Damage assessment of chilli thrips using high resolution multispectral satellite data

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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Using improved DeepLabv3+ network integrated with normalized difference water index to extract water bodies in Sentinel-2A urban remote sensing images

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.15.018504 ◽

2021 ◽

Vol 15 (01) ◽

Author(s):

Houcheng Su ◽

Yuhao Peng ◽

Chao Xu ◽

Ao Feng ◽

Tao Liu

Keyword(s):

Remote Sensing ◽

Water Bodies ◽

Remote Sensing Images ◽

Water Index ◽

Urban Remote Sensing ◽

Normalized Difference Water Index ◽

Sentinel 2A

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Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake

Remote Sensing ◽

10.3390/rs12244098 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4098

Author(s):

Weixiao Han ◽

Chunlin Huang ◽

Hongtao Duan ◽

Juan Gu ◽

Jinliang Hou

Keyword(s):

Surface Water ◽

Random Forest ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Qinghai Lake ◽

Water Index ◽

Freeze Thaw ◽

Normalized Difference Water Index ◽

Break Up

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

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

Remote Sensing ◽

10.3390/rs12060907 ◽

2020 ◽

Vol 12 (6) ◽

pp. 907 ◽

Cited By ~ 1

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.

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

REKA GEOMATIKA ◽

10.26760/rg.v2017i2.1768 ◽

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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A Survey on Global Thresholding Methods for Mapping Open Water Body Using Sentinel-2 Satellite Imagery and Normalized Difference Water Index

Archives of Computational Methods in Engineering ◽

10.1007/s11831-020-09416-2 ◽

2020 ◽

Cited By ~ 2

Author(s):

Aliihsan Sekertekin

Keyword(s):

Satellite Imagery ◽

Water Body ◽

Open Water ◽

Water Index ◽

Normalized Difference Water Index ◽

Global Thresholding ◽

Open Water Body ◽

Sentinel 2

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Variations in Surface Albedo Arising from Flooding and Desiccation Cycles on the Bonneville Salt Flats, Utah

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0219.1 ◽

2019 ◽

Vol 58 (4) ◽

pp. 773-785 ◽

Cited By ~ 2

Author(s):

Kevin M. Craft ◽

John D. Horel

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Rainfall Runoff ◽

Water Index ◽

Mountain Ranges ◽

Salt Flats ◽

Normalized Difference Water Index ◽

Northern Utah ◽

Flooding Events ◽

Topographic Relief

AbstractDesert playas, such as those in northern Utah, form a landscape often in stark contrast to surrounding mountain ranges due to their minimal topographic relief, lack of vegetation, and saline soils. Dry highly reflective halite surfaces, which make up many of the desert playas in northern Utah, are generally characterized by a surface albedo over 40%. However, their albedo can be reduced abruptly to less than 20% by flooding due to rainfall, runoff from surrounding higher terrain, or surface winds transporting shallow water across the playas. A weather station installed during September 2016 to study the Bonneville Salt Flats (BSF) in northern Utah provides estimates of surface albedo that can be related to cycles of flooding and desiccation of the halite surface. The normalized difference water index (NDWI) derived from the MODIS MOD09A1 land surface reflectance product estimates the fractional coverage of surface water over the BSF. NDWI values computed over 8-day periods from 2000 to 2018 highlight year-to-year and seasonal variations in playa flooding events over the BSF. Periods of playa flooding were observed with both ground-based observations and NDWI with sharp reductions in albedo when the surface is flooded.

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Assessing the Impacts of the 2009/2010 Drought on Vegetation Indices, Normalized Difference Water Index, and Land Surface Temperature in Southwestern China

Advances in Meteorology ◽

10.1155/2017/6837493 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Xiaoqiang Zhang ◽

Yasushi Yamaguchi ◽

Fei Li ◽

Bin He ◽

Yaning Chen

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Deciduous Forest ◽

Southwestern China ◽

Evergreen Forest ◽

Drought Period ◽

Water Index ◽

Normalized Difference Water Index

Droughts are projected to increase in severity and frequency on both regional and global scales. Despite the increasing occurrence and intensity of the 2009/2010 drought in southwestern China, the impacts of drought on vegetation in this region remain unclear. We examined the impacts of the 2009/2010 drought in southwestern China on vegetation by calculating the standardized anomalies of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST). The standardized anomalies of NDVI, EVI, and NDWI exhibited positively skewed frequency distributions, while the standardized anomalies of LST exhibited a negatively skewed frequency distribution. These results implied that the NDVI, EVI, and NDWI declined, while LST increased in the 2009/2010 drought-stricken vegetated areas during the drought period. The responses of vegetation to the 2009/2010 drought differed substantially among biomes. Savannas, croplands, and mixed forests were more vulnerable to the 2009/2010 drought than deciduous forest and grasslands, while evergreen forest was resistant to the 2009/2010 drought in southwestern China. We concluded that the 2009/2010 drought had negative impacts on vegetation in southwestern China. The resulting assessment on the impacts of drought assists in evaluating and mitigating its adverse effects in southwestern China.

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