To see the other types of publications on this topic, follow the link: Remote sensing and vegetation condition index (VCI).
Journal articles on the topic 'Remote sensing and vegetation condition index (VCI)'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Remote sensing and vegetation condition index (VCI).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Andujar, Erika, Nir Y. Krakauer, Chuixiang Yi, and Felix Kogan. "Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing." Advances in Meteorology 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8434020.
Full textAbstract:
Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.
2
Mutowo, Godfrey, and David Chikodzi. "Remote sensing based drought monitoring in Zimbabwe." Disaster Prevention and Management 23, no. 5 (October 28, 2014): 649–59. http://dx.doi.org/10.1108/dpm-10-2013-0181.
Full textAbstract:
Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.
3
Dubey, S. K., A. Gavli, and S. S. Ray. "VEGETATION CONDITION INDEX: A POTENTIAL YIELD ESTIMATOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 211–15. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-211-2019.
Full textAbstract:
<p><strong>Abstract.</strong> Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. Current study explored a remote sensing-based approach of predicting the yield of Wheat, Kharif Rice and Rabi Rice at district level, using Vegetation Condition Index (VCI), under the FASAL programme. In order to make the estimates 14-years’ historical database (2003&ndash;2016) of NDVI was used to derive the VCI. The yield estimation was carried out for 335 districts (136 districts of Wheat, 23 districts of Rabi Rice and 159 districts of Kharif Rice) for the period of 2016&ndash;17. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of peak growing period of crop were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of crops. Estimated yields are good in agreement with the actual district level yield with the R<sup>2</sup> of, 0.78 for Wheat, 0.52 for Rabi Rice and 0.69 for Kharif Rice. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level crop yield. Though there was variation in model performance in different states and crops, overall, the study showed the usefulness of VCI, which can be used as an input for operational crop yield forecasting, at district level.</p>
4
Filgueiras, Roberto, Donizeti Aparecido Pastori Nicolete, Antonio Ribeiro Cunha, and Célia Regina Lopes Zimback. "VARIAÇÃO ESPAÇO-TEMPORAL DA CONDIÇÃO DA VEGETAÇÃO NO INTERIOR PAULISTA." Nativa 7, no. 5 (September 12, 2019): 582. http://dx.doi.org/10.31413/nativa.v7i5.7121.
Full textAbstract:
A compreensão da dinâmica das condições da vegetação ao longo do tempo tem sido ampliada devido ao avanço das técnicas de sensoriamento remoto. O presente trabalho teve como objetivo analisar a dinâmica espaço-temporal da vegetação estratificada por diferentes usos e cobertura da terra, em área localizada no município de Botucatu-SP. Para isso, foram utilizadas 21 imagens da plataforma Landsat-5/TM, totalizando uma série temporal de 25 anos (1985 a 2010). As imagens foram submetidas aos processos de conversão dos números digitais para valores físicos, correção atmosférica e correção topográfica. As imagens corrigidas foram utilizadas para estimar os valores do VCI (Vegetation Condition Index). Os resultados da estimativa do VCI foram promissores para subsidiar a análise espaço-temporal da condição da vegetação em nível local, sendo sensível às variações locais de precipitação pluviométrica, amplificando a variabilidade intra-classe de uso da terra para o vigor da vegetação. O comportamento característico da floresta semidecidual (classe de uso da terra = Floresta) presente na propriedade foi perceptível nessa análise, fato que faz com se recomende essa metodologia em pesquisas futuras relacionadas a análise da condição da vegetação.Palavras-chave: monitoramento ambiental; sensoriamento remoto; séries temporais. SPATIAL AND TEMPORAL CHANGES IN VEGETATION CONDITIONS IN PAULIST INTERIOR ABSTRACT: There is an increasing demand to better understand the dynamics of the vegetation conditions over time as a result of the improvement of remote sensing techniques. Yhis study aimed to analyse the spatio-temporal behavior of vegetation, stratified by land use in area located in Botucatu-SP. We sused21 Landsat-5 TM images in 25 years (1985 – 2010) of analysis. We applied conversion of the digital numbers to physical values, atmospheric and topographic corrections, which allowed to analyze the vegetation changes by using the VCI (Vegetation Condition Index) calculation. The VCI showed a good performance in analyzing the spatiotemporal vegetation condition at a local level, it is sensitive to local variations of rainfall, it enhances the variability of the intra-class land use for the vigor of vegetation. By applying the VCI we were able to observe the vegetation pattern of the semideciduous forest (land use class = Forest) present in the area, fact that makes this methodology recommended in future researches related to the analysis of the vegetation condition.Keywords: environmental monitoring; remote sensing; time series.
5
Sur, Chanyang, Dongkyun Kim, Joo-Heon Lee, Muhammad Mazhar Iqbal, and Minha Choi. "Hydrological Drought Assessment of Energy-Based Water Deficit Index (EWDI) at Different Geographical Regions." Advances in Meteorology 2019 (May 29, 2019): 1–11. http://dx.doi.org/10.1155/2019/8512727.
Full textAbstract:
This study applied the remote sensing-based drought index, namely, the Energy-Based Water Deficit Index (EWDI), across Mongolia, Australia, and Korean Peninsula for the period between 2000 and 2010. The EWDI is estimated based on the hydrometeorological variables such as evapotranspiration, soil moisture, solar radiation, and vegetation activity which are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) imageries. The estimated EWDI was compared with the Evaporative Stress Index (ESI), the Vegetation Condition Index (VCI), and the Standardized Precipitation Index (SPI). The correlation coefficients between the drought indices are as follows: 0.73–0.76 (EWDI vs ESI), 0.64–0.71 (EWDI vs VCI), 0.54–0.64 (EWDI vs SPI-3), 0.69–0.71 (ESI vs VCI), 0.55–0.62 (ESI vs SPI-3), and 0.53–0.57 (VCI vs SPI-3). The drought prediction accuracy of each index according to error matrix analysis is as follows: 83.33–94.17% (EWDI), 70.00–91.67% (ESI), 47.50–85.00% (VCI), and 61.67–88.33% (SPI-3). Based on the results, the EWDI and ESI were found to be more accurate in capturing moderate drought conditions than the SPI at different geographical regions.
6
Han, Yang, Ziying Li, Chang Huang, Yuyu Zhou, Shengwei Zong, Tianyi Hao, Haofang Niu, and Haiyan Yao. "Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices." Remote Sensing 12, no. 3 (February 6, 2020): 530. http://dx.doi.org/10.3390/rs12030530.
Full textAbstract:
Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.
7
Macarof, Paul, Florian Statescu, Cristian Iulian Birlica, and Paul Gherasim. "IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY." Present Environment and Sustainable Development 13, no. 2 (October 15, 2019): 179–86. http://dx.doi.org/10.15551/pesd2019132013.
Full textAbstract:
In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.
8
Faridatul, Mst Ilme, and Bayes Ahmed. "Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach." Remote Sensing 12, no. 20 (October 15, 2020): 3363. http://dx.doi.org/10.3390/rs12203363.
Full textAbstract:
Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.
9
SHAHABFAR, A., and J. EITZINGER. "Agricultural drought monitoring in semi-arid and arid areas using MODIS data." Journal of Agricultural Science 149, no. 4 (January 18, 2011): 403–14. http://dx.doi.org/10.1017/s0021859610001309.
Full textAbstract:
SUMMARYThe performances of two remote sensing drought indices were evaluated at selected agricultural sites in different agro-climatic zones in Iran to detect the severity of drought phenomena related to temporal variation and different climatic conditions. The indices used were the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), which are derived from moderate resolution imaging spectroradiometer (MODIS) satellite images (MOD13A3 V005). The correlations between these perpendicular indices and two other remote sensing indices in ten different agro-climatic zones of Iran from February 2000 to December 2005 were analysed. The additional indices evaluated were the enhanced vegetation index (EVI) and the vegetation condition index (VCI) along with five water balance parameters, including climatic water balance (CL), crop water balance (CR), monthly reference crop evapotranspiration (ET0), crop evapotranspiration (ETc) and required irrigation water (I). Winter wheat was selected as the reference crop because it is grown in the majority of climatic conditions in Iran.The results show that in several climatic regions, there is a statistically significant correlation between PDI and MPDI and the water balance parameters, indicating an acceptable performance in detecting crop drought stress conditions. In all zones except at the sites located in northwest and northeast of Iran, VCI and EVI are less correlated with the applied water balance indicators compared to PDI and MPDI. In a temporal analysis, PDI and MPDI showed a greater ability to detect CR conditions than VCI and EVI in the most drought-sensitive winter wheat-growing stages. Since Iran is characterized by arid or semi-arid climatic conditions and winter wheat is a major agricultural crop, a combination of both PDI and MPDI could be used as simple remote sensing-based tool to map drought conditions for crops in Iran and in other developing countries with similar climatic conditions.
10
Sur, Koyel, and M. M. Lunagaria. "Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India." Journal of Water and Climate Change 11, S1 (April 29, 2020): 189–202. http://dx.doi.org/10.2166/wcc.2020.157.
Full textAbstract:
Abstract Drought is a complex hazard which directly affects the water balance of any region. It impacts agricultural, ecological and socioeconomical spheres. It is a global concern. The occurrence of drought is triggered by climatic phenomena which cannot be eliminated. However, its effect can be well managed if actual spatio-temporal information related to crop status influenced by drought is available to decision-makers. This study attempted to assess the efficiency of remote sensing products from space sensors for monitoring the spatio-temporal status of meteorological drought in conjunction with impact on vegetation condition and crop yield. Time series (2000–2019) datasets of the Tropical Rainfall Measuring Mission (TRMM) were used to compute Standardized Precipitation Index (SPI) and MODIS (MODerate resolution Imaging Spectroradiometer) was used to compute Vegetation Condition Index (VCI). Association between SPI and VCI was explored. YAI was calculated from the statistical data records. Final observations are that the agricultural crop yield changed as per the climate variability specific to location. The study indicates drought indices derived from remote sensing give a synoptic view because of the course resolution of the satellite images. It does not reveal the precise relationship to the small-scale crop yield. Remote sensing can be an effective way to monitor and understand the dynamics of the drought and agriculture pattern over any region.
11
Rimkus, Egidijus, Edvinas Stonevicius, Justinas Kilpys, Viktorija Maciulyte, and Donatas Valiukas. "Drought identification in the eastern Baltic region using NDVI." Earth System Dynamics 8, no. 3 (July 17, 2017): 627–37. http://dx.doi.org/10.5194/esd-8-627-2017.
Full textAbstract:
Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53–60° N and 20–30° E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1- to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5–6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3- and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.
12
Zhao, Xiaoyang, Haoming Xia, Li Pan, Hongquan Song, Wenhui Niu, Ruimeng Wang, Rumeng Li, Xiqing Bian, Yan Guo, and Yaochen Qin. "Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine." Remote Sensing 13, no. 18 (September 18, 2021): 3748. http://dx.doi.org/10.3390/rs13183748.
Full textAbstract:
Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.
13
Yu, Haozhe, Lijuan Li, Yang Liu, and Jiuyi Li. "Construction of Comprehensive Drought Monitoring Model in Jing-Jin-Ji Region Based on Multisource Remote Sensing Data." Water 11, no. 5 (May 23, 2019): 1077. http://dx.doi.org/10.3390/w11051077.
Full textAbstract:
Drought is a complex hazard that has more adverse effects on agricultural production and economic development. Studying drought monitoring techniques and assessment methods can improve our ability to respond to natural disasters. Numerous drought indices deriving from meteorological or remote sensing data are focused mainly on monitoring single drought response factors such as soil or vegetation, and the ability to reflect comprehensive information on drought was poor. This study constructed a comprehensive drought-monitoring model considering the drought factors including precipitation, vegetation growth status, and soil moisture balance during the drought process for the Jing-Jin-Ji region, China. The comprehensive drought index of remote sensing (CDIR), a drought indicator deduced by the model, was composed of the vegetation condition index (VCI), the temperature condition index (TCI), and the precipitation condition index (PCI). The PCI was obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite. The VCI and TCI were obtained from a moderate-resolution imaging spectroradiometer (MODIS). In this study, a heavy drought process was accurately explored using the CDIR in the Jing-Jin-Ji region in 2016. Finally, a three-month scales standardized precipitation index (SPI-3), drought affected crop area, and standardized unit yield of wheat were used as validation to evaluate the accuracy of this model. The results showed that the CDIR is closely related to the SPI-3, as well as variations in the drought-affected crop area and standardized unit yield of crop. The correlation coefficient of the CDIR with SPI-3 was between 0.45 and 0.85. The correlation coefficient between the CDIR and drought affected crop was between −0.81 and −0.86. Moreover, the CDIR was positively correlated with the standardized unit yield of crop. It showed that the CDIR index is a decent indicator that can be used for integrated drought monitoring and that it can synthetically reflect meteorological and agricultural drought information.
14
Rawat, Kishan Singh, Anil Kumar Mishra, Rakesh Kumar, and Jitendra Singh. "Vegetation condition index pattern (2002-2007) over Indian agro-climate regions, using of GIS and SPOT sensor NDVI data." Journal of Applied and Natural Science 4, no. 2 (December 1, 2012): 214–19. http://dx.doi.org/10.31018/jans.v4i2.252.
Full textAbstract:
This study describes the Vegetation Condition Index in the near-real-time with help of SPOT based Normalized Difference Vegetation Index (NDVI) for Agro climatic-region of India and gave the development pattern in last six year (2002-2007) over the study area of India using decadal time data set from SPOT satellite sensor for 2002-2007 time periods. The each Agro-climatic region of study, 1°x1° degree in area, part of India agro-climate regions, has been taken for analysis using remote sensing and Geographical Information System (RS and GIS)methods, SPOT satellite sensor NDVI data, and from processed data set (geo-referenced data set), cut out 1°x1° degree of area by preparing a layers representing Agro-climatic region of India as base mapping units (BMU),The results indicated that NDVI index is only water stress over vegetation while VCI is an appropriate index for vegetation pattern monitoring over study area. As satellite observations provide better spatial and temporal coverage, the VCI based system will provide efficient tools for management of the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available.
15
Orhan, Osman, Semih Ekercin, and Filiz Dadaser-Celik. "Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/142939.
Full textAbstract:
The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable within situmeasurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about2∘C) in the Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–2∘Cduring the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.
16
Bouras, El houssaine, Lionel Jarlan, Salah Er-Raki, Clément Albergel, Bastien Richard, Riad Balaghi, and Saïd Khabba. "Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco." Remote Sensing 12, no. 24 (December 8, 2020): 4018. http://dx.doi.org/10.3390/rs12244018.
Full textAbstract:
In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.
17
Dutta, Dipanwita, Arnab Kundu, N. R. Patel, S. K. Saha, and A. R. Siddiqui. "Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI)." Egyptian Journal of Remote Sensing and Space Science 18, no. 1 (June 2015): 53–63. http://dx.doi.org/10.1016/j.ejrs.2015.03.006.
Full text
18
Shahzaman, Muhammad, Weijun Zhu, Irfan Ullah, Farhan Mustafa, Muhammad Bilal, Shazia Ishfaq, Shazia Nisar, Muhammad Arshad, Rashid Iqbal, and Rana Waqar Aslam. "Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries." Remote Sensing 13, no. 16 (August 20, 2021): 3294. http://dx.doi.org/10.3390/rs13163294.
Full textAbstract:
The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.
19
Qu, Carolyn, Xianjun Hao, and John J. Qu. "Monitoring Extreme Agricultural Drought over the Horn of Africa (HOA) Using Remote Sensing Measurements." Remote Sensing 11, no. 8 (April 13, 2019): 902. http://dx.doi.org/10.3390/rs11080902.
Full textAbstract:
The Horn of Africa ((HOA), including Djibouti, Eritrea, Ethiopia, and Somalia) has been slammed by extreme drought within the past years, and has become one of the most food-insecure regions in the world. Millions of people in the HOA are undernourished and are at risk of famine. Meanwhile, global climate change continues to cause more extreme weather and climate events, such as drought and heat waves, which have significant impacts on crop production and food security. This study aimed to investigate extreme drought in the Horn of Africa region, using satellite remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), a key instrument onboard the National Aeronautics and Space Administration (NASA) satellites Terra and Aqua, as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data products. Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) data from 2000 to 2017 were derived from the MODIS measurements and analyzed for assessments of the temporal trend of vegetation health and the impacts of extreme drought events. The results demonstrated the severity of vegetation stress and extreme drought during the past decades. From 1998 to 2017, monthly precipitation over major crop growth seasons decreased significantly. From 2001 to 2017, the mean VHI anomaly of HOA cropland decreased significantly, at a trend of −0.2364 ± 0.1446/year, and the mean TCI anomaly decreased at a trend of −0.2315 ± 0.2009/year. This indicated a deterioration of cropland due to drought conditions in the HOA. During most of the crop growth seasons in 2015 and 2016, the VHI values were below the 10-year (2001–2010) average: This was caused by extreme drought during the 2015–2016 El Niño event, one of the strongest El Niño events in recorded history. In addition, monthly VHI anomalies demonstrated a high correlation with monthly rainfall anomalies in July and August (the growth season of major crops in the HOA), and the trough points of the monthly rainfall and VHI anomaly time series of July and August were consistent with the timing of drought events and El Niño events.
20
Aswathi, P. V., B. R. Nikam, A. Chouksey, and S. P. Aggarwal. "ASSESSMENT AND MONITORING OF AGRICULTURAL DROUGHTS IN MAHARASHTRA USING METEOROLOGICAL AND REMOTE SENSING BASED INDICES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (November 15, 2018): 253–64. http://dx.doi.org/10.5194/isprs-annals-iv-5-253-2018.
Full textAbstract:
<p><strong>Abstract.</strong> Drought is a recurring climatic event characterized by slow onset, a gradual increase in its intensity, and persistence for a long period depending upon the availability of water. Droughts, broadly classified into meteorological, hydrological and agricultural drought, which are interconnected to each other. India, being an agriculture based economy depends primarily on agriculture production for its economic development and stability. The occurrence of agriculture drought affects the agricultural yield, which affects the regional economy to a larger extent. In present study, agricultural and meteorological drought in Maharashtra state was monitored using traditional as well as remote sensing methods. The meteorological drought assessment and characterization is done using two standard meteorological drought indices viz. standard precipitation index (SPI) and effective drought index (EDI). The severity and persistency of meteorological drought were studied using SPI for the period 1901 to 2015. However, accuracy of SPI in detection of sub-monthly drought is limited. Therefore, sub-monthly drought is effectively monitored using EDI. The monthly and sub-monthly drought mapped using SPI and EDI, respectively were then compared and assessed. It was concluded that EDI serves as a better indicator to monitor sub-monthly droughts. The agricultural drought monitoring was carried out using the remote sensing based indices such as vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), shortwave angle slope index (SASI) and the index which maps the agricultural drought in a better way was identified. The area under drought as calculated by various agricultural drought indices compared with that of the EDI, it was found that the results of SASI matched with results of EDI. SASI denotes different values for the dry and wet soil and for the healthy and sparse vegetation. SASI monitors the agricultural drought better as compared to other indices used in this study.</p>
21
Tuvdendorj, Battsetseg, Bingfang Wu, Hongwei Zeng, Gantsetseg Batdelger, and Lkhagvadorj Nanzad. "Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia." Remote Sensing 11, no. 21 (November 1, 2019): 2568. http://dx.doi.org/10.3390/rs11212568.
Full textAbstract:
In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.
22
Thilagaraj, P., P. Masilamani, R. Venkatesh, and J. Killivalavan. "GOOGLE EARTH ENGINE BASED AGRICULTURAL DROUGHT MONITORING IN KODAVANAR WATERSHED, PART OF AMARAVATHI BASIN, TAMIL NADU, INDIA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2021 (June 30, 2021): 43–49. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2021-43-2021.
Full textAbstract:
Abstract. The agricultural drought assessment and monitoring has become a prime concern in recent times as it impedes land capability and causes food scarcity. Therefore, the present study constructed a methodological framework through the Google Earth Engine (GEE) platform, which offers advanced and effective monitoring in a timely concern of the drought occurrences. The study has been carried out in the Kodavanar watershed, a part of the Amaravathi basin is noted with signs of drought such as insufficient rainfall and vegetation stress in the current situation. The remote sensing indices are utilised for the agriculture drought assessment including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI) and Vegetation Health Index (VHI). In particular, the VHI results show that the area of healthy vegetation and no drought category is rapidly decreased from 934.29 to 107.83 sq.km across the years and have been reached threatening condition as extreme drought category with extremely low vegetation cover has been increasing in a exponential proportion of over 5% in the year 2019 and 2020. However, the agriculture drought results compared through the meteorological drought indicator of Standardized Precipitation Index (SPI) reflects that the SPI and VHI are reflecting similar signs and indicating the dry condition of precipitation with moderate vegetation over the highlighted regions of northern tip and central eastern portions. This present work illustrates the effective use of the GEE platform in monitoring the agriculture drought and the highlighted portions of the study should be implemented with proper water resource management by the researchers, planners and policymakers in the Kodavanar watershed for reducing the vegetation stress.
23
Bhaga, Trisha Deevia, Timothy Dube, Munyaradzi Davis Shekede, and Cletah Shoko. "Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review." Remote Sensing 12, no. 24 (December 21, 2020): 4184. http://dx.doi.org/10.3390/rs12244184.
Full textAbstract:
Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.
24
Ryu, Jae-Hyun, Kyung-Soo Han, Yang-Won Lee, No-Wook Park, Sungwook Hong, Chu-Yong Chung, and Jaeil Cho. "Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea." Remote Sensing 11, no. 15 (July 27, 2019): 1773. http://dx.doi.org/10.3390/rs11151773.
Full textAbstract:
Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.
25
Li, Weijiao, Yunpeng Wang, and Jingxue Yang. "Cloudy Region Drought Index (CRDI) Based on Long-Time-Series Cloud Optical Thickness (COT) and Vegetation Conditions Index (VCI): A Case Study in Guangdong, South Eastern China." Remote Sensing 12, no. 21 (November 6, 2020): 3641. http://dx.doi.org/10.3390/rs12213641.
Full textAbstract:
Widespread and long-lasting drought disasters can aggravate environmental degradation. They can lead to significant economic losses and even affect social stability. The existing drought index mostly chose arid and semi-arid regions as study areas, because cloudy weather in humid and semi-humid regions hindered the satellite in its attempts to obtain the surface reflectivity. In order to solve this problem, a cloudy region drought index (CRDI) is proposed to estimate the drought of the clouded pixels. Due to the cumulative effect of drought, the antecedent drought index (ADI) has a certain impact on the calculation of the current drought. Furthermore, cloud is the only source of natural precipitation, and it also affects the evaporation and emission process on the ground. Therefore, based on the remote sensing drought index, ADI and cloud optical thickness (COT) are used to estimate the drought of pixels with missing data due to cloud occlusion. In this paper, a case study of the cloudy Guangdong, which is located in a humid area, is presented. First, we calculated the CRDI using Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2003 to 2017, and then discussed the effect of CRDI with the data from 2016 as examples. Through the analysis of the parameters of regression equation, filling efficiency, rationality of the estimated value, the continuity of CRDI and the rationality of CRDI spatial distribution results, it is concluded that CRDI can effectively estimate the drought severity of the cloud-covered pixels, and more comprehensive drought data can be obtained by using CRDI. The successful application of CRDI in Guangdong shows it is robust and flexible, suggesting high efficiency and great potential for further utilization.
26
Dhawale, R., and S. K. Paul. "A COMPARATIVE ANALYSIS OF DROUGHT INDICES ON VEGETATION THROUGH REMOTE SENSING FOR LATUR REGION OF INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 403–7. http://dx.doi.org/10.5194/isprs-archives-xlii-5-403-2018.
Full textAbstract:
<p><strong>Abstract.</strong> Drought intensifies stress on the water resource which is already in a critical condition due to rapid urbanization and population growth thus, affecting people, economy, and environment. The drought conditions are worsening in many parts of India due to deficit rainfall, change in land and surface temperature, and vegetation pattern coupled with mismanagement of water resources and poor governance. The present study conducted for Latur, Marathwada is an agricultural rich land which is severely affected due to prolonged drought conditions. A comparative study is presented using the three drought indices VCI, VHI, TCI to analyze the vegetation condition for drought years. The results through TCI detects the drought only during the dry period or in the months where the temperature is high. The VCI detects drought conditions as more sensitive in wet seasons. The VHI combines both the indicators to give comprehensive results about drought conditions. Further, Land Surface Temperature study is conducted to substantiate the analyzed drought conditions. Our study illustrates that the comparative analysis of various indices represents a better interpretation and monitoring of drought for the areas which are majorly affected due to vegetative drought.</p>
27
Prasetyo, Sri Yulianto Joko, Kristoko Dwi Hartomo, Mila Chrismawati Paseleng, Dian Widiyanto Chandra, and Edi Winarko. "Satellite imagery and machine learning for aridity disaster classification using vegetation indices." Bulletin of Electrical Engineering and Informatics 9, no. 3 (June 1, 2020): 1149–58. http://dx.doi.org/10.11591/eei.v9i3.1916.
Full textAbstract:
Central Java Province is one of provinces in Indonesia that has a high aridity risk index. Aridity disaster risk monitoring and detection can be done more accurately in larger areas and with lower costs if the vegetation index is extracted from the remote sensing imagery. This study aims to provide accurate aridity risk index information using spectral vegetation index data obtained from LANDSAT 8 OLI satellite. The classification of drought risk areas was carried out using k-nn with the Spatial Autocorrelation method. The spectral vegetation indices used in the study are NDVI, SAVI, VHI, TCI and VCI. The results show a positive correlation and trend between the spectral vegetation index influenced by seasonal dynamics and the characteristics of the High R.A. and Middle R.A. drought risk areas. The highest correlation coefficient is SAVI with a High R.A. amounted to 0.967 and Middle R.A. amounted to 0.951. The results of the Kappa accuracy test comparison show that SVM and k-nn have the same accuracy of 88.30. The result of spatial prediction using the IDW method shows that spectral vegetation index data that initially as an outlier, using the k-nn method, the spectral vegetation index data can be identified as data in the aridity classification. The spatial connectivity test among sub-districts that experience drought was done using Moran’s I Analysis.
28
Darra, Nicoleta, Emmanouil Psomiadis, Aikaterini Kasimati, Achilleas Anastasiou, Evangelos Anastasiou, and Spyros Fountas. "Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards." Agronomy 11, no. 4 (April 11, 2021): 741. http://dx.doi.org/10.3390/agronomy11040741.
Full textAbstract:
Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas.
29
Zagade, Nayan, Ajaykumar Kadam, Bhavana Umrikar, and Bhagyashri Maggirwar. "Remote Sensing Based Assessment of Agricultural Droughts in Sub-Watersheds of Upper Bhima Basin, India." Remote Sensing of Land 2, no. 2 (July 12, 2019): 105–11. http://dx.doi.org/10.21523/gcj1.18020204.
Full textAbstract:
Drought assessment for agricultural sector is vital in order to deal with the water scarcity in Ahmednagar and Pune districts, particularly in sub-watersheds of upper catchment of the River Bhima. Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite data (2000, 2002, 2009, 2014, 2015 and 2017) for the years receiving less rainfall have been procured and various indices were computed to understand the intensity of agricultural droughts in the area. Vegetation health index (VHI) is computed on the basis of vegetation moisture, vegetation condition and land surface temperature condition. Most of the reviewed area shows moderate to extreme drought conditions.
30
Giovos, Rigas, Dimitrios Tassopoulos, Dionissios Kalivas, Nestor Lougkos, and Anastasia Priovolou. "Remote Sensing Vegetation Indices in Viticulture: A Critical Review." Agriculture 11, no. 5 (May 18, 2021): 457. http://dx.doi.org/10.3390/agriculture11050457.
Full textAbstract:
One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.
31
Sun, Xiaofang, Meng Wang, Guicai Li, and Yuanyuan Wang. "Regional-scale drought monitor using synthesized index based on remote sensing in northeast China." Open Geosciences 12, no. 1 (June 24, 2020): 163–73. http://dx.doi.org/10.1515/geo-2020-0037.
Full textAbstract:
AbstractDrought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China.
32
Golubeva, E. I., M. V. Zimin, O. V. Tutubalina, Y. I. Timokhina, and A. S. Azarova. "LEAF AREA INDEX: METHODS OF FIELD INSTRUMENTAL MEASUREMENTS AND USING REMOTE SENSING MATERIALS." ECOLOGY ECONOMY INFORMATICS. GEOINFORMATION TECHNOLOGIES AND SPACE MONITORING 2, no. 5 (2020): 70–74. http://dx.doi.org/10.23885/2500-123x-2020-2-5-70-74.
Full textAbstract:
The article discusses the methods of field instrumental measurements and the use of remote sensing materials for measuring the leaf area index of vegetation cover, their capabilities and limitations, verified during research in northern forests. The leaf area index LAI is the ratio of the area of leaves (one of their sides) and/or needles of all plants to the soil area occupied by a given ecosystem. LAI is an important parameter that reflects material and energy metabolism in the processes of photosynthesis, respiration, the cycle of carbon and plant nutrients in ecosystems, predicting their growth and productivity. LAI is a key variable functionally related to phytomass production, water cycle and nutrient cycle under specific microclimatic conditions. A reliable estimate of LAI is one of great importance for monitoring and analyzing various biophysical processes in ecosystems; it is a complex indicator that quantitatively reflects the closeness of the tree canopy, the projective cover of shrub, grass, moss-lichen layers of natural ecosystems or crops of agrocenoses. The LAI is used in environmental studies aimed at studying the state of the vegetation cover for solving scientific and applied problems. The global LAI is 4.5, its values depend both on natural conditions, mainly hydrothermal, and on the characteristics and degree of anthropogenic impact. The theoretical foundations of studying LAI and various methods of its measurement are considered. In field studies, LAI is determined either with the help of special devices or in a destructive way: the assimilation area of leaves is determined by the method of “incisions” of a fixed area, and the accumulation of dry matter – by the gravimetric method, followed by drying of the vegetative mass of plants to an air-dry or absolutely dry state... The use of remote sensing data is very promising to determine LAI of various types of vegetation without removing phytomass and to assess their condition.
33
Radeva, Kameliya, Emiliya Velizarova, and Adlin Dancheva. "Land cover monitoring as part of a survey on wetland ecosystem conservation in the Negovan village area using remote sensing tools." Bulletin of the Faculty of Forestry, no. 119 (2019): 175–88. http://dx.doi.org/10.2298/gsf1919175r.
Full textAbstract:
The main purpose of the present survey is to apply remote sensing data to the investigation of different components of a wetland ecosystem, situated in the area of the village of Negovan (Sofia region), such as soil, vegetation and water, and their variation for certain temporal intervals including the vegetation period. This survey represents the process of interim ecological monitoring (IEM) implementation on the studied ecosystem. Data for the current condition of different ecosystem components - soil, vegetation and water components, and their variations within the selected time period of 5 years (2014-2018) have been obtained. Specific relations among wetland actual components conditions such as soil wetness and vegetation vs climate factors within the respective temporal intervals of wetland monitoring process have been established. Aerospace data with different temporal, space and spectral resolution, satellite data from Sentinel 2, MSI and aerophoto with a very high resolution have been used. The results for ?Brightness?, ?Greenness? and ?Wetness? components obtained on the basis of orthogonalization of satellite data from Sentinel 2 have been introduced. The results reflect the value of Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI 2), Normalized Difference Greenness Index (NDGI) and Normalized Difference Water Index (NDWI), which are of great importance for the relationship between soil health indexes and ecosystem sustainability. Thematic maps are generated based on the results obtained by surveying land cover components. Data received for the current condition of Negovan wetland ecosystem and established variations of different parameters, including soil component could be used while assessing wetland ecosystem services.
34
Biswal, A., B. Sahay, K. V. Ramana, S. V. C. K. Rao, and M. V. R. Sesha Sai. "Relationship between AWiFS derived Spectral Vegetation Indices with Simulated Wheat Yield Attributes in Sirsa district of Haryana." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 689–94. http://dx.doi.org/10.5194/isprsarchives-xl-8-689-2014.
Full textAbstract:
Satellite remote sensing can provide information on plant status for large regions with high temporal resolution and proved as a potential tool for decision support. It allows accounting for spatial and temporal variations of state and driving variables, influencing crop growth and development, without extensive ground surveys. The crop phenological development and condition can be monitored through multi-temporal reflectance profiles or multi-temporal vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). At the same time, Process based dynamic crop growth simulation models are useful tools for estimating crop growth condition and yield on large spatial domains if their parameters and initial conditions are known for each point. Therefore, combined approaches integrating remote sensing and dynamic crop growth models for regional yield prediction have been developed in several studies. In these models the vegetation state variables, e.g., development phase, dry mass, LAI are linked to driving variables, e.g., weather condition, nutrient availability and management practices. Output of these models is usually final yield or accumulated biomass. The model outputs are a summary containing an overview of the main development events, water and nitrogen variables, yield and yield components. In the present work, IRS P6 AWiFS derived vegetation indices like NDVI and NDWI are computed to study the growth profile of wheat crop in Sirsa district of Haryana along with crop growth simulation model DSSAT-CERES from 2008–09 to 2012–13.several iteration of wheat crop simulation are carried out with four sowing dates and four soil types varying with respect to the fertility parameters to represent the average simulation environment of Sirsa district in Haryana state of India. Four years time series NDVI and NDWI are used to establish the correlation between the spectral vegetation indices and simulated wheat yield attributes at critical growth stages of wheat. This work is a basic investigation towards assimilation of remote sensing derived state variables in to the crop growth model.
35
Shofiyati, Rizatus, Wataru Takeuchi, Soni Darmawan, and Parwati Sofan. "AN EFFECTIVE INFORMATION SYSTEM OF DROUGHT IMPACT ON RICE PRODUCTION BASED ON REMOTE SENSING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 11, no. 2 (April 12, 2017): 153. http://dx.doi.org/10.30536/j.ijreses.2014.v11.a2613.
Full textAbstract:
Long droughts experienced in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. MODIS, MTSAT, AMSR-E, TRMM and GSMaP have been used in this activity. Meteorological drought index (SPI) of the daily and monthly rainfall data from TRMM and GSMaP have analyzed for last 10-year period. While, agronomic drought index has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS, MTSAT, and AMSR-E data at a period of 4 years. Network for satellite data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, University of Tokyo (technical supporter), and NASA. Two information system have been developed: 1) agricultural drought using the model developed by LAPAN, and 2) meteorological drought developed by Takeuchi (University of Tokyo).The accuracy study using quantitative method for LAPAN model uses VHI is 60% (Kappa 0,44), while that of for University of Tokyo model uses qualitative model with KBDI value 500-600 shows an early indication of drought for paddy field. This will help the government or field officers in rapid management actions for the indicated drought area.This paper describes the implementation and dissemination of drought impact monitoring model on the area of rice production center using an integrated information system satellite based model. The two developed information systems are effective for spatially dissemination of drought information.
36
Lupa, Michał, Katarzyna Adamek, Renata Stypień, and Wojciech Sarlej. "Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake." E3S Web of Conferences 63 (2018): 00017. http://dx.doi.org/10.1051/e3sconf/20186300017.
Full textAbstract:
The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.
37
Komarudin, R. Ade, Aris Kabul Pranoto, Dian Sutono, and Anthon Anthonny Djari. "Study of Mangrove Forest Existing Condition using Remote Sensing Image in The Karawang Coast of 2018." PELAGICUS 2, no. 1 (January 27, 2021): 37. http://dx.doi.org/10.15578/plgc.v2i1.8932.
Full textAbstract:
ABSTRACTThe northern part of Karawang is a coastal area with mostly mud-sand substrates. This substrate tends to be unstable, so that naturally, this kind of sediment is supported by coastal vegetation that forms coastal ecosystems, such as mangroves; therefore, the importance of mangroves in Karawang coast is definite. Unfotunately the data regarding the condition of mangroves in Karawang Regency is quite insufficient. This information, especially about its existence, is needed as a database for further research and as basis to support government policies on coastal area management. The aim of this research is to provide information about the existence of mangrove in Karawang Regency. The method is by using Normalized Different Vegetation Index (NDVI) calculations on Landsat 8 2018 satellite imagery of Karawang to get the data that reveal the information. We have discovered that the existing of mangroves in Karawang Regency in 2018 is 305,14 Ha. Border coast that is vegetated is only 33.75 km of 77 km long coastline of Karawang. Only less than 5% of the total mangrove protected area in Karawang Regency is detected as mangrove from the total 9.055 Ha of the area.
38
Susantoro, Tri Muji, Ketut Wikantika, Agung Budi Harto, and Deni Suwardi. "Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)." HAYATI Journal of Biosciences 26, no. 3 (December 2, 2019): 117. http://dx.doi.org/10.4308/hjb.26.3.117.
Full textAbstract:
This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.
39
Sari, Nurwita Mustika, and Dony Kushardono. "QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA." Geoplanning: Journal of Geomatics and Planning 3, no. 2 (October 25, 2016): 93. http://dx.doi.org/10.14710/geoplanning.3.2.93-106.
Full textAbstract:
High-resolution remote sensing data as the acquisition result of LAPAN Surveillance Aircraft (LSA) has the potential to analyze urban areas. The purpose of this study was to develop a method of LSA multispectral data utilization with an analysis of the single tree object in urban areas with OBIA and vegetation index. The method proposed in this study is a hierarchical classification to obtain the specific tree object that will be used further to analyze the quality of vegetation. In particular, analysis of the vegetation quality on the tree object was carried out by calculating the value of vegetation index NDVI. As a result, the overall accuracy of the hierarchical classification of objects in urban areas reached 88 %. In conclusion, the analysis of the quality of vegetation NDVI has been able to perceive the condition of trees in the urban area.
40
Kurbanov, R. K., and N. I. Zakharova. "Application of Vegetation Indexes to Assess the Condition of Crops." Agricultural Machinery and Technologies 14, no. 4 (December 18, 2020): 4–11. http://dx.doi.org/10.22314/2073-7599-2020-14-4-4-11.
Full textAbstract:
Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.
41
Siwi, Sukentyas Estuti. "EFEKTIVITAS TRANSFORMASI INDEKS VEGETASI PENEKAN PENGARUH ATMOSFER BERBASIS CITRA SPOT-6 UNTUK ESTIMASI PRODUKSI TANAMAN KELAPA SAWIT (Elaeis Guineensis Jacq) DI SEBAGIAN KABUPATEN INDRAGIRI HULU, RIAU." MAJALAH ILMIAH GLOBE 19, no. 1 (April 28, 2017): 11. http://dx.doi.org/10.24895/mig.2017.19-1.470.
Full textAbstract:
<p class="JudulABSInd"><strong> ABSTRAK</strong></p><p class="abstrak">Kondisi atmosfer cukup signifikan untuk mempengaruhi nilai refleksi objek dari data penginderaan jauh, akibatnya mempengaruhi ekstraksi informasi dari data penginderaan jauh termasuk perkiraan produksi tanaman. Penelitian ini bertujuan untuk menilai kemampuan transformasi indeks vegetasi dasar (generik) yakni <em>Ratio Vegetation Index</em> (RVI) dan indeks vegetasi yang mampu mengurangi pengaruh kondisi atmosfer yakni <em>Atmospherically Resistant Vegetation Index </em>(ARVI) dan <em>Visible Atmospherically Resistant Index </em>(VARI) untuk memperkirakan produksi tanaman kelapa sawit menggunakan citra SPOT-6. Daerah penelitian di perkebunan kelapa sawit PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu. Metode yang digunakan adalah penerapan transformasi RVI, ARVI dan VARI dan analisis regresi statistik menggunakan citra SPOT-6 tanggal 13 Juni 2013. Metode pemilihan sampel menggunakan stratified random sampling. Analisis regresi dilakukan antara tahun tanaman, indeks vegetasi, dan produksi dari lapangan untuk menghasilkan formula berdasarkan parameter pengaruh produksi, nilai indeks vegetasi dan perkiraan produksi. Hasil penelitian menunjukkan nilai estimasi produksi untuk transformasi RVI adalah 141.710 ton, ARVI adalah 143.317,5 ton dan Vari adalah 148.122,4 ton. Dibandingkan dengan produksi data lapangan sebesar 181.702,6 ton. Akurasi perkiraan produksi sebesar 77,99% dari transformasi RVI, 78,87% dari transformasi ARVI dan 81,52% dari transformasi VARI. Jadi transformasi terbaik untuk memperkirakan produksi kelapa sawit adalah transformasi VARI. Hasil penelitian membuktikan bahwa efek atmosfer pada citra penginderaan jauh dapat ditekan dengan menggunakan ARVI dan VARI transformasi.</p><p><strong>Kata kunci</strong>: transformasi indeks vegetasi, citra SPOT-6, estimasi produksi tanaman, tanaman kelapa sawit</p><p class="judulABS"> <em> ABSTRACT</em></p><p class="Abstrakeng"><em>The atmosphere condition is quite significant to influence the object reflection value from the remote sensing data, so it can be affecting the information extraction from a remote sensing data including crop production estimates. This study aims to assess the ability of the transformation of vegetation index base (generic) is Ratio Vegetation Index (RVI) and the vegetation index which capable of reducing the influence of the atmosphere condition are Atmospherically Resistant Vegetation Index (ARVI) and Visible atmospherically Resistant Index (VARI) to estimate crop production of oil using SPOT-6 imagery. The study area is an oil palm plantation PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu. The method used role in this research is the remote sensing method by applying RVI, ARVI and VARI transformation and statistical regression analysis on SPOT-6 imagery recorded on 13 June 2013. The sample selection method used was stratified random sampling. Regression analysis was conducted between the year of crops, vegetation index, and also the production from the field to produce a formula based on the parameters the influence of production, vegetation index value and production estimate. Results showed the estimated value of production for the transformation of RVI was 141,710 tons, ARVI was 143.317,5 tons and Vari is 148.122,4 tons. Compared with the data field production amounted to 181.702,6 tons. The accuracy of estimated production amounted to 77,99% from RVI transformation, 78,87% from ARVI transformation and 81,52% from VARI transformation. So the best transformation to estimate production of palm oil is the VARI transformation. The research results prove that the atmosphere effect on the remote sensing image can be suppressed by using the ARVI and VARI transformation.</em></p><p><strong><em>Keywords</em></strong><em>: vegetation index transformation, SPOT-6 image, crop production estimates, palm oil</em></p>
42
Dvorakova, Klara, Pu Shi, Quentin Limbourg, and Bas van Wesemael. "Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues." Remote Sensing 12, no. 12 (June 12, 2020): 1913. http://dx.doi.org/10.3390/rs12121913.
Full textAbstract:
Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2) = 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.
43
Buma, Willibroad Gabila, and Sang-Il Lee. "Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa." Remote Sensing 11, no. 21 (October 29, 2019): 2534. http://dx.doi.org/10.3390/rs11212534.
Full textAbstract:
As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.
44
ZVERKOV, M. S., and S. V. BRYL. "ASSESSMENT OF THE LAND RECLAMATION CONDITION OF THE IRRIGATION AND DRAINAGE SYSTEM USING DATA OF THE EARTH REMOTE SENSING AND DRONE." Prirodoobustrojstvo, no. 2 (2021): 6–16. http://dx.doi.org/10.26897/1997-6011-2021-2-6-16.
Full textAbstract:
The purpose of the work was to survey an area of the irrigation system by means of the earth remote sensing and a drone in accordance with the indicators of the reclamation condition of the governmental land registration. The tasks of the work included obtaining raster images of the field in an orthogonal projection, decryption of the orthophotographic plan and remote sensing images of the Earth in various spectral channels to compare them with the corresponding indicators of the reclamation condition. The method of assessment and indicators of the reclamation condition of the irrigation system are given. Three levels of assessment of the reclamation condition are used: «good», «satisfactory», «unsatisfactory». The Normalized Difference Vegetation Index NDVI, Land Surface Water Index LSWI, and Normalized Differential Salinity Index NDSI are used. The surveyed field is located on the territory of the Kolomna urban district of the Moscow region and is irrigated by a circular drive machine «Reinke»©. As a result, it was found that there are no saline lands («good» reclamation condition), unacceptable ground water depths are observed on 20…25% of the irrigated territory («satisfactory» condition), the area of eroded land was 4.7% («satisfactory» condition). The survey of the part of the irrigation system showed that in 2021 the object had a «satisfactory» reclamation condition. This may indicate to the risk of further reduction of the reclamation condition without appropriate preventive measures. It is noted that the survey of reclamation objects by means of the earth remote sensing and drones should solve the problem of identifying markers indicating the risks of reducing the reclamation condition and the need to conduct research directly on the object, in situ. It is extremely important to analyze the retrospective dynamics and the meteorological situation. In this case, it is possible to make a forecast of changes in the reclamation condition and evaluate the effectiveness of the preventive measures.
45
Prasasti, Indah, Suwarsono, and Nurwita Mustika Sari. "THE EFFECT OF ENVIRONMENTAL CONDITION CHANGES ON DISTRIBUTION OF URBAN HEAT ISLAND IN JAKARTA BASED ON REMOTE SENSING DATA." International Journal of Remote Sensing and Earth Sciences (IJReSES) 12, no. 1 (May 31, 2017): 27. http://dx.doi.org/10.30536/j.ijreses.2015.v12.a2670.
Full textAbstract:
Anthropogenic activities of urban growth and development in the area of Jakarta has caused increasingly uncomfortable climatic conditions and tended to be warmer and potentially cause the urban heat island (UHI). This phenomenon can be monitored by observing the air temperature measured by climatological station, but the scope is relatively limited. Therefore, the utilization of remote sensing data is very important in monitoring the UHI with wider coverage and effective. In addition, the remote sensing data can also be used to map the pattern of changes in environmental conditions (microclimate). This study aimed to analyze the effect of changes in environmental conditions (land use/cover, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI)) toward the spread of the urban heat island (UHI). In this case, the UHI was identified from pattern changes of Land Surface Temperature (LST) in Jakarta based on data from remote sensing. The data used was Landsat 7 in 2007 and Landsat 8 in 2013 for parameter extraction environmental conditions, namely: land use cover, NDVI, NDBI, and LST. The analysis showed that during the period 2007 to 2013, there has been a change in the condition of the land use/cover, impairment NDVI, and expansion NDBI that trigger an increase in LST and the formation of heat islands in Jakarta, especially in the area of business centers, main street and surrounding area, as well as in residential areas.
46
Putera, Rifky, Junaidi Junaidi, and Ahmad Junaidi. "Analysis of Land Cover Changing and Vegetation Index at Kuranji Watershed in Padang, West Sumatera, Indonesia." Journal of Geoscience, Engineering, Environment, and Technology 4, no. 4 (December 30, 2019): 286–90. http://dx.doi.org/10.25299/jgeet.2019.4.4.4101.
Full textAbstract:
Various activities around Kuranji watershed included the land conversioncan be impacted to topographic condition and also contributed to altering the vegetation density. Remote sensing technology is an effective methodfor land cover mapping. The objectives of the present study were to analyze the changing of land cover and classifying the vegetation density index in the upstream Kuranji Watershed. This study was conducted at Kuranji Watershed in Padang, West Sumatera Province. Two Landsat images representing the changing of the watershed area during 2017 and 2018 as well as obtaining the classification of vegetation density during corresponding years.Landsat 8 OLI images were classified using a supervised classification technique, then computed the vegetation index using the Normalized Difference Vegetation Index (NDVI). The result showed that the extension of forest area, settlement area and paddy field (283.92; 35.06; and 27 Ha, respectively) and decline of mix dryland agriculture, shrub and garden area (93.68; 277.43; and 190.95 Ha respectively). Decreasing of dense vegetation found at lower dense class (6.47 Ha) and highest dense class (5535.35 Ha). Therefore, the increasing area found at the cloud, dense and higher dense class (93.17; 5525.1; and 109.94 Ha, respectively). So, it is highlighted that changing land cover and vegetation index happen during the only one-year period.
47
Li, Boyang, Yaokui Cui, Xiaozhuang Geng, and Huan Li. "Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model." Remote Sensing 13, no. 8 (April 14, 2021): 1516. http://dx.doi.org/10.3390/rs13081516.
Full textAbstract:
Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.
48
Provencher, Louis, Jeff Campbell, and Jan Nachlinger. "Implementation of mid-scale fire regime condition class mapping." International Journal of Wildland Fire 17, no. 3 (2008): 390. http://dx.doi.org/10.1071/wf07066.
Full textAbstract:
We used mid-scale Fire Regime Condition Class (FRCC) mapping to provide Hawthorne Army Depot in the Mount Grant area of Nevada, USA, with data layers to plan fuels restoration projects to meet resource management goals. FRCC mapping computes an index of the departure of existing conditions from the natural range of variability, and consists of five primary steps: (1) mapping the Potential Natural Vegetation Types (PNVT) based on interpretation of a soil survey; (2) refining PNVTs based on additional information; (3) modelling the natural range of variability (NRV) per PNVT; (4) using field verification, calculation and mapping of departure of current distribution of structural vegetation classes interpreted by remote sensing (IKONOS 4-m resolution satellite imagery) from the NRV; and (5) mapping structural vegetation classes that differ from reference conditions. Pinyon–juniper and mountain mahogany woodlands were found within the NRV, whereas departure increased from moderate for low and big sagebrush PNVTs and mixed desert shrub to high for riparian mountain meadow. Several PNVTs showed departures that were close to FRCC class limits. The common recommendation to reach the NRV was to decrease the percentage of late-development closed and cheatgrass-dominant classes, thus increasing the percentage of early and mid-development classes.
49
Hammill, Kate A., and Ross A. Bradstock. "Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity." International Journal of Wildland Fire 15, no. 2 (2006): 213. http://dx.doi.org/10.1071/wf05051.
Full textAbstract:
Fire intensity affects ecological and geophysical processes in fire-prone landscapes. We examined the potential for satellite imagery (Satellite Pour l’Observation de la Terre [SPOT2] and Landsat7) to detect and map fire severity patterns in a rugged landscape with variable vegetation near Sydney, Australia. A post-fire, vegetation-based indicator of fire intensity (burnt shrub branch tip diameters, representing the size of fuel consumed) was also used to explore whether fire severity patterns can be used to retrospectively infer patterns of fire intensity. Six severity classes (ranging from unburnt to complete crown consumption) were defined using aerial photograph interpretation and a field assessment across five vegetation types of varying height and complexity (sedge-swamp, heath, woodland, open forest, and tall forest). Using established Normalised Difference Vegetation Index (NDVI) differencing methodology, SPOT2 and Landsat7 imagery yielded similar broad-scale severity patterns across the study area. This was despite differences in image resolution (10 m and 30 m, respectively) and capture dates (2 months and 9 months apart, respectively). However, differences in the total areas mapped for some severity classes were found. In particular, there was reduced differentiation between unburnt and low-severity areas and between crown-scorched and crown-consumed areas when using the Landsat7 data. These differences were caused by fine-scale classification anomalies and were most likely associated with seasonal differences in vegetation condition (associated with time of image capture), post-fire movement of ash, resprouting of vegetation, and low sun elevation. Relationships between field severity class and NDVIdifference values revealed that vegetation type does influence the detection of fire severity using these types of satellite data: regression slopes were greater for woodland, forest, and tall forest data than for sedge-swamp and heath data. The effect of vegetation type on areas mapped in each fire severity class was examined but found to be minimal in the present study due to the uneven distribution of vegetation types in the study area (woodland and open forest cover 86% of the landscape). Field observations of burnt shrub branch tips, which were used as a surrogate for fire intensity, revealed that relationships between fire severity and fire intensity are confounded by vegetation type (mainly height). A method for inferring fire intensity from remotely sensed patterns of fire severity was proposed in which patterns of fire severity and vegetation type are combined.
50
Storchak, Irina Gennadyevna, and Fedor Vladimirovich Eroshenko. "Use of remote methods for monitoring formation of yield of spring barley." Agrarian Scientific Journal, no. 11 (November 23, 2020): 58–61. http://dx.doi.org/10.28983/asj.y2020i11pp58-61.
Full textAbstract:
When cultivating barley, there is a need to monitor the condition of crops and forecast yields using objective and inexpensive methods. Remote sensing data of the Earth is used to solve various problems in the agricultural sector related to monitoring vegetation, including monitoring the condition of agricultural crops throughout the growing season. The main advantages of this observation are: efficiency, objectivity, multi-scale and cost-effective. The question of the possibility of predicting crop yields in the scientific literature has not yet been adequately reflected. Therefore, the purpose of the research was to identify the relationship between the data of remote sensing of the Earth and the yield of spring barley for the conditions of the Stavropol Territory. The studies used data from the VEGA IKI RAS service (averaged NDVI values of spring crops in the Stavropol Territory) and the statistical office of the Stavropol Territory. In the analysis of materials, NDVI values were tied to the stages of organogenesis. It was found that the closest correlation between (0.64) NDVI and spring barley yield corresponds to the phase of the formation of the caryopsis. When analyzing yield data and values of the NDVI vegetation index on fixed calendar dates (weeks) of the year, it was shown that a statistically significant correlation appears between the 13th and 26th calendar weeks of the year. Therefore, the Stavropol Territory is characterized by the dependence of barley productivity on NDVI values of spring crops. The closest it is observed in the phase of the formation of the seed. Thus, for the conditions of the Stavropol Territory, it is possible to predict the yield of spring barley according to remote sensing data of the Earth.