scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2012 ◽  
Vol 9 (8) ◽  
pp. 10149-10205 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Nitrogen Content ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Leaf Nitrogen ◽  
Nitrogen Pools ◽  
Canopy Nitrogen ◽  
Use Categories

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10–20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five European landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r2=0.7; rmse = 0.73), canopy chlorophyll content (r2=0.51; rmse = 439 mg m−2) and canopy nitrogen content (r2 = 0.53; rmse = 2.21 g m−2). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, barley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10 × 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (CHLc) for the five landscapes could be predicted with improved accuracy (LAI rmse = 0.59; CHLc rmse = 346 g m−2; Ncrmse = 1.49 g m−2). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 t km−2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2013 ◽  
Vol 10 (10) ◽  
pp. 6279-6307 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Leaf Nitrogen ◽  
Agricultural Landscapes ◽  
Nitrogen Pools ◽  
Spectral Vegetation Indices

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

scholarly journals Integrating an Expert System, GIS, and Satellite Remote Sensing to Evaluate Land Suitability for Sustainable Tea Production in Bangladesh

2020 ◽  
Vol 12 (24) ◽  
pp. 4136
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Multicriteria Analysis ◽  
Land Evaluation ◽  
Land Policy ◽  
Tea Production

Land evaluation is important for assessing environmental limitations that inhibit higher yield and productivity in tea. The aim of this research was to determine the suitable lands for sustainable tea production in the northeastern part of Bangladesh using phenological datasets from remote sensing, geospatial datasets of soil–plant biophysical properties, and expert opinions. Sentinel-2 satellite images were processed to obtain layers for land use and land cover (LULC) as well as the normalized difference vegetation index (NDVI). Data from the Shuttle Radar Topography Mission (SRTM) were used to generate the elevation layer. Other vector and raster layers of edaphic, climatic parameters, and vegetation indices were processed in ArcGIS 10.7.1® software. Finally, suitability classes were determined using weighted overlay of spatial analysis based on reclassified raster layers of all parameters along with the results from multicriteria analysis. The results of the study showed that only 41,460 hectares of land (3.37% of the total land) were in the highly suitable category. The proportions of moderately suitable, marginally suitable, and not suitable land categories for tea cultivation in the Sylhet Division were 9.01%, 49.87%, and 37.75%, respectively. Thirty-one tea estates were located in highly suitable areas, 79 in moderately suitable areas, 24 in marginally suitable areas, and only one in a not suitable area. Yield estimation was performed with the NDVI (R2 = 0.69, 0.66, and 0.67) and the LAI (R2 = 0.68, 0.65, and 0.63) for 2017, 2018, and 2019, respectively. This research suggests that satellite remote sensing and GIS application with the analytical hierarchy process (AHP) could be used by agricultural land use planners and land policy makers to select suitable lands for increasing tea production.

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

scholarly journals Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 1940 ◽  
Author(s):  
Liang Liang ◽  
Liping Di ◽  
Ting Huang ◽  
Jiahui Wang ◽  
Li Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Leaf Nitrogen ◽  
Support Vector ◽  
Leaf Nitrogen Content ◽  
Random Forest Regression ◽  
First Derivative ◽  
Remote Sensing Mapping

Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.

scholarly journals Multispectral remote sensing for site-specific nitrogen fertilizer management

2013 ◽  
Vol 48 (10) ◽  
pp. 1394-1401 ◽  
Author(s):  
Nikrooz Bagheri ◽  
Hojjat Ahmadi ◽  
Seyed Kazem Alavipanah ◽  
Mahmoud Omid
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Fertilizer Management ◽  
Site Specific ◽  
Multispectral Remote Sensing ◽  
Index 2 ◽  
Canopy Nitrogen ◽  
Corn Canopy

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

scholarly journals Management of current land use of perennial industrial crops by NDVI index: A case study in Chu Se District, Gia Lai Province, Vietnam

2014 ◽  
Vol 6 (2) ◽  
pp. 159-164
Author(s):  
Hoang Khanh Linh Nguyen ◽  
Bich Ngoc Nguyen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Modern Technology ◽  
Landsat 8 ◽  
Viet Nam ◽  
Gis Technology ◽  
Industrial Plants ◽  
Ndvi Index

Remote sensing and Geographic Information System (GIS) - an effective tool for managing natural resources, is quite common application in establishing thematic maps. However, the application of this modern technology in natural resource management has not yet been popular in Vietnam, particularly mapping the land use/cover. Currently, land use/cover map is constructed as traditional methods and gets limitations of management counting due to time-consuming for mapping and synthesis the status of land use/cover. Hence, information on the map is often outdated and inaccurate. The main objective of this study is to upgrade the accuracies in mapping current perennial crops in Chu Se District, Gia Lai Province in Vietnam by interpreted NDVI index (Normalized Difference Vegetation Index) from Landsat 8-OLI (Operational Land Imager). The results of study is satisfied the urgent of practical requirement and scientific research. There are 3 types of perennial industrial plants in the study area including rubber, coffee, and pepper, in which most coffee is grown, with an area of over 10,000 hectares. The results also show that integration of remote sensing and GIS technology enables to map current management and distribution of perennial industrial plants timely and accurately. This application is fully consistent with the trend of the world, and in accordance with regulations of established land use/cover map, and the process could be applied at other districts /towns or in higher administrative units. Viễn thám và hệ thông tin địa lý (GIS) là công cụ hữu hiệu để quản lý tài nguyên thiên nhiên, được ứng dụng khá phổ biến để thành lập các loại bản đồ. Tuy nhiên, việc áp dụng công nghệ hiện đại này trong lĩnh vực quản lý tài nguyên thiên nhiên ở Việt Nam chưa phổ biến, nhất là công tác xây dựng bản đồ hiện trạng sử dụng/độ phủ đất. Việc xây dựng bản đồ hiện trạng hiện nay vẫn theo phương pháp truyền thống, thường gặp nhiều hạn chế do thời gian tổng hợp và xây dựng bản đồ hiện trạng kéo dài, dẫn đến thông tin trên bản đồ bị lạc hậu và không chính xác. Mục tiêu chính của nghiên cứu này là nâng cao độ chính xác kết quả giải đoán ảnh viễn thám Landsat 8 bằng chỉ số NDVI (chỉ số khác biệt thực vật) để thành lập bản đồ hiện trạng sử dụng đất cây công nghiệp lâu năm ở huyện Chư Sê, tỉnh Gia Lai, Việt Nam. Từ đó quản lý hiện trạng sử dụng loại đất này phù hợp yêu cầu cấp bách thực tiễn sản xuất và nghiên cứu khoa học. Kết quả của nghiên cứu cho thấy có 3 loại hình cây công nghiệp trên địa bàn nghiên cứu gồm cây cao su, cà phê và hồ tiêu, trong đó cây cà phê được trồng nhiều nhất, với diện tích hơn 10.000 ha. Nghiên cứu cũng cho thấy, tích hợp công nghệ viễn thám và GIS cho phép quản lý hiện trạng và phân bố cây công nghiệp trong không gian một cách hiệu quả và nhanh chóng. Ứng dụng này hoàn toàn phù hợp với xu hướng của thế giới, đồng thời theo đúng quy định thành lập bản đồ hiện trạng sử dụng đất, và quy trình này có thể thực hiện được ở cấp huyện/thị xã hoặc đơn vị hành chính cấp cao hơn.

scholarly journals Application of High-Resolution Remote-Sensing Data for Land Use Land Cover Mapping of University Campus

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Irwan Ary Dharmawan ◽  
Muhammad Ario Eko Rahadianto ◽  
Edward Henry ◽  
Cipta Endyana ◽  
Muhammad Aufaristama
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Surface Runoff ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrastructure Development ◽  
Land Use Land Cover ◽  
University Campus

The study of Land Use Land Cover (LULC) is essential to understanding how land has been altered in recent years and what has caused the processes behind the change. This is significant for the future development of the area, particularly on the campus of the Universitas Padjadjaran Jatinangor. The purpose of this study was to apply remote-sensing techniques to map a university campus and vicinity by comparing the area of urban green space (UGS) and floor area ratios (FARs) of the campus in 2015 and 2017. Additionally, surface runoff analysis was also conducted. For our research, we used WorldView-2’s high-resolution satellite imagery with a resolution of 0.46 m in the Universitas Padjadjaran (Padjadjaran University, or Unpad) Jatinangor campus, Jawa Barat, Indonesia. Our approach was to interpret the imagery by running the normalized difference vegetation index (NDVI) to distinguish UGS and FAR and using digital elevation model (DEM) interferometric synthetic aperture radar (SAR) data with hydrologic analysis to identify the direction of surface runoff. The results obtained are as follows: the UGS remained more extensive compared with FAR, but the difference decreased over time owing to infrastructure development. Surface runoff has tended to flow toward the southeast in direct relation to the slope configuration.

scholarly journals Avaliação da regeneração natural e da recuperação ambiental em áreas de depósitos de expurgos no âmbito do Projeto de Integração do São Francisco com as Bacias do Nordeste Setentrional – PISF (Evaluation of natural regeneration and environmental...)

2017 ◽  
Vol 10 (4) ◽  
pp. 1199
Author(s):  
Getúlio Ezequiel da Costa Peixoto Filho ◽  
Edilson De Souza Bias
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Natural Regeneration ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographic Information ◽  
Erosion Processes ◽  
Main Instrument ◽  
High Degree

O Projeto de Integração da Bacia do São Francisco com as Bacias do Nordeste Setentrional – PISF é dotado de diversos tipos de obras e intervenções, tais como aquedutos, estações elevatórias, barragens, canais, formando – em seu conjunto – uma obra com alto grau de complexidade, produzindo diversos impactos ambientais e degradando muitas áreas. Dentre os 38 programas ambientais no âmbito do Projeto Básico Ambiental – PBA do PISF, há o PBA 09 “Programa de Recuperação de Áreas Degradadas” que tem por objetivo evitar o agravamento de processos erosivos e o comprometimento dos canais de água, assim como possibilitar a retomada do uso original ou alternativo das áreas onde houver intervenção. A proposta do presente estudo surgiu no sentido de complementar as ações adotados no âmbito do PBA 09. Nesse, avaliou-se a regeneração/ recuperação em áreas de depósitos de expurgos a partir de ferramentas de sensoriamento remoto, Sistemas de Informações Geográficas – SIG e dados de campo. Para tanto, o principal instrumento utilizado para avaliar a regeneração/ recuperação foi o Índice de Vegetação por Diferença Normalizada. A partir da obtenção do índice de vegetação e estabelecimento de classes de cobertura do solo através do Software Envi 5.1@, realizou-se a integração e análise com o uso do software ArcGis 10.1@. Os dados de campo utilizados para avaliar a regeneração/ recuperação ambiental foram as áreas onde receberam depósitos de expurgos. De acordo com os métodos adotados neste trabalho pode-se perceber que as ações de regeneração/ recuperação em áreas de depósitos de expurgos adotadas foram pouco expressivas.   A B S T R A C TThe Project for the Integration of the São Francisco Basin with the Northern Northeast Basins - PISF is endowed with many types of constructions and interventions, such as aqueducts, elevation stations, dams, canals, forming – together – a work with a high degree of complexity, producing several environmental impacts and degrading many areas. Among the 38 programs in the scope of the Basic Environmental Project - BEP of the PISF, there is the BEP 09 ";Program for the Recovery of Degraded Areas";, whose objective is to avoid the worsening of erosion processes and the impairment of water channels, as well as to enable a resumption of usage of original or alternate areas of intervention. The proposal of the present study emerged in the sense to complement the actions adopted in the scope of PBA 09. In this study the regeneration / recovery in areas of waste disposal sites was evaluated using remote sensing tools, Geographic Information Systems - GIS and in field data. For this, the main instrument used to evaluate the regeneration / recovery was the Normalized Difference Vegetation Index. From the collect of the vegetation index and the establishment of coverage classes of the soil through Envi 5.1® software, and integrations and analysis with the ArcGis 10.1® program. The field data used to evaluate the environmental regeneration / recovery were the areas that received deposits of purges. According to the methods adopted in this study it can be seen that the regeneration / recovery actions in areas of purge deposits were not very significant.Keywords: Natural Regeneration; Recovery of Degraded Areas; Vegetation Index; Remote Sensing; Geographic Information System. 

scholarly journals A New Remote Sensing Approach to Enrich Hydropower Dams’ Information and Assess Their Impact Distances: A Case Study in the Mekong River Basin

2019 ◽  
Vol 11 (24) ◽  
pp. 3016
Author(s):  
Zihan Lin ◽  
Jiaguo Qi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mekong River ◽  
Mekong River Basin ◽  
Hydropower Dam ◽  
Hydropower Dams

Hydropower dam information such as construction and completion timings is often incomplete and missing in existing dam databases, and the hydropower dam’s adjacency impact distance, which is important to the surrounding environment, is also lacking for many dams. In this study, we developed a new remote sensing approach to specifically determine the timings and to assess the influencing distance on land use and land cover at the above and below dam areas. We established the new remote sensing method by identifying levels shifts in trajectories of Normalized Difference Vegetation Index (NDVI) indicators and by identifying the change point in entropy coefficient of variation (CV) variations to allow an auto-acquisition of the information above at the water basin level. We used three geospatial datasets including 1) a 30-year Landsat time series, 2) an annual Landsat Normalized Difference Vegetation Index (NDVI) composite, and 3) digital elevation model (DEM) data. We applied the proposed method to the Mekong River Basin (MRB) in Southeast Asia, where hydropower dam constructions have increased significantly since the 1990s. Results suggested that we were able to obtain the desired information for 67 Mekong hydropower dams successfully. Pearson correlation tests were used to validate timing results against official records, and the correlation coefficients were found to be 0.96 and 0.90, respectively, for construction and completion timing determination. We discovered that the buffer radius of a Mekong dam’s adjacency impact on land use and land cover was usually 4.0-km and 2.5-km in the above and below dam area. The data determined from this study may fill important information gaps in existing dam databases, and the approach developed in this case may be generalized to the other watersheds of the world, where hydropower dams exist. However, essential dam information is either incomplete or unavailable.

scholarly journals Nitrogen fertilization management in white oat using spectral indices

2020 ◽  
Vol 50 ◽  
Author(s):  
José de Arruda Barbosa ◽  
Rogério Teixeira de Faria ◽  
Anderson Prates Coelho ◽  
Alexandre Barcellos Dalri ◽  
Luiz Fabiano Palaretti
Keyword(s):  
Remote Sensing ◽  
Nitrogen Fertilization ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technology ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Spectral Indices ◽  
Remote Sensing Techniques ◽  
Nitrogen Rates

ABSTRACT Remote sensing techniques have been considered a new technology in worldwide agriculture for diagnosing the plant nutritional demand. Fertilizer management efficiency is a goal to be achieved, and modern tools based on remote sensing are promising for monitoring the crop needs. This study aimed to evaluate the agronomic performance and relative economic return of white oat under nitrogen rates, as well as to verify whether the normalized difference vegetation index (NDVI) and leaf chlorophyll index (LCI) could be used for topdressing nitrogen fertilization management, in white oat. Treatments consisted of five topdressing nitrogen fertilization strategies: T1 - 160 kg ha-1 (reference rate); T2 - 90 kg ha-1 (recommended rate); T3 - 60 kg ha-1 (economic rate); T4 - 30 kg ha-1 (when NDVI < 90 % of T1); and T5 - 30 kg ha-1 (when LCI < 90 % of T1). The white oat did not respond to the topdressing nitrogen fertilization. Its temporal monitoring using spectral indices allowed dispensing the topdressing nitrogen fertilization without reducing the grain and biomass yields and the leaf nitrogen content, when compared to the recommended management (90 kg ha-1 of N as topdressing), with no differences between the evaluated spectral indices. Thus, both the NDVI and LCI spectral indices are promising tools for the topdressing nitrogen fertilization management in the white oat crop.

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