scholarly journals Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 79
Author(s):  
Rui Ni ◽  
Xiaohui Zhu ◽  
Yuping Lei ◽  
Xiaoxin Li ◽  
Wenxu Dong ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Vegetation Index ◽  
Negative Impact ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Rainfall Time Series ◽  
Phenological Data

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

scholarly journals MAIZE YIELD ESTIMATION IN KENYA USING MODIS

2020 ◽  
Vol V-3-2020 ◽  
pp. 477-482
Author(s):  
B. K. Kenduiywo ◽  
A. Ghosh ◽  
R. Hijmans ◽  
L. Ndungu
Keyword(s):  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Maize Yield ◽  
Percentage Error ◽  
Support Vector ◽  
County Level

Abstract. Monitoring staple crop production can support agricultural research, business such as crop insurance, and government policy. Obtaining accurate estimates through field work is very expensive, and estimating it through remote sensing is promising. We estimated county-level maize yield for the 37 maize producing countries in Kenya from 2010 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Support Vector Regression (SVR) and Random Forest (RF) were used to fit models with observed county level maize yield as a function of vegetation indices. The following five MODIS vegetation indices were used: green normalized difference vegetation index, normalized difference vegetation index, normalized difference moisture index, gross primary production, and fraction of photosynthetically active radiation. The models were evaluated with 5-fold leave one year out cross-validation. For SVR, R2 was 0.70, the Root Mean Square Error (RMSE) was 0.50 MT/ha and Mean Absolute Percentage Error (MAPE) was 27.6%. On the other hand for RF these were 0.69, 0.51 MT/ha and 29.3% respectively. These results are promising and should be tested in specific applications to understand if they are good enough for use.

scholarly journals Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

2021 ◽  
Vol 13 (13) ◽  
pp. 2517
Author(s):  
Lijun Wang ◽  
Jiayao Wang ◽  
Fen Qin
Keyword(s):  
Land Use ◽  
Time Series ◽  
Crop Production ◽  
Large Scale ◽  
Vegetation Index ◽  
Feature Fusion ◽  
Vegetation Indices ◽  
Google Earth ◽  
Classification Rule ◽  
Cultivated Land

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

scholarly journals Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 652
Author(s):  
Attila Nagy ◽  
Andrea Szabó ◽  
Odunayo David Adeniyi ◽  
János Tamás
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Tisza River ◽  
Yield Forecasting

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

scholarly journals Minimización de los efectos atmosféricos en el índice espectral de la vegetación IVIS

2018 ◽  
Vol 36 (1) ◽  
pp. 31
Author(s):  
Fernando Paz Pellat
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Image Data ◽  
Atmospheric Effects ◽  
Atmospheric Conditions ◽  
Atmospheric Effect ◽  
Remote Sensors

It is essential to minimize atmospheric effects on spectral information of remote sensors from space platforms to avoid under estimation of biophysical variables associated with satellite image data. In this paper, a generic algorithm was developed, based on sound theoretical arguments, to analyze time series ISVI spectral vegetation index (vegetation index based on iso-soil curves), thus avoiding the problems associated with the classic design of vegetation indices, where the spectral signal saturates quickly. The results, when applying the algorithm in pixel time series of AVHRR satellite images, showed that reduction and standardization of atmospheric effects in the ISVI was achieved. Using ISVI maximum values in time series (temporal window), a reasonable approximation to atmospheric conditions with minimum or standardized effects was obtained. In conclusion, although the scheme developed failed to eliminate the atmospheric effect on ISVI entirely, it was reduced to a minimum. The algorithm developed was simple enough for operational use, with regard to atmospheric correction methods using radiative model inversions.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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 Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

2011 ◽  
Vol 4 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
F. Maignan ◽  
F.-M. Bréon ◽  
F. Chevallier ◽  
N. Viovy ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Vegetation Dynamic ◽  
Rate Of Increase ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

scholarly journals Technical Note: Development of a Landsat Time Series for Application in Forest Status Assessment in the Inland Northwest United States

2008 ◽  
Vol 23 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Russell N. Beck ◽  
Paul E. Gessler
Keyword(s):  
United States ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Satellite Image ◽  
Temporal Trends ◽  
Monitoring Cost ◽  
Small Pilot Study ◽  
Inland Northwest

Abstract The Inland Northwest United States contains extensive areas of complex, inaccessible terrain requiring significant resource expenditure for forest inventory, assessment, and monitoring. Cost-effective methods are necessary for annual broad-scale assessment of forest condition over complex terrain. Proficiency in the use of timely satellite image products along with spatial analysis tools such as geographic information systems can assist natural resource managers to understand regional dynamics and change within these landscapes. Satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI) can effectively assess and monitor vegetation dynamics of large remote areas. This article presents a newly developed archive and example methods for monitoring forest dynamics through the creation of NDVI departure maps. The NDVI products were generated from a time series of Landsat imagery (1989–2004) to derive both density distributions and a long-term departure from average map for any year or series of years within the time series archive. A preliminary application of the data is demonstrated showing temporal trends of vegetation dynamics relating to harvesting and management within two small pilot study areas in north Idaho.

scholarly journals Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

2020 ◽  
Vol 9 (11) ◽  
pp. 641
Author(s):  
Alberto Jopia ◽  
Francisco Zambrano ◽  
Waldo Pérez-Martínez ◽  
Paulina Vidal-Páez ◽  
Julio Molina ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Management Practices ◽  
Water Status ◽  
Vegetation Indices ◽  
Turgor Pressure ◽  
Security Risk ◽  
Vegetative Development ◽  
Water Sensor ◽  
Sentinel 2

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths.

scholarly journals Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2512 ◽  
Author(s):  
Nicolas Karasiak ◽  
Jean-François Dejoux ◽  
Mathieu Fauvel ◽  
Jérôme Willm ◽  
Claude Monteil ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Cross Validation ◽  
Satellite Image ◽  
Support Vector ◽  
Validation Data ◽  
Spatial Instability ◽  
Classification Errors ◽  
Leave One Out ◽  
The Impact

Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years.

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