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 USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850 ◽  
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Thresholds definition in MOD13Q1 and VGT-S10 time series for coffee crop area estimation in Triângulo Mineiro/Alto Paranaíba

2019 ◽  
Vol 3 (2) ◽  
pp. 1-10
Author(s):  
Michel Eustáquio Dantas Chaves ◽  
Elizabeth Ferreira ◽  
Antonio Augusto Aguilar Dantas
Keyword(s):  
Land Use ◽  
Time Series ◽  
Large Scale ◽  
Agricultural Research ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Seasonal Development ◽  
Land Use Classification ◽  
Crop Area ◽  
The Time Domain

In the last decades, remote sensing application in agricultural research has intensified to evaluate phenological cycles. Vegetation indices time series have been used to obtain information about the seasonal development of agricultural vegetation on a large scale. The multitemporal approach increases the gain of information coming from orbital images, an important factor for analysis of its spatial distribution. The objective of this study was to test the application of vegetation indices of the MODIS and SPOT-VEGETATION sensors to estimate the areas destined for coffee crops in the Triângulo Mineiro/Alto Paranaíba mesoregion. The results show that the vegetation indices NDVI and EVI of the product MOD13Q1 were more adequate for the estimation of land use over the time domain, especially NDVI. The best minimum threshold varies between 0.39 - 0.42 and the best maximum threshold varies between 0.71 - 0.74. The contribution of this work is that these thresholds can serve as subsidies for land use classification studies on a regional scale and for estimating areas for planting.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

scholarly journals Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

2019 ◽  
Vol 11 (10) ◽  
pp. 1235 ◽  
Author(s):  
Aaron M. Shew ◽  
Aniruddha Ghosh
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Accuracy Assessment ◽  
Vegetation Indices ◽  
National Level ◽  
Google Earth ◽  
Dry Season ◽  
Future Research ◽  
Cropping Patterns

In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals ANÁLISE DO USO DA TERRA EM UMA SECÇÃO ÀS MARGENS DO RIO TOCANTINS AUXILIADA POR ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA – NDVI

2019 ◽  
Vol 5 (18) ◽  
pp. 9379
Author(s):  
Rodrigo Lima Santos ◽  
Fabrizia Gioppo Nunes
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Google Earth ◽  
Fish Ponds ◽  
Google Earth Engine ◽  
Tocantins River ◽  
Para Determinar ◽  
Del Rio

LAND USE ANALISYS IN A SECTION OF TOCANTINS’S RIVER MARGINAL STRIP SUPPORTED BY NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)ANÁLISIS DEL USO DE LA TIERRA EN UNA SECCIÓN LOS MARGENES DEL RÍO TOCANTINS AUXILIADOS POR ÍNDICE DE VEGETACIÓN POR DIFERENCIA NORMALIZADA – NDVIRESUMOO mapeamento de uso e cobertura da terra é um instrumento indispensável para uma boa gestão do ambiente em geral, podendo obedecer diferentes recortes espaciais. A adoção de alternativas para aperfeiçoar esse produto, tais como a leitura de dados anuais de Índices de Vegetação torna-o mais efetivo e capaz de oferecer respostas a determinadas questões. Nesta perspectiva, o presente estudo tem como objetivo o mapeamento e reconhecimento de áreas degradadas e de áreas preservadas, em uma secção delimitada as margens do Rio Tocantins, auxiliados por séries temporais de NDVI. A metodologia incluiu o mapeamento de uso e cobertura da terra no ano de 2015; delimitação da Área de Proteção Ambiental (APP) e; a utilização de séries anuais de NDVI, disponibilizadas pela plataforma online do Google Earth Engine. A ferramenta de NDVI é apresentada como uma alternativa a avaliação da conversão de coberturas naturais para diferentes tipologias de uso da terra. Como exemplos, são retratados três pontos de conversões de uso: solo exposto para vegetação regenerada; vegetação natural para tanques de pisciculturas e; solo exposto intercalado à vegetação rasteira para área urbanizada. Os resultados apontam que a APP analisada se encontra em estado de alerta, uma vez que sua conversão em áreas degradadas ultrapassa cerca de 50%, e a ferramenta de NDVI foi essencial para determinar quando ocorreram essas modificações em distintas classes de uso.Palavras-chave: Uso da Terra; Séries Temporais; Rio Tocantins; Imperatriz-MA.ABSTRACTThe mapping of land use and land cover is an indispensable tool for good management of the environment in general, and can obey different spatial cutouts. Adopting alternatives to improve this product, such as reading annual Vegetation Index data makes it more effective and able to provide answers to certain questions. In this perspective, the present study aims to map and recognize degraded areas and preserved areas, in a section delimited the banks of the Tocantins River, aided by NDVI time series. The methodology included land use and land cover mapping in 2015; delimitation of the Environmental Protection Area (APP) and; use of annual NDVI series made available through the Google Earth Engine online platform. The NDVI tool is presented as an alternative to evaluate the conversion of natural coverages for different land use typologies. As examples, three points of use conversions are depicted: exposed soil for regenerated vegetation; natural vegetation for fish ponds and; exposed soil interspersed with undergrowth to urbanized area. The results indicate that the analyzed APP is in a state of alert, since its conversion to degraded areas exceeds about 50%, and the NDVI tool was essential to determine when these changes occurred in different classes of use.Keywords: Land Use; Time Series; Tocantins River; Imperatriz-MA.RESUMENEl mapeo del uso de la tierra y la cobertura de la tierra es una herramienta indispensable para la buena gestión del medio ambiente en general, y puede obedecer a diferentes recortes espaciales. Adoptar alternativas para mejorar este producto, como leer los datos anuales del Índice de Vegetación, lo hace más efectivo y capaz de proporcionar respuestas a ciertas preguntas. En esta perspectiva, este estudio apunta a mapear y reconocer áreas degradadas y áreas preservadas, en una sección delimitada a orillas del río Tocantins, ayudado por series de tiempo NDVI. La metodología incluyó el uso del suelo y el mapeo de la cobertura del suelo en 2015; delimitación del Área de Protección Ambiental (APP) y; uso de la serie anual NDVI disponible a través de la plataforma en línea Google Earth Engine. La herramienta NDVI se presenta como una alternativa para evaluar la conversión de coberturas naturales para diferentes tipologías de uso de la tierra. Como ejemplos, se representan tres conversiones de puntos de uso: suelo expuesto para vegetación regenerada; vegetación natural para estanques de peces y; suelo expuesto intercalado con maleza en el área urbanizada. Los resultados indican que la aplicación analizada se encuentra en estado de alerta, ya que su conversión a áreas degradadas supera aproximadamente el 50%, y la herramienta NDVI fue esencial para determinar cuándo ocurrieron estos cambios en diferentes clases de uso.Palabras clave: Uso de la Tierra; Series Temporales; Río Tocantins; Imperatriz-MA.

scholarly journals Large-scale vegetation responses to terrestrial moisture storage changes

2016 ◽  
Author(s):  
Robert L. Andrew ◽  
Huade Guan ◽  
Okke Batelaan
Keyword(s):  
Land Use ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Water Storage ◽  
Temporal Variations ◽  
Discrete Wavelet ◽  
Large Spatial Scale ◽  
Normalised Difference Vegetation Index ◽  
Vegetation Activity

Abstract. The Normalised Difference Vegetation Index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of NDVI anomalies in a stepwise regression. Results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, NDVI appears to be more sensitive to inter-annual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher frequencies of water storage changes. Different types of vegetation, defined by areas of land use type show distinct differences in how they respond to the changes in water storage which is generally consistent with our physical understanding. This unique method provides useful insight into how NDVI is affected by changes in water storage at different temporal scales across land use types.

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 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.

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