-AVNIR2 yang terdiri dari empat saluran dengan resolusi spasial 10 meter terhadap akurasi hasil transformasi indeks vegetasi dan  klasifikasi penutup lahan untuk wilayah Salatiga-Ambarawa, Jawa Tengah.  Citra dikompresi pada sembilan tingkat, yaitu dari tidak kehilangan detil sama sekali (100%, atau sama dengan data asli) hingga 10%, dengan interval 10%. Indeks Vegetasi yang diterapkan meliputi NDVI, TVI dan MSARVI. Klasifikasi multispektral yang diujicobakan meliputi  klasifikasi per-piksel  dan klasifikasi berbasis objek.  Hasil penelitian ini menunjukkan bahwa transformasi indeks vegetasi dan klasifikasi per-piksel mengalami penurunan akurasi secara drastis, sejalan dengan meningkatnya kompresi citra, sementara klasifikasi berbasis objek mengalami perubahan akurasi relatif lebih sedikit dibandingkan analisis per-piksel. Temuan penelitian ini menunjukkan bahwa penggunaan citra terkompresi sebagai masukan proses klasifikasi secara digital sebaiknya dihindari. Meskipun demikian, kalau pun terpaksa dilakukan karena masalah ketersediaan data, maka metode klasifikasi berbasis objeklah yang sebaiknya diterapkan; dan untuk klasifikasi per-piksel maka algoritma jarak minimum terhadap rerata-lah yang  sebaiknya dipilih. Abstract The use of compression techniques for saving storage space of digital imagery has been commonly found in daily applications.  On the other hand, image compression can also provide consequences of losing data details, which will affect data integrity and theoretically will also affect the quality of data derived. This study examined the effect of ALOS-AVNIR2 multispectal image compression level consisting of four channels with 10 m spatial resolution to the accuracies of vegetation index transformation and land cover classification for Salatiga and Ambarawa region, Central Java. This study compressed the image into nine levels, i.e. from lossless details (100%, or equal to original data) up to 10% compression, at 10% intervals. The applied vegetation indices include NDVI, TVI and MSARVI. The multispectral classifications that were piloted include the per-pixel and object-based classification methods. The results of this study indicated that the vegetation index transformation and per-pixel classification have drastically decreased accuracies, in line with the increase in image compression; while the object-based classification has relatively more stable than per-pixel analysis. The findings of this study showed that the use of compressed imagery as an input to digital classification process should be avoided. However, even if it has to be done due to data availability issues, then object-based classification methods should be applied; and especially for per-pixel classification,  the minimum distance to mean algorithm should be chose.

scholarly journals Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs

2020 ◽  
Vol 12 (13) ◽  
pp. 2086
Author(s):  
Himadri Biswas ◽  
Keqi Zhang ◽  
Michael S. Ross ◽  
Daniel Gann
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Aerial Photographs ◽  
Watershed Segmentation ◽  
Marsh Vegetation ◽  
Tree Detection ◽  
Object Based ◽  
Segmented Images ◽  
Individual Trees ◽  
True Color

Mangrove migration, or transgression in response to global climatic changes or sea-level rise, is a slow process; to capture it, understanding both the present distribution of mangroves at individual patch (single- or clumped trees) scale, and their rates of change are essential. In this study, a new method was developed to delineate individual patches and to estimate mangrove cover from very high-resolution (0.08 m spatial resolution) true color (Red (R), Green (G), and Blue (B) spectral channels) aerial photography. The method utilizes marker-based watershed segmentation, where markers are detected using a vegetation index and Otsu’s automatic thresholding. Fourteen commonly used vegetation indices were tested, and shadows were removed from the segmented images to determine their effect on the accuracy of tree detection, cover estimation, and patch delineation. According to point-based accuracy analysis, we obtained adjusted overall accuracies >90% in tree detection using seven vegetation indices. Likewise, using an object-based approach, the highest overlap accuracy between predicted and reference data was 95%. The vegetation index Excess Green (ExG) without shadow removal produced the most accurate mangrove maps by separating tree patches from shadows and background marsh vegetation and detecting more individual trees. The method provides high precision delineation of mangrove trees and patches, and the opportunity to analyze mangrove migration patterns at the scale of isolated individuals and patches.

scholarly journals Analysis of the Influence of Vegetation Index Choice on the Classification of Satellite Images for Monitoring Forest Pathology

2020 ◽  
pp. paper49-1-paper49-12
Author(s):  
Evgeniy Trubakov ◽  
Olga Trubakova
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Multispectral Images ◽  
Classification Methods ◽  
Planted Forests ◽  
The Earth ◽  
Evaluation Algorithm ◽  
And Control

Rational use of natural resources and control over their recovery, as well as over destruction due to natural and technogenic causes, is currently one of the most urgent problems of the humanity. Forests are no exception. Multispectral images from Earth’s satellites are most often used for monitoring changes in forest planting. This is due to the fact that merging images taken in certain spectra makes it possible to recognize vegetation containing chlorophyll quite well. It also allows to detect changes in the level of chlorophyll, which shows the differences between healthy and damaged plants. Large areas of planted forests create the need to process huge amounts of data, which is difficult to do manually. One of the most important stages of image processing is the classification of objects in these images. This paper deals with various classification methods used to solve the problem of classifying images of remote sensing of the Earth. As a result, it was decided to evaluate the accuracy of classification methods on various vegetation indices. In the course of the study, the evaluation algorithm was determined, as well as one of the options for analyzing the results obtained. Conclusions were made about the work of classification methods on different vegetation indices.

scholarly journals Satellite imagery and machine learning for aridity disaster classification using vegetation indices

2020 ◽  
Vol 9 (3) ◽  
pp. 1149-1158
Author(s):  
Sri Yulianto Joko Prasetyo ◽  
Kristoko Dwi Hartomo ◽  
Mila Chrismawati Paseleng ◽  
Dian Widiyanto Chandra ◽  
Edi Winarko
Keyword(s):  
Vegetation Index ◽  
Risk Index ◽  
Vegetation Indices ◽  
Spatial Prediction ◽  
Drought Risk ◽  
Landsat 8 ◽  
Index Data ◽  
Central Java ◽  
Spectral Vegetation Indices ◽  
Risk Areas

Central Java Province is one of provinces in Indonesia that has a high aridity risk index. Aridity disaster risk monitoring and detection can be done more accurately in larger areas and with lower costs if the vegetation index is extracted from the remote sensing imagery. This study aims to provide accurate aridity risk index information using spectral vegetation index data obtained from LANDSAT 8 OLI satellite. The classification of drought risk areas was carried out using k-nn with the Spatial Autocorrelation method. The spectral vegetation indices used in the study are NDVI, SAVI, VHI, TCI and VCI. The results show a positive correlation and trend between the spectral vegetation index influenced by seasonal dynamics and the characteristics of the High R.A. and Middle R.A. drought risk areas. The highest correlation coefficient is SAVI with a High R.A. amounted to 0.967 and Middle R.A. amounted to 0.951. The results of the Kappa accuracy test comparison show that SVM and k-nn have the same accuracy of 88.30. The result of spatial prediction using the IDW method shows that spectral vegetation index data that initially as an outlier, using the k-nn method, the spectral vegetation index data can be identified as data in the aridity classification. The spatial connectivity test among sub-districts that experience drought was done using Moran’s I Analysis.

scholarly journals Evaluation of Rapeseed Winter Crop Damage Using UAV-Based Multispectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2618
Author(s):  
Łukasz Jełowicki ◽  
Konrad Sosnowicz ◽  
Wojciech Ostrowski ◽  
Katarzyna Osińska-Skotak ◽  
Krzysztof Bakuła
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Frost Damage ◽  
Crop Damage ◽  
Multispectral Imagery ◽  
Spectral Bands ◽  
Red Edge ◽  
Object Based ◽  
Direct Measurements

This research is related to the exploitation of multispectral imagery from an unmanned aerial vehicle (UAV) in the assessment of damage to rapeseed after winter. Such damage is one of a few cases for which reimbursement may be claimed in agricultural insurance. Since direct measurements are difficult in such a case, mainly because of large, unreachable areas, it is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage. In this experiment, UAV images were taken using a Sequoia multispectral camera that collected data in four spectral bands: green, red, red-edge, and near-infrared. Data were acquired from three altitudes above the ground, which resulted in different ground sampling distances. Within several tests, various vegetation indices, calculated based on four spectral bands, were used in the experiment (normalized difference vegetation index (NDVI), normalized difference vegetation index—red edge (NDVI_RE), optimized soil adjusted vegetation index (OSAVI), optimized soil adjusted vegetation index—red edge (OSAVI_RE), soil adjusted vegetation index (SAVI), soil adjusted vegetation index—red edge (SAVI_RE)). As a result, selected vegetation indices were provided to classify the areas which qualified for reimbursement due to frost damage. The negative influence of visible technical roads was proved and eliminated using OBIA (object-based image analysis) to select and remove roads from classified images selected for classification. Detection of damaged areas was performed using three different approaches, one object-based and two pixel-based. Different ground sampling distances and different vegetation indices were tested within the experiment, which demonstrated the possibility of using the modern low-altitude photogrammetry of a UAV platform with a multispectral sensor in applications related to agriculture. Within the tests performed, it was shown that detection using UAV-based multispectral data can be a successful alternative for direct measurements in a field to estimate the area of winterkill damage. The best results were achieved in the study of damage detection using OSAVI and NDVI and images with ground sampling distance (GSD) = 10 cm, with an overall classification accuracy of 95% and a F1-score value of 0.87. Other results of approaches with different flight settings and vegetation indices were also promising.

scholarly journals Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

2020 ◽  
Vol 12 (3) ◽  
pp. 514 ◽  
Author(s):  
Dominic Fawcett ◽  
Cinzia Panigada ◽  
Giulia Tagliabue ◽  
Mirco Boschetti ◽  
Marco Celesti ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Radiometric Calibration ◽  
Quality Of Data ◽  
Vegetation Monitoring ◽  
Operational Conditions ◽  
Spatial Consistency ◽  
Normalised Difference Vegetation Index ◽  
Airborne Imaging

Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

scholarly journals Prediksi Spasial Wilayah Resiko Tanah Longsor Di Jawa Tengah Berdasarkan SAVI, OSAVI, DVI, NDVI Menggunakan Krigging

2018 ◽  
Vol 1 (2) ◽  
pp. 80-86
Author(s):  
Dwi Hayati ◽  
Sri Yulianto Joko Prasetyo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Digital Signal ◽  
Spatial Prediction ◽  
Sensor Data ◽  
Tree Cover ◽  
Landslide Occurrence ◽  
High Category ◽  
Central Java

Landslides are the process of moving rock periods (soil) due to gravity. On the spatial prediction of landslide occurrence in the District in Central Java based on vegetation index using kriging. The vegetation index is the amount of green vegetation values obtained from the processing of digital signal data of the brightness value of several satellite sensor data channels. Some of the vegetation index algorithms used are SAVI (Soil Adjusted Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), DVI (Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index). Kriging is one of the prediction and interpolation methods in geostatistika, consisting of two types of ordinary kriging when only one variable and cokriging when there are more than one variable observed. Kriging functioning formation of color gradient pattern on map result of data interpolation. In this research it was found that the occurrence of landslide in the sample area correlated with low, medium, high, DVI vegetation index of DVI, NDVI, SAVI, OSVII. Banjarnegara Regency is prone to landslides in medium category, Wonosobo Regency in High category, Magelang Regency in High category, Kebumen Regency in Low category, Purworejo Regency in Low category. So it can be concluded that landslides are affected or associated with low tree cover seen by NDVI, DVI, SAVI, OSAVI vegetation indices.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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