scholarly journals Integration of Leaf Water Content Index (LWCI) and Enhanced Vegetation Index (EVI) for Stress Detection of Rice Plant Using Landsat 8 Satellitte Imagery

2019 ◽  
Author(s):  
Abdi Sukmono ◽  
Arief Laila Nugraha ◽  
Hana Sugistu Firdaus
Keyword(s):  
Water Content ◽  
Satellite Imagery ◽  
Stress Level ◽  
Agricultural Intensification ◽  
Vegetation Index ◽  
Leaf Water ◽  
Rice Fields ◽  
Landsat 8 ◽  
Enhanced Vegetation Index ◽  
Rice Consumption

Rice is the main staple food for Indonesian society. Almost 95% of Indonesians consume rice. Along with the increasing population in Indonesia, the level of rice consumption each year has increased. But on the other hand, the amount of paddy fields has decreased due to the development of settlements and industry. Consequently, the business of fulfilling rice consumption needs should prioritize agricultural intensification method. This agricultural intensification program requires good supporting data. One of the supporting data required is a plant health condition that can be represented in data on rice stress levels. Monitoring the stress level of rice plants can be done using remote sensing methods based on satellite imagery. One of them is Landsat-8 satellite imagery with certain algorithm. In this research, a modification algorithm of Rice Paddy Stress Index (RPSI) was obtained by integrating Leaf Water Canopy Index (LWCI) and Enhanced Vegetation Index (EVI). LWCI is used as a representation of water content in vegetation and EVI is used as a representation of the greenish level of plants associated with chlorophyll content. Plants that experience a decrease in health will decrease the content of chlorophyll and water. The results of this study indicate that in 2015 planting season 2 in Kendal Regency there are 1696.26 ha of rice fields indicated experiencing stress and 3493.85 Ha of rice fields have a potential stress. The result of validation test shows that RPSI algorithm method has 75% accuracy for determining rice stress level.

scholarly journals PENGEMBANGAN PENGGUNAAN PENGINDERAAN JAUH UNTUK ESTIMASI PRODUKSI PADI (STUDI KASUS KABUPATEN BEKASI)

2019 ◽  
Vol 19 (1) ◽  
pp. 6-12
Author(s):  
Eka Rudiana ◽  
Ernan Rustiadi ◽  
Muhammad Firdaus ◽  
Dede Dirgahayu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Rice Yield ◽  
Equation Model ◽  
Rice Production ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Enhanced Vegetation Index ◽  
The One ◽  
The Relationship

The utilization of remote sensing imagery such Landsat-8 (OLI) to estimate harvested area and yield using Enhanced Vegetation Index (EVI) parameter is a new approach to estimate regional rice production. Based on the analysis of the satellite imagery acquisition during May-August 2015, the estimation of rice harvested area in Bekasi District during July-October 2015 is 15.86 thousand ha or 7.74 thousand ha (32.79%) lower than BPS figures in the same period. Based on the relationship between yield (from the crop cutting survei, BPS) and EVI maximum, the equation model for rice yield estimation is: Yield (qu ha-1) = 36.818 + 44.965 EVImax. R2 value is 0.809. Based on the model, the estimation of rice yield in Bekasi District during July-October 2015 is 47.40 qu ha-1. Compared to the data published by BPS, the result is 12.66 qu ha-1 lower than the yield figure in subround I 2015, 6.77 qu ha-1 lower than the one in subround II 2015, 10.15 qu ha-1 lower than the one subround III 2015, and 6.62 qu ha-1 lower than the one in January-December 2015. Meanwhile, based on satellite imagery analysis, the estimation of rice production in the period of July-October 2015 is 75.16 thousand tons of GKG or 55.35 thousand tons of GKG (42.41%) lower than BPS figures during the same period. Keywords: Enhanced Vegetation Index, Landsat-8 (OLI), rice production estimation

scholarly journals Transformasi Indeks Vegetasi Citra LANDSAT 8 OLI untuk Pemetaan Musim Tanam pada Lahan Sawah di Kabupaten Buru, Provinsi Maluku

2020 ◽  
Vol 16 (2) ◽  
pp. 197-205
Author(s):  
Nunung N Tatisina ◽  
Willem A Siahaya ◽  
Johanes P Haumahu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Rice Fields ◽  
Paddy Fields ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Accuracy Test ◽  
Planting Season

The use of satellite imagery in agriculture has been widely used, for example in plantation areas or in rice fields. Satellite imagery can estimate the area of paddy fields and estimate rice production; however, the application of satellites for mapping the planting season in rice fields using Landsat 8 OLI imagery and NDVI (Normal Difference Vegetation Index) transformation has not been widely undertaken. This study aimed to analyze the growing season in paddy fields based on the results of NDVI analysis of Multitemporal Landsat 8 OLI imagery. Based on the results of research analyzed from the spectral value of NDVI images recorded in March and May 2020 then added with the results of observations   and farmer interviews in September 2020, it was found that there were color differences in NDVI images caused by differences in planting time or plant age at the time of recording the image. The planting season in rice fields in the Buru regency was generally two times per year or five times per two years. The increase in the rice planting season could be realized due to the availability of water in the field that was sufficient for the growth of rice plants, both from irrigation and rainfall. Based on the interpretation of the images used and field checks, it was found that the accuracy test results showed the overall accuracy of 88%. The overall accuracy value is considered correct if it exceeds the tolerance limit given, which is ≥ 80%. Keywords: Buru Regency, growing season, Landsat 8, NDVI, paddy fields   ABSTRAK Pemanfaatan citra satelit pada bidang pertanian telah banyak dilakukan, misalnya pada areal perkebunan ataupun pada areal persawahan. Citra satelit dapat menduga luas lahan sawah sampai dengan menduga produksi padi; tetapi pemanfaatan citra satelit untuk pemetaan musim tanam di lahan sawah menggunakan citra Landsat 8 OLI dan transformasi NDVI (Normalized Difference Vegetation Index) sampai saat ini belum banyak dilakukan. Penelitian ini bertujuan untuk Menganalisis musim tanam di lahan sawah berdasarkan hasil analisis NDVI Citra Landsat 8 OLI Multitemporal. Berdasarkan hasil penelitian yang dianalisis dari nilai spektral citra NDVI perekaman bulan Maret dan Mei tahun 2020 ditambah dengan hasil pengamatan serta wawancara petani pada bulan September 2020 didapatkan adanya perbedaan warna pada citra NDVI yang diakibatkan oleh perbedaan waktu tanam ataupun umur tanaman pada saat perekaman citra. Musim tanam pada lahan sawah di Kabupaten Buru umumnya dua kali per tahun atau lima kali per dua tahun. Peningkatan musim tanam padi dapat terealisasi karena ketersediaan air di lapangan untuk mencukupi pertumbuhan tanaman padi, baik yang bersumber dari air irigasi maupun curah hujan. Berdasarkan interpretasi citra yang digunakan dan pengecekan lapangan didapatkan bahwa hasil uji   akurasi menunjukkan hasil overall accuracy sebesar 88%. Nilai overall accuracy dianggap benar jika melebihi batas toleransi yang diberikan yakni ≥ 80%. Kata kunci:  Kabupaten Buru, Landsat 8, lahan sawah, musim tanam, NDVI

scholarly journals Relation of leaf water content with real evapotranspiration and biomass in Caatinga biome, using remote sensing data

2017 ◽  
Vol 10 (5) ◽  
pp. 1545
Author(s):  
Josiclêda Domiciano Galvíncio
Keyword(s):  
Energy Balance ◽  
Water Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Energy Balance ◽  
Climatic Variability ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Content Index ◽  
Caatinga Biome

R E S U M OA Caatinga é um biome que sofre com grande variabilidade climática anual e intraanual. Essa variabilidade climática faz com que o bioma em grande parte do ano sofra com grande estresse hídrico. Estudar as relações existentes entre o conteúdo de água na planta e outras variáveis do ecossistemas, tais como: biomassa e evapotranspiração pode auxiliar e prever impactos da escassez hídrica e seca climatológica sobre a produção de biomassa do bioma Caatinga. Assim, este estudo pretende analisar as relações existentes entre o conteúdo de água na folha com a biomassa e evapotranspiração em área do bioma caatinga localizado em São José do Sabugi, Paraiba, Brasil. Foi utilizado o algoritmo SEBAL-Surface Energy Balance para estimar a evapotranspiração e o foram calculados os índices de vegetação NDVI- Normalized Difference Vegetation Index, SAVI- Soil Adjusted Vegetation Index e o índice de conteúdo de água na folha LWCI- Leaf Water Content Index. Os resultados mostraram uma boa relação existente entre os índices de vegetação e o conteúdo de água na folha, sendo r=0.76 para o SAVI e 0.64 para o NDVI. Para a evapotranspiração a correlação foi de r =0.386. Conclui-se que a quantidade de água na folha está altamente correlacionada com a biomassa.Palavra chave: bioma, sazonalidade, seca, semiárido. A B S T R A C TThe Caatinga is a biome that suffers from high annual and intra-annual climatic variability. This climatic variability makes the biome in great part of the year suffer with high great water stress. To study the relationships between water content in the plant and other ecosystem variables, such as: biomass and evapotranspiration can help and predict impacts of water scarcity and climatological drought on the biomass production of the Caatinga biome. Thus, this study intends to analyze the relationship between water content in the leaf with biomass and evapotranspiration in the area of the caatinga biome located in São José do Sabugi, Paraiba, Brazil. The SEBAL-Surface Energy Balance algorithm was used to estimate the evapotranspiration and NDVI-Normalized Difference Vegetation Index, SAVI-Soil Adjusted Vegetation Index and the water content index in the LWCI- Leaf Water Content Index. were calculated. The results showed a good relationship between vegetation index and leaf water content, with r = 0.76 for SAVI and 0.64 for NDVI. For evapotranspiration the correlation was r = 0.386. It is concluded that the amount of water in the leaf is highly correlated with the biomass.Keywords: biome, seasonality, dry, semiarid

scholarly journals Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1221 ◽  
Author(s):  
Jun Wang ◽  
Lichun Sui ◽  
Xiaomei Yang ◽  
Zhihua Wang ◽  
Yueming Liu ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Eastern China ◽  
Chlorophyll Concentration ◽  
False Negative ◽  
Distribution Data ◽  
Landsat 8 ◽  
Negative Results

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

scholarly journals Spatio-Temporal Analysis of Satellite Imagery (NDVI) to Identify Terroir and Vineyard Yeast Differences According to Appellation of Origin (AOP) and Biogeographic Origin

J ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 244-256
Author(s):  
Sergio Vélez ◽  
Enrique Barajas ◽  
Pilar Blanco ◽  
José Antonio Rubio ◽  
David Castrillo
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Analysis ◽  
Landsat 8 ◽  
Appellation Of Origin ◽  
Core Concepts ◽  
Spatio Temporal ◽  
Plant Habitat

Terroir is one of the core concepts associated with wine and presumes that the land from which the grapes are grown, the plant habitat, imparts a unique quality that is specific to that growing site. Additionally, numerous factors can influence yeast diversity, and terroir is among the most relevant. Therefore, it can be interesting to use Remote Sensing tools that help identify and give helpful information about the terroir and key characteristics that define the AOP (Appellation of Origin). In this study, the NDVI (Normalized Difference Vegetation Index) calculated from Landsat 8 imagery was used to perform a spatio-temporal analysis during 2013, 2014, and 2015 of several vineyards belonging to four different AOP in Galicia (Spain). This work shows that it is possible to use Remote Sensing for AOP delimitation. Results suggest: (i) satellite imagery can establish differences in terroir, (ii) the higher the NDVI, the higher the yeast species richness, (iii) the relationship between NDVI, terroir, and yeasts shows a stable trend over the years (Pearson’s r = 0.3894, p = 0.0119).

scholarly journals Mapping the vegetation soil and water region analysis of Tuticorin district using Landsat images

2017 ◽  
Vol 7 (1.3) ◽  
pp. 161
Author(s):  
Cynthia J ◽  
Suguna M ◽  
Senthil S
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Water Bodies ◽  
High Spectral Resolution ◽  
Landsat 8 ◽  
Landsat Images ◽  
Water Region ◽  
Vegetation Region ◽  
Soil And Water ◽  
Water Vegetation

Mapping of water bodies, soil and vegetation region from satellite imagery has been widely explored in the recent past. Several approaches have been developed to detect water bodies and identify the soil types from different satellite imagery varying in spatial, spectral, and temporal characteristics. Due to the introduction of a New Operational Land Imager (OLI) sensor on Landsat 8 with a high spectral resolution and improved signal-to-noise ratio, the quality of imagery sensed is increased. Its imagery produces a better result in classifying the soil and water regions. The current study puts forward an approach to map water bodies, soil and vegetation region from a Landsat satellite imagery using the various processing models. In this study, to identify the water region and soil region, we go with water index, vegetation index and soil index measures. By using reflectance bands, it is easy to analyze the water, vegetation and soil regions. The proposed method accurately and quickly discriminated the water, vegetation and soil region from other land cover features.

scholarly journals Combining the trapezoidal relationship between land surface temperature and vegetation index with the Priestley-Taylor equation to estimate evapotranspiration

2015 ◽  
Vol 368 ◽  
pp. 379-384
Author(s):  
X. Wang ◽  
W. Wang ◽  
Y. Jiang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Hydrological Cycle ◽  
Bare Soil ◽  
Landsat 8 ◽  
Extreme Conditions ◽  
Enhanced Vegetation Index ◽  
Vegetation Water

Abstract. Evapotranspiration (ET) plays an important role in the hydrological cycle. A method of combining the Priestley-Taylor (P-T) equation with a trapezoidal space between land surface temperature (Ts) and enhanced vegetation index (EVI) is proposed based on the principle of energy balance. Generally, this method is divided into three major parts: (1) construct the Ts versus EVI (Ts-VI) trapezoidal space for calculating the Ts at four extreme conditions (i.e. well-watered vegetation, water-stressed vegetation, saturated bare soil and dry bare soil); (2) calculate the P-T coefficient for each pixel according to the position of the observed (EVI, Ts) point in the trapezoid space; (3) calculate actual ET of the pixel using the P-T equation. The method is validated using Landsat-8 images and ground-observed data for a semi-humid area in China. The result shows that the ET estimates match the observations well, which indicates the effectiveness the proposed method here.

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