scholarly journals Different Temporal Stability and Responses to Droughts between Needleleaf Forests and Broadleaf Forests in North China during 2001–2018

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1331
Author(s):  
Xiran Li ◽  
Muxing Liu ◽  
Olivia L. Hajek ◽  
Guodong Yin
Keyword(s):  
North China ◽  
Vegetation Index ◽  
Physiological Activity ◽  
Temporal Stability ◽  
Legacy Effects ◽  
Forest Types ◽  
Decadal Time Scale ◽  
Enhanced Vegetation Index ◽  
Forest Modeling ◽  
Management And Conservation

Droughts can affect the physiological activity of trees, damage tissues, and even trigger mortality, yet the response of different forest types to drought at the decadal time scale remains uncertain. In this study, we used two remote sensing-based vegetation products, the MODIS enhanced vegetation index (EVI) and MODIS gross primary productivity (GPP), to explore the temporal stability of deciduous needleleaf forests (DNFs) and deciduous broadleaf forests (DBFs) in droughts and their legacy effects in North China from 2001 to 2018. The results of both products showed that the temporal stability of DBFs was consistently much higher than that of DNFs, even though the DBFs experienced extreme droughts and the DNFs did not. The DBFs also exhibited similar patterns in their legacy effects from droughts, with these effects extending up to 4 years after the droughts. These results indicate that DBFs have been better acclimated to drought events in North China. Furthermore, the results suggest that the GPP was more sensitive to water variability than EVI. These findings will be helpful for forest modeling, management, and conservation.

scholarly journals Enhanced Vegetation Index (EVI) na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES

2008 ◽  
Vol 32 (6) ◽  
pp. 1099-1107 ◽  
Author(s):  
André Quintão de Almeida ◽  
Gilson Fernandes da Silva ◽  
José Eduardo Macedo Pezzopane ◽  
Carlos Alexandre Damasceno Ribeiro
Keyword(s):  
Vegetation Index ◽  
Enhanced Vegetation Index ◽  
Espírito Santo ◽  
Espirito Santo

Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994), 125 observações do Enhanced Vegetation Index (EVI) foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC) -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1)(1,0,1)12.

scholarly journals Integration of Soil Electrical Conductivity and Indices Obtained through Satellite Imagery for Differential Management of Pasture Fertilization

2019 ◽  
Vol 1 (4) ◽  
pp. 567-585 ◽  
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
José Calado ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Stability ◽  
Soil Characteristics ◽  
Low Fertility ◽  
Soil Electrical Conductivity ◽  
Potential Productivity

Dryland pastures in the Alentejo region, located in the south of Portugal, normally occupy soils that have low fertility but, simultaneously, important spatial variability. Rational application of fertilizers requires knowledge of spatial variability of soil characteristics and crop response, which reinforces the interest of technologies that facilitates the identification of homogeneous management zones (HMZ). In this work, a pasture field of about 25 ha, integrated in the Montado mixed ecosystem (agro-silvo-pastoral), was monitored. Surveys of apparent soil electrical conductivity (ECa) were carried out in November 2017 and October 2018 with a Veris 2000 XA contact sensor. A total of 24 sampling points (30 × 30 m) were established in tree-free zones to allow readings of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). Historical time series of these indices were obtained from satellite imagery (Sentinel-2) in winter and spring 2017 and 2018. Three zones with different potential productivity were defined based on the results obtained in terms of spatial variability and temporal stability of the measured parameters. These are the basis for the elaboration of differentiated prescription maps of fertilizers with variable application rate technology, taking into account the variability of soil characteristics and pasture development, contributing to the sustainability of this ecosystem.

scholarly journals Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery

2007 ◽  
Vol 58 (4) ◽  
pp. 316 ◽  
Author(s):  
A. B. Potgieter ◽  
A. Apan ◽  
P. Dunn ◽  
G. Hammer
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
High Accuracy ◽  
Correct Classification ◽  
Pixel Classification ◽  
Study Region ◽  
Enhanced Vegetation Index ◽  
Multi Temporal ◽  
Crop Area ◽  
Pixel Scale

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April–November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

scholarly journals Variações da reflectância e dos índices de vegetação em função dos parâmetros da modelagem topográfica no Parque Estadual do Turvo, Rio Grande do Sul, Brasil

2016 ◽  
Author(s):  
William Gaida ◽  
Fabio Marcelo Breunig ◽  
Lênio Soares Galvão ◽  
Thiago Souza Teles ◽  
Rafaelo Balbinot
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rio Grande ◽  
Rio Grande Do Sul ◽  
Enhanced Vegetation Index ◽  
Aster Gdem

<p>Técnicas de sensoriamento remoto têm sido amplamente utilizadas em estudos florestais, por permitirem avaliar e monitorar importantes áreas florestais, como as do Parque Estadual do Turvo (PET). O PET é o maior remanescente de floresta subtropical decídua do sul do Brasil. Contudo, fatores externos como efeitos topográficos podem influenciar a resposta espectral dos alvos nos produtos de satélites. Este estudo teve como objetivo avaliar a magnitude das variações na reflectância de superfície e nos índices de vegetação em função das características topográficas locais. A metodologia desenvolvida incluiu a aquisição de dados do sensor RapidEye e o modelo digital de elevação ASTER GDEM 2. O efeito da variação da geometria de iluminação foi avaliado a partir de duas cenas RapidEye: junho e outubro de 2012. Dados de orientação de vertentes, altimetria e relevo sombreado foram gerados a partir do ASTER GDEM 2, permitindo avaliar o efeito topográfico sobre a reflectância e os índices de vegetação Normalized Difference Vegetation Index (NDVI) e Enhanced Vegetation Index (EVI). Os resultados mostraram que as variaveis topográficas afetam a reflectâcia de todas as bandas RapidEye e os índices de vegetação, especialmente o EVI. Os efeitos topográficos foram mais pronunciados em junho (maior ângulo zenital solar - AZS) do que em outubro (menor AZS). O NDVI foi menos afetado pelas variações sazonais das condições de iluminação e da fenologia do que o EVI. Quanto a orientação, os maiores valores de reflectância e índices de vegetação foram encontrados na orientação norte e os menores na orientação sul.</p>

scholarly journals Pemetaan Pola Tanam dan Kalender Tanam Padi Sawah menggunakan Teknik Penginderaan Jauh

2018 ◽  
Vol 2017 (2) ◽  
Author(s):  
Rika Hernawati ◽  
Agung Budi Harto ◽  
Dewi Kania Sari
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Growth Monitoring ◽  
Enhanced Vegetation Index ◽  
Water Index ◽  
Gaussian Filtering ◽  
Land Surface Water

ABSTRAKPemantauan dan prakiraan hasil tanam padi sawah penting untuk dilakukan antara lain dalam rangka menjaga ketahanan pangan nasional. Saat ini, pemantauan pertumbuhan tanaman padi sawah dapat dilakukan dengan mengaplikasikan teknologi pengindraan jauh, antara lain dengan mendeteksi fenologi tanaman padi sawah yang terekam pada setiap piksel citra yang selanjutnya dapat digunakan untuk pemetaan pola tanam dan kalender tanam padi sawah. Penelitian ini bertujuan untuk mengembangkan algoritma deteksi fenologi padi sawah dengan menggunakan indeks vegetasi Enhanced Vegetation Index (EVI) dan Land Surface Water Index (LSWI) berkala yang diturunkan dari data citra MODIS, dengan menerapkan proses penapisan Gaussian. Penerapan teknik penapisan Gaussian pada data indeks vegetasi tersebut diharapkan dapat meminimalisasi derau, sehingga akan meningkatkan ketelitian hasil pendeteksian fenologi tanaman padi sawah. Wilayah studi mencakup 3 Kabupaten di Provinsi Jawa Barat bagian utara, yaitu Kabupaten Subang, Kabupaten Karawang, dan Kabupaten Bekasi. Hasil penelitian menunjukkan bahwa penerapan penapisan Gaussian pada metode deteksi fenologi padi sawah berbasis indeks vegetasi EVI dan LSWI berkala telah dapat meningkatkan ketelitian hasil deteksi tanggal-tanggal fenologis padi sawah. Keakuratan hasil estimasi luas tanam dan luas panen padi sawah divalidasi menggunakan data statistik dari Dinas Pertanian Kabupaten.Kata Kunci: deteksi fenologi, EVI, LSWI, penapisan GaussianABSTRACTMonitoring and forecasting yields of paddy rice are important to do, in order to maintain national food security. The current paddy crop growth monitoring can be done by applying remote sensing technology by detecting paddy phenology to produce the date of planting and harvest dates, which were recorded at each pixel of the digital image of rice field and can then be used for cropping pattern and planting calendar mapping. This research aims to develop a detection algorithm phenology paddy using vegetation indices Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) periodic image data derived from MODIS, by applying a Gaussian filtering process. The application of Gaussian filtering techniques to the data of vegetation indeces, EVI and LSWI, are expected to minimize the noise, thereby increasing the precision of detection of paddy rice crop phenology. The study area covers three districts in the northern part of West Java Province, i.e. Subang, Karawang and Bekasi. The results showed that the application of Gaussian filtering on the detection method of paddy rice phenology based on multitemporal vegetation indices EVI and LSWI can improve the precision of the detection of paddy phenological dates. The accuracy of the estimation results of the planting and harvested area of paddy were validated using statistical data from the District Agricultural Office.Keywords: phenology detection, EVI, LSWI, Gaussian filtering

scholarly journals Application of multispectral sensors and unmanned aerial system in Stara Planina region

2020 ◽  
pp. 175-186
Author(s):  
Nenad Šurjanac ◽  
Marija Milosavljević ◽  
Mara Tabaković-Tošić ◽  
Miroslava Marković
Keyword(s):  
Vegetation Index ◽  
Physiological Activity ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Sensor Data ◽  
Spectral Signature ◽  
Unmanned Aerial System ◽  
Positive Ratio ◽  
Pest Infestation ◽  
Leaf Mining

In the area of Stara Planina mountain, a multispectral survey of forest vegetation was performed. Data acquisition was done with unmanned aerial system DJI Phantom 4 Pro, equipped with integrated RGB 20Mpix sensor, and MicaSense RedEdge M, 5-channel narrowband multispectral sensor. Data was collected in the form of images, and 4 composite orthomosaics were produced-broadband visible RGB, narrowband visible RGB, and with vegetation indices applied NDVI and NDRE. RGB orthomosaic showed no significant changes in canopies apart from the variability of levels of green. Orthomosaics with vegetation indices applied showed changes in the level of physiological activities of leaves. NDVI map showed the negative changes of the top of the canopies, while NDRE map showed more dramatic changes within the canopy as well. Besides the map, 5 polygons with different NDRE values were selected and their respective spectral signature graphs were produced. The areas with the lowest NDRE values had the highest reflectance values in all wavelengths, while the absorption of light is much higher in physiologically active vegetation. Values of NDRE lower than 0.479 were inspected from the ground. This kind of ground-truth provided evidence that the areas coded in red were with lower physiological activity due to the infestation by beech leaf-mining weevil Orchestes fagi L. Another interesting finding was that both NDVI and NDRE values were higher in the areas not directly exposed to the sunlight. The areas shaded by surrounding canopies received only diffuse light but it showed a more positive ratio between absorbed and reflected wavelength which could be characteristic of the Fagus Sylvatica species. The findings in this study showed a strong correlation between low values NDRE vegetation index and negative changes deep within the canopy of high vegetation, which can serve as an indicator of pest infestation in forestry.

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