scholarly journals Application of an improved vegetation index from the visible spectrum in the diagnosis of degraded pastures: Implications for development

2021 ◽  
Author(s):  
Thiago Luiz Silva Quinaia ◽  
Renato Farias Valle Junior ◽  
Victor Peçanha Miranda Coelho ◽  
Rafael Carvalho Cunha ◽  
Carlos Alberto Valera ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Visible Spectrum ◽  
Degraded Pastures

scholarly journals Application of an improved vegetation index from the visible spectrum in the diagnosis of degraded pastures: Implications for development

2021 ◽  
Author(s):  
Thiago Quinaia ◽  
Renato Valle Junior ◽  
Victor Coelho ◽  
Rafael Cunha ◽  
Carlos Valera ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Visible Spectrum ◽  
Google Earth ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Pasture Management ◽  
Efficient Detection ◽  
Pasture Degradation ◽  
Degraded Pastures

Inadequate pasture management causes land degradation and negative impacts on the socio-economic development of agricultural regions. Given the importance for Brazil and the World of pasture-based livestock production, the recognition of pasture degradation is essential. The use of remote sensing satellite systems to detect degraded pastures increased in the recent past, because of their capability to survey large portions of Earth’s surface. A struggle nowadays is to improve detection accuracy and to implement high-resolution surveys at farmland scale using unmanned aerial vehicles (UAVs). The satellite sensors capture reflectance from the visible spectrum and near infrared bands, which allows estimating plant’s vigor vegetation indices. The NDVI is a widely accepted index, but to generate an NDVI map using a UAV a relatively high-cost multispectral sensor is required, while most UAVs are equipped with low-cost RGB cameras. In the present study, a script developed on the Google Earth Engine image-processing platform manipulated images from the Landsat 8 satellite, and compared the performances of NDVI and an improved color index that we coined “Total Brightness Quotient” of red (TBQR), green (TBQG) and blue (TBQB) bands. An efficient detection of pasture degradation using the TBQs would be a good prognosis for the surveys at farm scale where environmental authorities are progressively using UAVs and forcing landowners towards pasture restoration. When compared to NDVI, the TBQG showed a correlation of 0.965 and an accuracy of 88.63%. Thus, the TBQG proved as efficient as the NDVI in the diagnosis of degraded pastures.

scholarly journals Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane

2017 ◽  
Vol 35 (1) ◽  
pp. 82-91
Author(s):  
Cesar Edwin García ◽  
David Montero ◽  
Hector Alberto Chica
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Early Stage ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Crop Productivity ◽  
Spectral Information ◽  
Nitrogen Effect ◽  
Sugarcane Yield

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

scholarly journals Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices

2020 ◽  
Vol 12 (18) ◽  
pp. 2970
Author(s):  
Anna C. Talucci ◽  
Elena Forbath ◽  
Heather Kropp ◽  
Heather D. Alexander ◽  
Jennie DeMarco ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Basal Area ◽  
Vegetation Recovery ◽  
Climate Conditions ◽  
Northeastern Siberia ◽  
Larch Forests

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.

scholarly journals Increase of Yield, Lycopene, and Lutein Content in Tomatoes Grown Under Continuous PAR Spectrum LED Lighting

2021 ◽  
Vol 12 ◽  
Author(s):  
Dennis Dannehl ◽  
Thomas Schwend ◽  
Daniel Veit ◽  
Uwe Schmidt
Keyword(s):  
Emission Spectrum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Visible Spectrum ◽  
Growth And Yield ◽  
Carotene Content ◽  
Light Emitting ◽  
Efficient Alternative ◽  
Number Of Leaves ◽  
Use Efficiency

Light emitting diodes (LEDs) are an energy efficient alternative to high-pressure sodium (HPS) lighting in tomato cultivation. In the past years, we have learned a lot about the effect of red and blue LEDs on plant growth and yield of tomatoes. From previous studies, we know that plants absorb and utilize most of the visible spectrum for photosynthesis. This part of the spectrum is referred to as the photosynthetically active radiation (PAR). We designed a LED fixture with an emission spectrum that partially matches the range of 400 to 700 nm and thus partially covers the absorption spectrum of photosynthetic pigments in tomato leaves. Tomato plants grown under this fixture were significantly taller and produced a higher fruit yield (14%) than plants grown under HPS lighting. There was no difference in the number of leaves and trusses, leaf area, stem diameter, the electron transport rate, and the normalized difference vegetation index. Lycopene and lutein contents in tomatoes were 18% and 142% higher when they were exposed to the LED fixture. However, the ß-carotene content was not different between the light treatments. Transpiration rate under LED was significantly lower (40%), while the light use efficiency (LUE) was significantly higher (19%) compared to HPS lighting. These data show that an LED fixture with an emission spectrum covering the entire PAR range can improve LUE, yields, and content of secondary metabolites in tomatoes compared to HPS lighting.

scholarly journals USING UAVs/DRONES AND VEGETATION INDICES IN THE VISIBLE SPECTRUM TO MONITORING AGRICULTURAL LANDS

2021 ◽  
Vol 52 (3) ◽  
pp. 601-610
Author(s):  
Qubaa & et al.
Keyword(s):  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Agricultural Field ◽  
Smart Systems ◽  
Different Types ◽  
Management And Development ◽  
Uav Images ◽  
First Time

Unmanned Aerial Vehicles UAVs or Drones have made great progress in the field of aerial surveys to study vegetation and farmland. The research focuses on developing smart systems for managing agricultural fields, thus facilitating decision-making, increasing agricultural productivity, improving profitability and protecting the environment. The paper highlights the ability of drones to distinguish agricultural land intended for cultivation and classified as deserted or cultivated or in the germination stage. For the first time in the Nineveh governorate, a Phantom 4 DJI UAV images were used, in addition to using the spatialized Pix4Dfielde program to process these images. Four types of the standard agricultural indices that rely on the visible spectrum have been used (Visible Atmospherically Resistant Index (VARI), Triangular Greenness Index (TGI), Synthetic Normalized Differences Vegetation Index (S-NDVI) and Visible Difference Vegetation Index (VDVI)) to test UAVs images and to categorize different types of agricultural land. The results showed that when using the S-NDVI and VDVI indicators, the values 0.16 and 0.14 appeared respectively in certain areas, which indicates the presence and integrity of vegetation cover, unlike other regions, whose indicators showed 0.010 and -0.004, respectively, which indicate that the plant has a bad condition or its absence at all.  All results finding in this research reflect and confirm the validity of using UAVs images for agricultural field management and development.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

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.

A Simple Model for Yield Prediction of Rice Based on Vegetation Index Derived from Satellite and AMeDAS Data during Ripening Period

2003 ◽  
Vol 59 (4) ◽  
pp. 277-286 ◽  
Author(s):  
Yasuyuki WAKIYAMA ◽  
Kimio INOUE ◽  
Kou NAKAZONO
Keyword(s):  
Simple Model ◽  
Vegetation Index ◽  
Yield Prediction ◽  
Ripening Period
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