scholarly journals Correlação linear entre a precipitação e o Índice de Área Foliar do bioma Caatinga

2020 ◽  
Vol 13 (07) ◽  
pp. 3304
Author(s):  
Josiclêda Domiciano Galvíncio ◽  
Sandra Maria Mendes ◽  
Weronica Meira Souza ◽  
Magna Soelma Beserra de Moura ◽  
Wanderson Santos
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Temporal Dynamics ◽  
Correction Method ◽  
Area Index ◽  
Caatinga Biome ◽  
Linear Correction ◽  
Eta Model ◽  
Statistical Relationships

Sabe-se que a precipitação é uma variável de difícil estimativa em especial nas regiões semiáridas. Mesmo com os diversos estudos e avanços já obtidos ainda se necessita desenvolver modelos que possam proporcionar estimativas mais reais. Com o intuito de contribuir nessa linha de pesquisa, este estudo teve como objetivo avaliar a relação existente entre a precipitação e o índice de área foliar no bioma caatinga. Para tanto se faz necessário uma boa distribuição espacial da precipitação uma vez que com o uso do sensoriamento remoto é possível obter uma boa estimativa de índice de área foliar espacialmente. Os dados de precipitação utilizados neste estudo foram obtidos pelo o modelo ETA. Os dados de índice de área foliar foram obtidos pelo o sensor MODIS. Utilizou-se o método de correção linear simples. As relações estatísticas mostraram uma boa correlação entre o índice de área foliar e a precipitação.  Assim, conclui-se que o entendimento da dinâmica do índice de área foliar espacial e temporal pode ajudar no entendimento da dinâmica espacial e temporal da precipitação na caatinga. Acredita-se que a estimativa da precipitação pelo modelo ETA pode ser melhorada com o uso do índice de área foliar.Palavras-chave: sensoriamento remoto, LAI, modelo ETA, precipitação, caatinga. Linear correlation between rainfall and Leaf Area Index of the Caatinga biome A B S T R A C TIt is known that rainfall is a variable that is difficult to estimate, especially in semiarid regions. Even with the various studies and advances already obtained, it is still necessary to develop models that can provide more real estimates. In order to contribute to this line of research, this study aimed to assess the relationship between rainfall and the leaf area index in the caatinga biome. Therefore, a good spatial distribution of precipitation is necessary since with the use of remote sensing it is possible to obtain a good estimate of the spatial leaf area index. The precipitation data used in this study were obtained using the ETA model. The leaf area index data were obtained by the MODIS sensor. The simple linear correction method was used. The statistical relationships showed a good correlation between the leaf area index and precipitation. Thus, it is concluded that understanding the dynamics of the spatial and temporal leaf area index can help in understanding the spatial and temporal dynamics of precipitation in the caatinga. It is believed that the precipitation estimate by the ETA model can be improved with the use of the leaf area index.Keywords: remote sensing, LAI, ETA model, rainfall, caatinga.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

scholarly journals A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

scholarly journals Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals

2018 ◽  
Vol 37 (3) ◽  
pp. 269-280 ◽  
Author(s):  
William A. White ◽  
Maria Mar Alsina ◽  
Héctor Nieto ◽  
Lynn G. McKee ◽  
Feng Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Indirect Measurement ◽  
Area Index

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

1997 ◽  
Vol 18 (16) ◽  
pp. 3459-3471 ◽  
Author(s):  
S. E. Franklin ◽  
M. B. Lavigne ◽  
M. J. Deuling ◽  
M. A. Wulder ◽  
E. R. Hunt
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Production ◽  
Remote Sensing And Gis ◽  
Area Index ◽  
Gis Data

scholarly journals Estimation of Leaf Area Index Using Remote Sensing Data.

1999 ◽  
Vol 12 (3) ◽  
pp. 210-220 ◽  
Author(s):  
Takashi ISHII ◽  
Makoto NASHIMOTO ◽  
Hisashi SHIMOGAKI
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

2021 ◽  
Vol 13 (16) ◽  
pp. 3263
Author(s):  
Zhijie Liu ◽  
Pengju Guo ◽  
Heng Liu ◽  
Pan Fan ◽  
Pengzong Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Shaanxi Province ◽  
Gradient Boosting ◽  
Support Vector ◽  
Apple Orchards ◽  
Apple Trees ◽  
Area Index ◽  
Spatial Efficiency

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

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