scholarly journals Maize Yield Forecast Using GIS and Remote Sensing The Case of Kaffa Zone, South Western Ethiopia

2021 ◽  
Author(s):  
Dereje Biru ◽  
Jemal Tefera .
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Time Series Data ◽  
Potential Evapotranspiration ◽  
Maize Yield ◽  
Geographical Information ◽  
Series Data ◽  
Yield Data ◽  
Yield Forecast ◽  
Central Statistical

Abstract Background: Policy makers, government planners and agriculturalist in Ethiopia require accurate and timely information about maize yield and production. Kaffa zone is by far the most important maize producing zone in the country. The manual collection of field data and data processing for crop forecasting by the CSA requires significant amounts of time before official reports are released. Several studies have shown that maize yield can be effectively forecast using satellite remote sensing data. The objectives of this study were to develop a maize yield forecast model in kaffa Zone derived from time series data of eMODIS_NDVI, actual and potential evapotranspiration and CHIRPS for the years 2008-2017.Official grain yield data from the Central statistical Agency of Ethiopia was used to validate the strength of the indices in explaining the yield. Crop masking at crop land area was applied and refined by using agro ecological zones suitable for the crop of interest. Correlation analyses were used to determine associations among crop yield, spectral indices and agro meteorological variables for maize crop of the long rainy season (kiremt). Indices with high correlation with maize yield were identified. Results: Average Normalized Difference Vegetation Index and rainfall have high correlation of maize yield with 84% and 89%, respectively. That means their variables are positively strong related with maize yield. The generated spectro-agro meteorological yield model was successfully tested against the Central Statistical Agency's expected Zone level yields (r2= 0.89, RMSE = 1.54qha1, and 16.7% coefficient of variation).Conclusions: Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers there by proving the clear potential of spectro-agro meteorological factors for maize yield forecasting, particularly for Ethiopia.

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

scholarly journals The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

scholarly journals Remote Sensing Index for Mapping Canola Flowers Using MODIS Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3912
Author(s):  
Yunze Zang ◽  
Xuehong Chen ◽  
Jin Chen ◽  
Yugang Tian ◽  
Yusheng Shi ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Flowering Period ◽  
Yield Data ◽  
Yellowness Index ◽  
Modis Data ◽  
Ndvi Time Series

Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.

scholarly journals Evaluating the Relationships of Inter-Annual Farmland Vegetation Dynamics with Biodiversity Using Multi-Spatial and Multi-Temporal Remote Sensing Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1479
Author(s):  
Niloofar Alavi ◽  
Douglas King
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Time Series Data ◽  
Temporal Trends ◽  
Agricultural Landscapes ◽  
Natural Vegetation ◽  
Series Data ◽  
Positive Trend

Agricultural landscapes are highly dynamic ecosystems, but the effects of temporal farmland vegetation dynamics on species diversity have not been widely studied. In 93 sample farm landscapes in eastern Ontario, Canada, biodiversity data for seven taxa were collected in 2011 and 2012, prior to the initiation of this study. The goal of this study was to determine if trends and variability in vegetation productivity detected in these sample landscapes using long-term archived moderate and coarse resolution remote sensing time series data are related to the measured biodiversity. Mid-summer Moderate Resolution Imaging Spectroradiometer (MODIS) (2000–2011) and Landsat 5 (1985–2011) Normalized Difference Vegetation Index (NDVI) data were used with the Thiel–Sen slope and Contextual Mann–Kendall trend analysis to identify pixels showing significant trends. NDVI temporal metrics included 1) the percentage of pixels in each landscape with a significant negative or positive trend, and 2) the temporal coefficient of variation (CV) of both the mean and spatial CV of landscape NDVI. Larger areas of significant positive NDVI trends were found in the sample landscapes than negative trends, the former being associated with agricultural intensification or crop changes and the latter with smaller areas of natural vegetation removal. Landsat better-detected changes in individual fields or small areas of natural vegetation due to its much smaller pixel size. In addition, the longer Landsat time series showed a change in the NDVI trend from positive (1985–2000) to negative or a leveling off (2000–2011) for many pixels. In biodiversity modeling, the Landsat temporal CV of NDVI was negatively correlated with 2011–2012 plant and beetle diversity, while plant biodiversity was positively correlated with the percentage of pixels in a sample landscape showing a significantly positive NDVI trend. No significant relationships were found using the MODIS data. This study shows that temporal trends and variability in farmland vegetation density derived from Landsat data are related to biodiversity for certain taxa and that such relationships should be considered along with the more commonly studied spatial landscape attributes in evaluating landscape-level impacts of farming on biodiversity.

scholarly journals Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

2020 ◽  
Vol 12 (1) ◽  
pp. 163-173 ◽  
Author(s):  
Xiaofang Sun ◽  
Meng Wang ◽  
Guicai Li ◽  
Yuanyuan Wang
Keyword(s):  
Remote Sensing ◽  
Northeast China ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Regional Scale ◽  
Growth Period ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Yield Data

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.

scholarly journals Monitoring Urban Sprawl Using Time-Series Data: Famagusta Region of Northern Cyprus

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110074
Author(s):  
Kamyar Fuladlu ◽  
Müge Riza ◽  
Mustafa Ilkan
Keyword(s):  
Time Series ◽  
Urban Sprawl ◽  
Census Data ◽  
Time Series Data ◽  
Developed Countries ◽  
Geographical Information ◽  
Series Data ◽  
Great Success ◽  
European Cities ◽  
Northern Cyprus

Monitoring urban sprawl is a controversial topic among scholars. Many studies have tried to employ various methods for monitoring urban sprawl in cases of North American and Northern and Western European cities. Although numerous methods have been applied with great success in various developed countries, they are predominantly impractical for cases of developing Mediterranean European cities that lack reliable census data. Besides, the complexity of the methods made them difficult to perform in underfunded situations. Therefore, this study aims to develop a new multidimensional method that researchers and planners can apply readily in developing Mediterranean European cities. The new method was tested in the Famagusta region of Northern Cyprus, which has been experiencing unplanned growth for the past half-century. In support of this proposal, a detailed review of the existing literature is presented with an emphasis on urban sprawl characteristics. Four characteristics were chosen to monitor urban sprawl’s development in the Famagusta region. The method was structured based on a time-series (2001, 2006, 2011, and 2016) dataset that used remote sensing data and geographical information systems to monitor the urban sprawl. Based on the findings, the Famagusta region experienced rapid growth during the last 15 years. The lack of a masterplan resulted in the uncontrolled expansion of the city in the exurban areas. The development configuration was polycentric and linear in form with single-use composition. Together, the expansion and configuration manifested as more built-up area, scattered development, and increased automobile dependency.

scholarly journals Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

2018 ◽  
Vol 7 (11) ◽  
pp. 418 ◽  
Author(s):  
Tian Jiang ◽  
Xiangnan Liu ◽  
Ling Wu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Vegetation Index ◽  
Southern China ◽  
Series Data ◽  
Support Vector ◽  
Complex Landscape

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

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