Identification of Apple Orchard Planting Year Based on Spatiotemporally Fused Satellite Images and Clustering Analysis of Foliage Phenophase

The planting year of apple orchard not only determines the fruit output but also provides information for the governmental management of the fruit industry. However, considering that different orchards use different management and cultivation methods, this may result in some trees having similar outlines but different planting years, and it is, therefore, difficult to effectively determine the actual planting year based on textural or structural characteristics. Therefore, the monitoring method provided in this paper is not to monitor the growing year positively from the planting of orchard seedlings but to use time series remote sensing data to reverse determine the continuous growth age of each existing orchard. The city of Qixia, Shandong Province, China, was used as a case study. Firstly, the spatial distribution of apple orchards was accurately extracted using the Sentinel-2 normalized difference vegetation index (NDVI) spatiotemporally fused images and phenological vegetation information. Secondly, using region of interest (ROI) data for different vegetation types obtained from a field survey, NDVI time series were extracted from the Sentinel-2 NDVI spatiotemporally fused image. Among them, three characteristic phenological periods were selected, and the NDVI time series for apple orchards was used as a template to extract the apple orchard distribution area from 2000 to 2017. Then, the distribution area of apple orchards was defined as the area of interest in the planting year, combined with the Landsat NDVI time series image composed of three characteristic phenological periods each year from 2000 to 2017, and the apple orchard phenological curve. Subsequently, a Euclidean distance (ED) method was used to calculate the distribution area of apple orchards for each year between 2000 and 2017. Finally, a pixel-by-pixel inverse time series calculation method was used to obtain the planting year of apple orchards in the study area. This study provides a new way to accurately identify the planting year of apple orchards using satellite remote sensing images.

[1]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[2]  Zhaohui Xue,et al.  Phenology-Driven Land Cover Classification and Trend Analysis Based on Long-term Remote Sensing Image Series , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Thomas Hilker,et al.  Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery , 2011 .

[5]  Anthony R. Ives,et al.  Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Lizhe Wang,et al.  A time-series classification approach based on change detection for rapid land cover mapping , 2019 .

[7]  Joanne C. White,et al.  Forest stand age classification using time series of photogrammetrically derived digital surface models , 2016 .

[8]  L. Cizek,et al.  Age estimation of large trees: New method based on partial increment core tested on an example of veteran oaks , 2016 .

[9]  A. Brenning,et al.  Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile , 2015 .

[10]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[11]  B. Griscom,et al.  Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data , 2004 .

[12]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[13]  Lifan Chen,et al.  Dynamic monitoring of the Poyang Lake wetland by integrating Landsat and MODIS observations , 2018 .

[14]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[15]  Cornelius Senf,et al.  A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series , 2017 .

[16]  Waldo Kleynhans,et al.  A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Chunjiang Zhao,et al.  Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data , 2019, PeerJ.

[18]  Dirk Pflugmacher,et al.  Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series , 2013, Remote. Sens..

[19]  Chao Li,et al.  Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison , 2019, Remote. Sens..

[20]  P. Danielsson.  Euclidean distance mapping , 1980 .

[21]  Deren Li,et al.  A review of vegetation phenological metrics extraction using time-series, multispectral satellite data , 2020 .

[22]  Nannan Zhang,et al.  Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model , 2019, Remote. Sens..

[23]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[24]  Xiaoxia Wang,et al.  Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data , 2014, Remote. Sens..

[25]  M. Schlerf,et al.  Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods , 2005 .

[26]  Janatul Aziera binti Abd Razak,et al.  Mapping rubber trees based on phenological analysis of Landsat time series data-sets , 2017 .

[27]  R. Hall,et al.  Discrimination of conifer height, age and crown closure classes using Landsat-5 TM imagery in the Canadian Northwest Territories , 2003 .

[28]  Dan G. Blumberg,et al.  Classification-based mapping of trees in commercial orchards and natural forests , 2018, International Journal of Remote Sensing.

[29]  Yingying Dong,et al.  Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Ahmad Al Bitar,et al.  Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data , 2016 .

[31]  Michael A. Lefsky,et al.  Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity , 2005 .

[32]  Andrew Robson,et al.  Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia , 2017, Remote. Sens..

[33]  A. Cracknell,et al.  Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia , 2013 .

[34]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[35]  Ryutaro Tateishi,et al.  Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques , 2015, Remote. Sens..

[36]  Xiang Li,et al.  Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series , 2016, Remote. Sens..

[37]  Jennifer Pontius,et al.  Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty , 2014 .

[38]  Ladislav Mucina,et al.  Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[39]  Mirela G. Tulbure,et al.  Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics , 2018, Remote Sensing of Environment.

[40]  Yang Shao,et al.  An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data , 2016 .

[41]  Alfonso Fernández-Manso,et al.  Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Wei Luo,et al.  Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987-2015 , 2018, Remote. Sens..

[43]  S. Barr,et al.  Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations , 2019, Remote Sensing of Environment.

[44]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[45]  Farrah Melissa Muharam,et al.  Textural measures for estimating oil palm age , 2018, International Journal of Remote Sensing.

[46]  Bahareh Kalantar,et al.  Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis , 2018, J. Sensors.

[47]  Jean-Claude Thill,et al.  Stand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[48]  Ujjwal Maulik,et al.  Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery , 2013 .

[49]  Annekatrin Metz,et al.  ombined use of multi-seasonal high and medium resolution satellite magery for parcel-related mapping of cropland and grassland , 2014 .

[50]  Julia Mcmorrow,et al.  Relation of oil palm spectral response to stand age , 1995 .

[51]  Eike Luedeling,et al.  Distribution margins as natural laboratories to infer species’ flowering responses to climate warming and implications for frost risk , 2019, Agricultural and Forest Meteorology.

[52]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[53]  Cong Xu,et al.  Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Hannes Taubenböck,et al.  Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[55]  Cornelius Senf,et al.  Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery , 2015 .

[56]  A. Chemura,et al.  Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana , 2015 .

[57]  Nicholas C. Coops,et al.  Estimating Forest Stand Age from LiDAR-Derived Predictors and Nearest Neighbor Imputation , 2014 .

[58]  Juha Hyyppä,et al.  Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Dino Ienco,et al.  Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture , 2019 .