scholarly journals Monitoring of Afforestation Activities Using Landsat-8 Temporal Images, Billion Trees Afforestation Project, Pakistan

2021 ◽  
Vol 42 ◽  
pp. 27-40
Author(s):  
Muhammad Daud Khan ◽  
Saba Ali Arooj ◽  
Waqar Ahmed ◽  
Zia-ur Rehman ◽  
Arif Iqbal ◽  
...  
Keyword(s):  
Survival Rate ◽  
Growth Performance ◽  
Forest Restoration ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Robinia Pseudoacacia ◽  
Vegetation Indices ◽  
Eucalyptus Camaldulensis ◽  
Landsat 8 ◽  
Pinus Roxburghii

In context of Bonn Challenge commitment, Pakistan (Khyber Pukhtunkhwa) has implemented forest restoration and afforestation on 0.35 million hectares between 2015–2017. Billion Tree Afforestation Project (BTAP) is an initiative of mass afforestation and forest restoration to meet the Bonn Challenge commitment. The current study is a pilot study to evaluate the success of plantation activities by assessment of regeneration, growth performance and survival rate of plantations raised under BTAP in Malakand Forest Division. Further, four vegetation indices were computed from Landsat-8 image, which include Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Difference Vegetation Index (DVI). A total of 72 sample plots of 0.1 ha were laid out in 11 plantation sites extended over an area of 647 ha in Timargara, Chakdara and Jandool. According to the results, all the selected plantation sites showed good performance in terms of survival rate, mostly above 90%. In terms of species composition, Eucalyptus camaldulensis has the highest share (81%) followed by Robinia pseduacacia with 17% and Pinus roxburghii with 2% share in the plantation. Growth performance was good in all species; Pinus roxburghii attained an average girth of 14.3 cm and height of 3.21 feet, whereas Eucalyptus camaldulensis and Robinia pseudoacacia attained a mean girth of 10.3 and 12.1 cm with the height of 8.6 and 8.2 feet in 27 months, respectively. Further, a good correlation was observed between the volume (m3) and Landsat-8 spectral values. The highest performance (R2=0.63) was recorded by NDVI and SAVI. The temporal changes in spectral values of Landsat-8 images from 2013 to 2018 showed that the plantation was successful at these sites. The study concluded that FLR activities across the Khyber Pukhtunkwa province will rehabilitate and improve the existing forest ecosystems and support local livelihood for climate change mitigation.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

scholarly journals IMPACT ASSESSMENT OF A MINE SUBSIDENCE ON NATIVE VEGETATION OF SOUTH EASTERN COALFIELDS, INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 487-490
Author(s):  
A. K. Vishwakarma ◽  
A. K. Agnihotri ◽  
R. Rai ◽  
B. K. Shrivastva ◽  
S. Mishra
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Adverse Impact ◽  
Native Vegetation ◽  
Landsat 8 ◽  
Vegetation Growth ◽  
South Eastern ◽  
Wide Range

<p><strong>Abstract.</strong> This study aims to evaluate the effect of underground coal mining subsidence on the growth of native vegetation. For this study, an underground coal mine of South Eastern Coalfields Limited (SECL), India was selected. Changes in vegetation indices were analyzed using three remote sensing data of the previous five years. Three period’s Landsat 8 OLI resolution image data were used to calculate Normalized Difference Vegetation Index (NDVI) of the years 2014, 2016 and 2018 in QGIS environment. The study showed that the local grassland and forest were affected by the mining exploitation and subsidence but those effects were not significant to have an adverse impact on the same. The short-term mining was having an impact on the vegetation growth but the effects gradually disappeared with the gradual stabilization of the subsided land and in absence of human interference, vegetation recovered well. In long-term, subsidence was not having a major impact on the vegetation growth. Thus, coal resources exploitation and subsidence of the said mine of SECL did not bring out an adverse impact on a wide range of forest and grassland ecosystems, and these ecosystems could carry the partial destruction and ultimately stabilized ecosystems by self-repair.</p>

scholarly journals Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

2021 ◽  
Vol 14 (2) ◽  
pp. 869
Author(s):  
João Pedro Ocanha Krizek ◽  
Luciana Cavalcanti Maia Santos
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Environmental Parameters ◽  
Biomass Productivity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
De Se ◽  
Correct Estimation

A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa.  Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação.   Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

scholarly journals ANÁLISE DO IMPACTO DA CORREÇÃO ATMOSFÉRICA NO CÁLCULO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA A PARTIR DE IMAGEM LANDSAT 8/OLI

2020 ◽  
Vol 13 (1) ◽  
pp. 076
Author(s):  
Cristiane Nunes Francisco ◽  
Paulo Roberto da Silva Ruiz ◽  
Cláudia Maria de Almeida ◽  
Nina Cardoso Gruber ◽  
Camila Souza dos Anjos
Keyword(s):  
Rio De Janeiro ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat 8 Oli ◽  
Arithmetic Operations

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação.  Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

scholarly journals Landscape Representation by a Permanent Forest Plot and Alternative Plot Designs in a Typhoon Hotspot, Fushan, Taiwan

2020 ◽  
Vol 12 (4) ◽  
pp. 660 ◽  
Author(s):  
Jonathan Peereman ◽  
James Aaron Hogan ◽  
Teng-Chiu Lin
Keyword(s):  
Forest Dynamics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Permanent Plots ◽  
Forest Plot ◽  
Landsat 8 ◽  
Four Square

Permanent forest dynamics plots have provided valuable insights into many aspects of forest ecology. The evaluation of their representativeness within the landscape is necessary to understanding the limitations of findings from permanent plots at larger spatial scales. Studies on the representativeness of forest plots with respect to landscape heterogeneity and disturbance effect have already been carried out, but knowledge of how multiple disturbances affect plot representativeness is lacking—particularly in sites where several disturbances can occur between forest plot censuses. This study explores the effects of five typhoon disturbances on the Fushan Forest Dynamics Plot (FFDP) and its surrounding landscape, the Fushan Experimental Forest (FEF), in Taiwan where typhoons occur annually. The representativeness of the FFDP for the FEF was studied using four topographical variables derived from a digital elevation model and two vegetation indices (VIs), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII), calculated from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data. Representativeness of four alternative plot designs were tested by dividing the FFDP into subplots over wider elevational ranges. Results showed that the FFDP neither represents landscape elevational range (<10%) nor vegetation cover (<7% of the interquartile range, IQR). Although disturbance effects (i.e., ΔVIs) were also different between the FFDP and the FEF, comparisons showed no under- or over-exposure to typhoon damage frequency or intensity within the FFDP. In addition, the ΔVIs were of the same magnitudes in the plots and the reserve, and the plot covered 30% to 75.9% of IQRs of the reserve ΔVIs. Unexpectedly, the alternative plot designs did not lead to increased representation of damage for 3 out of the 4 tested typhoons and they did not suggest higher representativeness of rectangular vs. square plots. Based on the comparison of mean Euclidian distances, two rectangular plots had smaller distances than four square or four rectangular plots of the same area. Therefore, this study suggests that the current FFDP provides a better representation of its landscape disturbances than alternatives, which contained wider topographical variation and would be more difficult to conduct ground surveys. However, upscaling needs to be done with caution as, in the case of the FEF, plot representativeness varied among typhoons.

scholarly journals COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS

2016 ◽  
Vol XLI-B7 ◽  
pp. 671-678 ◽  
Author(s):  
J. M. Landmann ◽  
M. Rutzinger ◽  
M. Bremer ◽  
K. chmidtner
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Forest Stand ◽  
Spectral Unmixing ◽  
Larch Forest ◽  
Main Step ◽  
Landsat 8 ◽  
Spectral Signal ◽  
Larch Tree

Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (&lt;i&gt;Larix decidua&lt;/i&gt; MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as “bias correction” for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.

scholarly journals COMPREHENSIVE SPECTRAL SIGNAL INVESTIGATION OF A LARCH FOREST COMBINING GROUND- AND SATELLITE-BASED MEASUREMENTS

2016 ◽  
Vol XLI-B7 ◽  
pp. 671-678
Author(s):  
J. M. Landmann ◽  
M. Rutzinger ◽  
M. Bremer ◽  
K. chmidtner
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Forest Stand ◽  
Spectral Unmixing ◽  
Larch Forest ◽  
Main Step ◽  
Landsat 8 ◽  
Spectral Signal ◽  
Larch Tree

Collecting comprehensive knowledge about spectral signals in areas composed by complex structured objects is a challenging task in remote sensing. In the case of vegetation, shadow effects on reflectance are especially difficult to determine. This work analyzes a larch forest stand (<i>Larix decidua</i> MILL.) in Pinnis Valley (Tyrol, Austria). The main goal is extracting the larch spectral signal on Landsat 8 (LS8) Operational Land Imager (OLI) images using ground measurements with the Cropscan Multispectral Radiometer with five bands (MSR5) simultaneously to satellite overpasses in summer 2015. First, the relationship between field spectrometer and OLI data on a cultivated grassland area next to the forest stand is investigated. Median ground measurements for each of the grassland parcels serve for calculation of the mean difference between the two sensors. Differences are used as “bias correction” for field spectrometer values. In the main step, spectral unmixing of the OLI images is applied to the larch forest, specifying the larch tree spectral signal based on corrected field spectrometer measurements of the larch understory. In order to determine larch tree and shadow fractions on OLI pixels, a representative 3D tree shape is used to construct a digital forest. Benefits of this approach are the computational savings compared to a radiative transfer modeling. Remaining shortcomings are the limited capability to consider exact tree shapes and nonlinear processes. Different methods to implement shadows are tested and spectral vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Greenness Index (GI) can be computed even without considering shadows.

scholarly journals Spacialization of the Degradation Risk in the Natural Monument of the São Francisco River

2019 ◽  
Vol 9 (4) ◽  
pp. 204
Author(s):  
Douglas Alberto De Oliveira Silva ◽  
Suzana Maria Gico Lima Montenegro ◽  
Pabrício Marcos Oliveira Lopes ◽  
Gabriel Siqueira Tavares Fernandes ◽  
Ênio Farias de França e Silva ◽  
...  
Keyword(s):  
Decision Tree ◽  
Change Detection ◽  
San Francisco ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Detection Technique ◽  
Landsat 8 ◽  
Decision Tree Classification ◽  
Natural Monument

Understanding changes related to environmental degradation by parameters such as Normalized Difference Vegetation Index (NDVI), Surface Albedo (α) and Moving Standard Deviation Index (MDSI) has been of great relevance in the study of environmental impacts. The objective of the present study was to analyze the evolution of soil degradation and also of the soil use and occupation in the San Francisco River Natural Monument, using surface data and images from Landsat-5 and Landsat-8, for the 1987, 1997, 2007 and 2017 years. Remote sensing techniques were used to estimate indices such as NDVI, albedo (α) and MDSI. The change detection technique and decision tree classification based on predefined rules in NDVI, albedo and MDSI were applied to infer degradation, soil use and occupation. There was a significant increase in degradation, especially for areas with high degradation. Vegetation indices showed the lowest values for areas of low vegetation and exposed soil, being the highest values found for Caatinga dense vegetation. It was concluded that the change detection technique and decision tree classification were efficient in identifying the degradation during the study period. The change detection technique algorithm was more sensitive to water bodies than the change intensity technique.

scholarly journals Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 652
Author(s):  
Attila Nagy ◽  
Andrea Szabó ◽  
Odunayo David Adeniyi ◽  
János Tamás
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Tisza River ◽  
Yield Forecasting

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

scholarly journals Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series

2020 ◽  
Author(s):  
Odunayo David Adeniyi ◽  
Andrea Szabo ◽  
János Tamás ◽  
Attila Nagy
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Image Data ◽  
Efficiency Index ◽  
Landsat 8 ◽  
Large Area ◽  
Yield Data

Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in J&aacute;sz-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.

Sign in / Sign up

Export Citation Format

Share Document