scholarly journals Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

2019 ◽  
Vol 11 (14) ◽  
pp. 1699 ◽  
Author(s):  
Qi Yin ◽  
Maolin Liu ◽  
Junyi Cheng ◽  
Yinghai Ke ◽  
Xiuwan Chen
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Paddy Rice ◽  
Northeastern China ◽  
Rice Fields ◽  
Fusion Algorithm ◽  
Landsat Images ◽  
Modis Data ◽  
Temporal Sampling ◽  
New Strategy

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

Monitoring of Sugarcane Growth Based on the Fused Remote Sensing NDVI Series and Ground Seeding Survey

2020 ◽  
Vol 63 (6) ◽  
pp. 1795-1804
Author(s):  
Yanli Chen ◽  
Weihua Mo ◽  
Jianfei Mo ◽  
Meihua Ding
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Southern China ◽  
Growth Monitoring ◽  
List Type ◽  
Fusion Model ◽  
Growth Status ◽  
Modis Data ◽  
Timing Data ◽  
Ndvi Time Series

HighlightsThe spatial and temporal fusion model ESTARFM was used to obtain NDVI timing data with high fusion accuracy and high spatial and temporal resolution.High-quality NDVI timing data could be obtained by using ESTARFM to fuse HJ-1 CCD and MODIS data.Fused NDVI data coupled with ground seeding survey data could effectively monitor sugarcane growth status.Abstract. This study addressed the instability of clear-sky remote sensing data with high spatial resolution in sugarcane growing areas in southern China and the current inconsistency between traditional survey results and remote sensing results for seedling growth. Moderate-resolution imaging spectroradiometer (MODIS) data and China land resources satellite (HJ-1 CCD) data were used to build high-resolution normalized difference vegetation index (NDVI) time series using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Agronomic indicators of sugarcane were obtained by field sampling and were used for determining the remote sensing monitoring index (NDVI) of sugarcane growth. The method provided satisfactory results for evaluating sugarcane growth, with accuracy exceeding 90%. Moreover, sugarcane growth monitoring in a wider area was highly correlated with yield per unit area. Keywords: Growth status, HJ-1 CCD, MODIS, NDVI time series, Spatial and temporal fusion, Sugarcane.

scholarly journals Análise multitemporal da linha de costa da praia de Macapá no litoral do Piauí a partir de imagens Landsat / Multitemporal analysis of the coastline of Macapá beach on the coast of Piauí from Landsat images

2017 ◽  
Vol 27 (1) ◽  
pp. 189
Author(s):  
Érico Rodrigues Gomes ◽  
Inessa Racine Gomes de Araújo
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Coastal Plain ◽  
Human Activities ◽  
Coastal Erosion ◽  
General Tendency ◽  
Coastal Monitoring ◽  
Landsat Images ◽  
Coastal Dynamics ◽  
Landscape Unit

<p>A planície costeira do estado do Piauí tem passado por diversas intervenções em função das atividades naturais e humanas. A zona costeira representa uma unidade de paisagem que mesmo sem apresentar grande ocupação já apresenta indicativos ambientais no que se refere a erosão costeira. A metodologia foi baseada em uma análise de séries temporais de 30 anos (1985 a 2015) através de imagens Landsat para a detecção e variação da linha de costa. Os resultados obtidos indicam que há uma tendência generalizada no processo de avanço das águas oceânicas sobre a linha da costa na praia de Macapá e que está relacionado com a dinâmica costeira e também com o fato de que neste local há intensa carga de sedimentos oriundos do continente, através do trabalho de deposição e transporte dos rios Cardoso e Camurupim, que deságuam no oceano em forma de estuário.</p><p><strong>Palavras–chave:</strong> erosão costeira, monitoramento costeiro, linha de costa, sensoriamento remoto.</p><p><strong>Abstract </strong></p><p>The coastal plain of the state of Piauí has undergone several interventions due to natural and human activities. The coastal zone represents a landscape unit that even without presenting great occupation already presents environmental indicatives with respect to coastal erosion. he methodology was based on a 30-year time series analysis (1985 to 2015) using Landsat images for the detection and variation of the coastline. The results indicate that there is a general tendency in the process of advancing the oceanic waters on the coastline in the beach of Macapá and that is related to the coastal dynamics and also to the fact that in this place there is an intense load of sediments originating from the continent, through the work of deposition and transportation of the rivers Cardoso and Camurupim, that fall into the ocean in the form of estuary.</p><p><strong>Keywords</strong>: coastal erosion, coastal monitoring, coast line, remote sensing.</p>

scholarly journals Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms

2015 ◽  
Vol 160 ◽  
pp. 99-113 ◽  
Author(s):  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Weili Kou ◽  
Yuanwei Qin ◽  
Geli Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Paddy Rice ◽  
Landsat Images

scholarly journals A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 363 ◽  
Author(s):  
Moreno-Ruiz ◽  
García-Lázaro ◽  
Arbelo
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Reference Data ◽  
Global Climate ◽  
Fire Service ◽  
Burned Area ◽  
Spatial And Temporal Patterns ◽  
Modis Data

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

scholarly journals Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4400
Author(s):  
Rongkun Zhao ◽  
Yuechen Li ◽  
Jin Chen ◽  
Mingguo Ma ◽  
Lei Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Fusion ◽  
Survey Data ◽  
Paddy Rice ◽  
Spatiotemporal Data ◽  
Series Data ◽  
Landsat 8 ◽  
Rice Area ◽  
Sentinel 2

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

scholarly journals Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5184
Author(s):  
Linghua Meng ◽  
Huanjun Liu ◽  
Susan L. Ustin ◽  
Xinle Zhang
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Cotton Field ◽  
Cotton Yield ◽  
Yield Estimation ◽  
Landsat Images ◽  
Temporal Sampling ◽  
Reference Images ◽  
Fusion Images ◽  
The Impact

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the ‘mixed pixels’ nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

scholarly journals HISTORICAL AGRICULTURAL LANDSCAPES: MAPPING SEASONAL CONDITIONS FOR SUSTAINABLE TOURISM

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 641-646
Author(s):  
A. Rolando ◽  
A. Scandiffio
Keyword(s):  
Remote Sensing ◽  
Agricultural Landscape ◽  
Sustainable Tourism ◽  
Paddy Rice ◽  
Agricultural Landscapes ◽  
Rural Landscape ◽  
Rice Fields ◽  
Rice Cultivation ◽  
Gis Mapping ◽  
Ecological Processes

Abstract. The current research aims at exploring the relationships between historical agricultural landscapes and sustainable tourism, by combining the potential of aerial and ground observation methods, that are able to detect the seasonal landscape changes. These phenomena are strongly interlaced with the annual cycle of plants, which have many implications for ecological processes, agriculture, health, tourism, regional/urban planning and economy. In many countries, similar phenomena as the timing of spring-blooming or the timing of autumn coloring foliage are of great visual value and can be of touristic interest, so to enhance the overall attractiveness of a territory. The research analyzes the case study of the historical agricultural landscape, localized in the in-between territories Turin and Milan, which is characterized by large portions of paddy-rice fields, which assume different aesthetical configurations over the year. This landscape, made up of an articulated system of waterways that support large portions of rice cultivation, protected natural areas, historical farmhouses, urban settlements, is the result of a long process of interaction between natural elements and human activities. Remote sensing and ground observations can play an important role in a high-accuracy mapping of the seasonal conditions of this kind of landscape. The flooding of paddy-rice fields determines a high scenic value of large portions of the rural landscape, that can be detected through remote sensing. The specificity of rice cultivation is that plants grow on flooded soils. Such a temporary condition of the landscape can become an unexpected tourist destination. From the methodological point of view, the research combines the potential of time series of satellite high-resolution imagery, for computing vegetation indexes (i.e. NDVI, NDWI etc.), and ground observations, through GIS mapping tools. This interpretation tools are useful to trace a network of slow scenic routes that allow perceiving such temporary landscape conditions and that support a territorial strategy aiming at a sustainable development of these fragile territories.

scholarly journals GENERATING TIME SERIES OF MEDIUM-RESOLUTION TEMPERATURE VEGETATION DROUGHT INDEX IMAGES USING A KALMAN FILTER METHOD FOR SOIL MOISTURE CHANGE ANALYSIS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 495-501
Author(s):  
H. Zhai ◽  
F. Huang ◽  
H. Qi ◽  
Y. Ren ◽  
R. Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Land Surface ◽  
Time Series Data ◽  
Series Data ◽  
Modis Data ◽  
Medium Resolution ◽  
Short Period

Abstract. Soil moisture is one of key environmental variables that affect vegetation cover and energy exchange between the land surface and the atmosphere. Satellite remote sensing technology can provide information for monitoring large-scale soil moisture dynamics quickly. The temperature vegetation dryness index (TVDI) acts as an effective indicator of inferring soil moisture status which is calculated according to the empirical parameterization of composed of the land surface temperature (LST) and the normalized difference vegetation index (NDVI) characteristic space. In this paper, the MODIS TVDI was calculated based on MODIS LST product (MOD11A2, 1 km) and NDVI data (derived from MOD09A1, 500m). Meanwhile, LST and NDVI from Landsat8 OLI images were estimated to obtain Landsat-based TVDI. Then, a Kalman filter algorithm was used to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and MODIS data. We selected the west of the Songnen Plain, China as the test area and high quality cloudy-free images during growing season (April to October) of 2018 as the input data. The predicted TVDI time series data of medium resolution not only improved the temporal resolution to capture the changes at fine scale within a short period, but also made up for the deficiency of low spatial resolution MODIS data. The results show that it is feasible to generate medium or high resolution TVDI time series data by applying different remote sensing data by Kalman filtering algorithm.

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