scholarly journals Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images

2016 ◽  
Vol 24 (1) ◽  
pp. 78-86
Author(s):  
O. M. Kunah ◽  
O. S. Papka
Keyword(s):  
Land Surface ◽  
Ecological Niche ◽  
Vegetation Index ◽  
Principal Component ◽  
Vegetative Cover ◽  
Asclepias Syriaca ◽  
Water Index ◽  
The Common ◽  
Common Milkweed Asclepias Syriaca ◽  
Vegetative Indices

The patterns of variation in vegetative indices received by means of data of remote land sensing are described as being dependant on geomorphological predictors and the sizes of agricultural fields in an experimental polygon within Poltava region. The possibilities of application of vegetative indices have been explored through ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca L.) and other weeds. On the basis of images of the land surface taken on 23 March and 27 August 2015 by the sensor control Operational Land Imager (OLI), installed on the satellite Landsat 8, vegetative indices have been calculated (AC-Index – aerosol/coastal index, Hydrothermal Composite, NDTI – Normalized Difference Tillage Index, NDVI – Normalized Difference Vegetation Index, VI – Vegetation Index, MNDW – Modified Normalized Difference Water Index, LSWI – Land Surface Water Index, NBR – Normalized Burn Ratio, M15). The data obtained have been subjected to principal component analysis and the revealed principal components have been interpreted with the help of regression analysis, in which geomorphological variables have been applied as predictors. It was possible to explain the trends of variability of the vegetative cover, formalized in the form of the principal component, by means of indices which quantitatively characterise features of relief. The various aspects of variation of vegetative cover have been shown to be characterised by the specificity of the influence of relief factors. A prominent aspect of the variation of the vegetative cover of agroecosystems is variability within a field. The degree of a variation of conditions is proportional to the size of a field. Large fields occupy level plain positions. In turn, within small fields sources of variation are changes in ecological conditions which arise owing to unevenness of relief, which increases in proximity to gullies and ravines. We have identified the aspects of the variation of vegetative cover which by their nature can be considered as contributers to the growth of weeds in agroceonoses. Satellite imaging by Landsat does not allow direct identification of concentrations of weeds, but it can reveal complex changes in the landscape cover, which act as markers of the processes connected with development of weed vegetation. The procedure of further decoding of satellite images for the purpose of identification of weeds requires greater attention in this field of research.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

scholarly journals Optical sensors for variable rate nitrogen application in dairy pastures

2017 ◽  
Vol 79 ◽  
pp. 223-227
Author(s):  
K. Wigley ◽  
J.L. Owens ◽  
J.A.K. Trethewey ◽  
D.C. Ekanayake ◽  
R.L. Roten ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Vegetation Index ◽  
N Uptake ◽  
Variable Rate ◽  
Water Index ◽  
Foliar N ◽  
Normalised Difference Vegetation Index ◽  
Fertiliser Application ◽  
Vegetative Indices ◽  
Simple Ratio

Reducing the amount of nitrogen (N) fertiliser applied to dairy pastures down to agronomically optimised levels would have positive economic and environmental results. The ability of commercially available optical sensors to estimate biomass yield and foliar-N uptake in pastures was investigated. Vegetative indices (Simple Ratio, SR; Water Index, WI; and Normalised Difference Vegetation Index, NDVI) from two active optical reflectance sensors (N-Sensor, Yara; and Greenseeker, Trimble) were compared with manually measured biomass and N-uptake in above-ground foliage. There were three measurements over time, from pastures that had received different N fertiliser applications rates (0, 10, 20, 40 and 80 kg N/ha). It was found that the sensors were able to detect differences in biomass and foliar N-uptake following defoliation of grazed pastures. The tested optical sensors have the potential to inform a real-time variable rate fertiliser application system. Keywords: pasture, nitrogen, optical sensors

scholarly journals Mapping and Tracking Forest Burnt Areas in the Indio Maiz Biological Reserve Using Sentinel-3 SLSTR and VIIRS-DNB Imagery

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5423
Author(s):  
Shou-Hao Chiang ◽  
Noel Ivan Ulloa
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Burnt Area ◽  
Water Index ◽  
Multiple Sensors ◽  
Biological Reserve

Wildfires are considered one of the most major hazards and environmental issues worldwide. Recently, Earth observation satellite (EOS) sensors have proven to be effective for wildfire detection, although the quality and usefulness of the data are often hindered by cloud presence. One practical workaround is to combine datasets from multiple sensors. This research presents a methodology that utilizes data of the recently-launched Sentinel-3 sea and land surface temperature radiometer (S3-SLSTR) to reflect its applicability for detecting wildfires. In addition, visible infrared imaging radiometer suite day night band (VIIRS-DNB) imagery was introduced to assure day-night tracking capabilities. The wildfire event in the Indio Maiz Biological Reserve, Nicaragua, during 3–13 April 2018, was the study case. Six S3-SLSTR images were processed to compute spectral indices, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized burn ratio (NBR), to perform image segmentation for estimating the burnt area. The results indicate that 5870.7 ha of forest was affected during the wildfire, close to the 5945 ha reported by local authorities. In this study, the fire expansion was delineated and tracked in the Indio Maiz Biological Reserve using a modified fast marching method on nighttime-sensed temporal VIIRS-DNB. This study shows the importance of S3-SLSRT for wildfire monitoring and how it can be complemented with VIIRS-DNB to track burning biomass at daytime and nighttime.

Plant-insect relationships between common milkweed Asclepias syriaca (Gentianales: Asclepiadacees) and the European skipper Thymelicus lineola (Lepidoptera: Hesperiidae)

1977 ◽  
Vol 55 (11) ◽  
pp. 1553-1555 ◽  
Author(s):  
Jeremy N. McNeil
Keyword(s):  
Insect Pest ◽  
Asclepias Syriaca ◽  
The Common ◽  
Common Milkweed Asclepias Syriaca

European skipper adults aggregate on common milkweed and their legs and (or) proboscis often become entangled in the flowers. Occasionally this results in the loss of legs or in the death of the insect. Normally the insects escape unharmed and frequently carry with them milkweed pollinia, attached to the extremity of their tarsi. Thus the European skipper, an insect pest, may act as a pollinating agent of a weed pest, the common milkweed.

scholarly journals Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

2020 ◽  
Vol 12 (5) ◽  
pp. 2099 ◽  
Author(s):  
Xiaobo Zhu ◽  
Honglin He ◽  
Mingguo Ma ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Northern China ◽  
Systematic Evaluation ◽  
Temperate Grasslands ◽  
Water Index ◽  
Stacked Autoencoders ◽  
Machine Learning Model

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

scholarly journals Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest

2020 ◽  
Vol 12 (6) ◽  
pp. 907 ◽  
Author(s):  
Teodoro Semeraro ◽  
Andrea Luvisi ◽  
Antonio O. Lillo ◽  
Roberta Aretano ◽  
Riccardo Buccolieri ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Vegetation Indices ◽  
Atlantic Multidecadal Oscillation ◽  
Enso Event ◽  
Amazon Forest ◽  
Ecosystem Response ◽  
Water Index

Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.

A Study of MODIS-Based Ecosystem Respiration Model in a Semi-Arid Grassland of Inner Mongolia

2020 ◽  
Author(s):  
Haimei Jiang ◽  
Haotian Ye ◽  
Yong Hao
Keyword(s):  
Land Surface ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Water Index ◽  
Growing Seasons ◽  
Land Surface Water ◽  
Respiration Model ◽  
The Difference ◽  
Semi Arid

<p>Eddy covariance data from Xilinhaote National Climatological Observatory in Xilin Gol League during growing seasons of 2010—2013 as well as MODIS data were used to validate an ecosystem respiration model based on enhanced vegetation index (EVI), land surface water index (LSWI) and land surface temperature (LST) in a semi-arid grassland of Inner Mongolia. The limitations of this remote sensing respiration model were also discussed. The results indicate that this model can successfully simulate the variations of nocturnal ecosystem respiration (Reco) in the growing seasons and between different years. The simulated nocturnal Reco also agreed remarkably with the observed Reco (R2=0.90, RMSE=0.02 mgCO2/(m2·s)). Moreover, the observed nocturnal Reco showed a good linear correlation with EVIs×Ws (R2=0.63), in which EVIs and Ws are response functions of EVI and LSWI on photosynthesis, respectively. The response of nocturnal Reco to LST was also found following the L-T equation (R2=0.39). In addition, the difference between responses of nocturnal Reco to EVIs×Ws and LST in the early, middle and late stages of the growing season is indicated as one principal source of the deviations of model results.</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

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