scholarly journals Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

2021 ◽  
Vol 13 (19) ◽  
pp. 3820
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Luis Paixão ◽  
José Marques da Silva ◽  
Tiago Morais ◽  
...  
Keyword(s):  
Optical Sensor ◽  
Precision Agriculture ◽  
Environmental Sustainability ◽  
Satellite Images ◽  
Vegetation Index ◽  
Livestock Production ◽  
Tree Canopy ◽  
Neutral Detergent Fiber ◽  
Pasture Quality ◽  
Montado Ecosystem

The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R2 = 0.82) and measured by proximal optical sensor (R2 = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems.

scholarly journals Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

2020 ◽  
Vol 10 (13) ◽  
pp. 4463
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
Emanuel Carreira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Service Providers ◽  
Quality Parameters ◽  
Neutral Detergent Fiber ◽  
Pasture Quality ◽  
Reference Methods

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

scholarly journals Climate Changes Challenges to the Management of Mediterranean Montado Ecosystem: Perspectives for Use of Precision Agriculture Technologies

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 218 ◽  
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
Emanuel Carreira ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Positive Impact ◽  
Floristic Composition ◽  
Tree Canopy ◽  
Spatial And Temporal Variability ◽  
Pastoral System ◽  
Negative Effect ◽  
Vegetative Cycle ◽  
Montado Ecosystem

Montado is an agro-silvo-pastoral system characterized by a high complexity as a result of the interactions between climate, soil, pasture, trees, and animals. It is in this context that management decisions must be made, for example with respect to soil fertilization, grazing, or animal supplementation. In this work, the effect of the tree canopy on the spatial and temporal variability of the soil and productivity, quality, and floristic composition of the pasture was evaluated. Precision agriculture (PA) technologies for monitoring soil and pasture were also evaluated. The study was carried out between October 2015 and June 2018 in an experimental field of 2.3 ha. The results showed: (i) The positive impact of trees and animal grazing on soil fertility; (ii) the influence of inter-annual variability of precipitation on the pattern of pasture vegetative cycle; (iii) the positive effect of trees in pasture quality; (iv) the negative effect of trees in pasture productivity; (v) the role of pasture floristic composition as an indicator of soil limitations or climatic changes; (vi) the potential of technologies associated with the concept of PA as express tools to decision making support and for the optimization of the herbaceous stratum and the dynamic management of grazing in this ecosystem in a holistic and sustainable form.

scholarly journals Geospatial Analysis of Changes in Vegetation Cover over Nigeria

2017 ◽  
Vol 13 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Olutoyin Fashae ◽  
Adeyemi Olusola ◽  
Oluwatola Adedeji
Keyword(s):  
Information Technology ◽  
Markov Chain ◽  
Environmental Sustainability ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalised Difference Vegetation Index ◽  
Ecological Hazards ◽  
Spatio Temporal

AbstractVegetation cover over Nigeria has been on the decrease recently, hence the need for adequate monitoring using geo-information technology. This study examined the spatio-temporal variation of vegetation cover over Nigeria for thirty years with a view to developing a strategy for enhancing environmental sustainability. In order to predict the spatial extent of vegetation cover in 2030, the study utilised satellite images from between 1981 and 2010 using the Normalised Difference Vegetation Index (NDVI) coupled with cellular automata and Markov chain techniques in ArcGIS 10.3. The results showed that dense vegetal areas decreased in area from 358,534.2 km2in 1981 to 207,812 km2in 2010, while non-vegetal areas increased from 312,640.8 km2in 1981 to 474,436.4 km2in 2010 with a predicted increase to 501,504.9 km2by 2030, i.e. an increase of about 27,068.4 km2between 2010 and 2030. The study concluded that geoinformation techniques are effective in monitoring long-term intra- and inter-annual variability of vegetation and also useful in developing sustainable strategies for combating ecological hazards.

scholarly journals Precision Agriculture Application for Sustainable Nitrogen Management of Justicia brandegeana Using Optical Sensor Technology

Agriculture ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 98 ◽  
Author(s):  
Ariel Freidenreich ◽  
Gabriel Barraza ◽  
Krishnaswamy Jayachandran ◽  
Amir Ali Khoddamzadeh
Keyword(s):  
Optical Sensor ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Visual Analysis ◽  
Sensor Technology ◽  
Soil Leachate ◽  
Nutrient Runoff ◽  
Non Destructive ◽  
Spad Readings

Over-fertilization is a common practice in ornamental nursery production. Oftentimes, visual analysis is used to determine plant nutrient levels, leading to less accurate estimates of fertilizer application. This study focused on exploring the suitability of two non-destructive sensors, Soil Plant Analysis Development (SPAD-502) and GreenSeekerTM, for measuring plant tissue nutrient uptake. Florikan Top-Dress fertilizer 12N-6P-8K was applied to Justicia brandegeana in various increments (0, 10, 20, 30, 40, and 50 g) to simulate plants with deficient to excessive nitrogen rates. Various parameters were recorded including Normalized Difference Vegetation Index (NDVI) and SPAD readings, soil leachate analysis (nitrates and phosphate), and total leaf carbon:nitrogen (C:N). The NDVI and SPAD readings were recorded biweekly for three months after the initial controlled release fertilizer (CRF) treatments. Leaf C:N was analyzed through dry combustion while nitrates and phosphate were determined from soil leachate. Results suggest that the smaller amount (20 g) of CRF is as effective in providing N to J. brandegeana as larger amounts (30, 40, 50 g). Implementation of this fertilizer regimen will result in reduced agricultural nutrient runoff and overall negative environmental impacts. Application of optical sensor technology using SPAD and GreenSeekerTM showed promising results in determining the fertilizer requirements of J. brandegeana. This method could serve as a guideline for nursery producers and landscape personnel as a fast and non-destructive tool for sustainable fertilizer management practices within the ornamental plant industry.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Optical Sensors ◽  
Near Infrared ◽  
Production Systems ◽  
External Validation ◽  
Neutral Detergent Fiber ◽  
Reference Laboratory ◽  
Pasture Quality ◽  
Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

scholarly journals Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1221
Author(s):  
Yuki Hamada ◽  
Colleen R. Zumpf ◽  
Jules F. Cacho ◽  
DoKyoung Lee ◽  
Cheng-Hsien Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Great Promise ◽  
Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

scholarly journals Smarter Robotic Sprayer System for Precision Agriculture

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2061
Author(s):  
André Rodrigues Baltazar ◽  
Filipe Neves dos Santos ◽  
António Paulo Moreira ◽  
António Valente ◽  
José Boaventura Cunha
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Reference Value ◽  
Support Vector ◽  
Svm Classifier ◽  
Accuracy Score ◽  
Accuracy And Precision ◽  
Perception System ◽  
Water Rate ◽  
Leaf Density

The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

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