Caracterização de Cicatrizes de Queimadas nas Mesorregiões do Sertão e São Francisco Pernambucano a partir de dados do Sensor MODIS

As mesorregiões do Sertão e São Francisco Pernambucano apresentam clima semiárido, que podem afetar a produção agrícola, em função do clima quente e seco, com temperaturas elevadas e regime pluviométrico irregular. O bioma predominante da região é a Caatinga, que vem sofrendo ao longo dos anos com várias ações antrópicas, incluindo além do desmatamento eventos de queimadas. O objetivo deste artigo foi mapear, caracterizar e quantificar a incidência de focos de calor nas mesorregiões acima relacionadas, bem como a capacidade de recuperação e/ou regeneração natural da vegetação por meio do sensoriamento remoto e técnicas de mineração de dados. Imagens do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) a bordo da plataforma TERRA foram utilizadas para analisar o estado da vegetação nos períodos pré, durante e pós-queima. Para avaliar as condições necessárias para que ocorra a regeneração natural da superfície vegetal foi utilizado o software de mineração de dados Waikato Environment for Knowledge Analysis (WEKA) a partir do cruzamento dos dados do Índice de Vegetação da Diferença Normalizada (NDVI) e precipitação local. Os resultados demonstram um aumento na ocorrência dos focos no período analisado. Existe uma correlação de 91,76% entre o NDVI durante e 48 dias após o evento da queima. Além disso, os parâmetros NDVI 30 e 48 dias após a queima apresentaram um coeficiente de correlação de 83,96%. Portanto, as técnicas de sensoriamento remoto e mineração de dados permitiram avaliar as relações existentes entre o NDVI e a precipitação local para que ocorra a regeneração vegetal. Characterization of Burning Scars in the Sertão and São Francisco Pernambucano Mesoregions from MODIS Sensor dataA B S T R A C T The Sertão and São Francisco Pernambucano mesoregions have a semi-arid climate, which can affect agricultural production, due to the hot and dry climate, with high temperatures and irregular rainfall. The predominant biome of the region is the Caatinga, which has been suffering over the years with several anthropic actions, including in addition to deforestation, burning events. The purpose of this article was to map, characterize and quantify the incidence of hot spots in the mesoregions listed above, as well as the capacity for recovery and / or natural regeneration of vegetation through remote sensing and data mining techniques. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the TERRA platform were used to analyze the state of vegetation in the pre, during and post-firing periods. To assess the conditions necessary for the natural regeneration of the plant surface to occur, the data mining software Waikato Environment for Knowledge Analysis (WEKA) was used, by crossing the data from the Normalized Ddifference Vegetation Index (NDVI) and precipitation. The results demostrate an increase in the occurrence of outbreaks in the analyzed period. There is a 91.76% correlation between NDVI during and 48 days after burning event. In addition, the NDVI parameters 30 and 48 days after burning presented a correlation coefficient of 83.96%. Therefore, the techniques of remote sensing and data mining allowed to evaluate the existing relationships between NDVI and local precipitation so that plant regeneration to occurs.Keywords: remote sensing, vegetation indexes, hot spots, data mining.

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

Author(s):

Mostafa K. Mosleh ◽

Quazi K. Hassan ◽

Ehsan H. Chowdhury

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rice Yield ◽

Decision Makers ◽

Coefficient Of Determination ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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ANALISIS TUTUPAN LAHAN DENGAN MENGGUNAKAN METODE BREAK FOR ADDITIVE SEASONAL AND TREND (BFAST) BERDASARKAN INDEKS VEGETASI

10.31227/osf.io/yeba6 ◽

2017 ◽

Author(s):

Seminar Nasional Multidisiplin Ilmu 2017 ◽

Ramos Lumban Tobing

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Trend Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Metode penginderaan jarak jauh (Remote Sensing) telah banyak digunakan dalam berbagai bidang termasuk diantaranya bidang tutupan lahan/vegetasi termasuk perkebunan. Produk dari penginderaan jauh tersebut banyak tersedia diantaranya NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Indeks) yang merupakan indikator proxy dari suatu lokasi atau kondisi tutupan lahan lokasi tersebut. Dari beberapa penilitian, NDVI telah banyak digunakan namun EVI masih belum banyak digunakan. Kami membandingkan pengaruh dari penggunaan NDVI dan EVI pada jumlah dan waktu perubahan yang terekam dengan menggunakan metode BFAST (Breaks For Additive Seasonal and Trend). Data yang digunakan adalah MODIS (Moderate Resolution Imaging Spectroradiometer)16 harian NDVI dan EVI berupa gambar komposit (06 April 2000 s.d. 16 November 2014) dari empat piksel (pixel 293,294,295 dan 296) disekitar menara fluks Aek Loba.Hasil penelitian menunjukkan bahwa EVI untuk pemantauan tutupan lahan di kawasan perkebunan tropis yang ditutupi oleh awan intens lebih baik dari NDVI itu. Meskipun demikian, penelitian lebih lanjut dengan meningkatkan resolusi spasial dari citra satelit untuk aplikasi NDVI sangat dianjurkan

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Remote Sensing ◽

10.3390/rs13040719 ◽

2021 ◽

Vol 13 (4) ◽

pp. 719

Author(s):

Xiuxia Li ◽

Shunlin Liang ◽

Huaan Jin

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Data ◽

Landsat Data ◽

Area Index ◽

Free Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700009 ◽

2016 ◽

Vol 51 (7) ◽

pp. 858-868

Author(s):

Marcos Cicarini Hott ◽

Luis Marcelo Tavares de Carvalho ◽

Mauro Antonio Homem Antunes ◽

Polyanne Aguiar dos Santos ◽

Tássia Borges Arantes ◽

...

Keyword(s):

Vegetative Growth ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Growth ◽

Growth Index ◽

Data Series ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

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Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

International Journal of Wildland Fire ◽

10.1071/wf16131 ◽

2017 ◽

Vol 26 (5) ◽

pp. 384

Author(s):

L. M. Ellsworth ◽

A. P. Dale ◽

C. M. Litton ◽

T. Miura

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Fuel Moisture ◽

Fire Behaviour ◽

Native Grass ◽

Tropical Ecosystems ◽

Moderate Resolution ◽

Resolution Imaging ◽

Live Fuel Moisture ◽

Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

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