Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Non-local tensor completion for multitemporal remotely sensed images inpainting

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method

Remote Sensing of Coastal Wetlands: Long term vegetation stress assessment and data enhancement technique

Apalachicola Bay in the Florida panhandle is home to a rich variety of salt water and freshwater wetlands but unfortunately is also subject to a wide range of hydrologic extreme events. Extreme hydrologic events such as hurricanes and droughts continuously threaten the area. The impact of hurricane and drought on both fresh and salt water wetlands was investigated over the time period from 2000 to 2015 in Apalachicola Bay using spatio-temporal changes in the Landsat based NDVI. Results indicate that salt water wetlands were more resilient than fresh water wetlands. Results also suggest that in response to hurricanes, the coastal wetlands took almost a year to recover while recovery following a drought period was observed after only a month. This analysis was successful and provided excellent insights into coastal wetland health. Such long term study is heavily dependent on optical sensor that is subject to data loss due to cloud coverage. Therefore, a novel method is proposed and demonstrated to recover the information contaminated by cloud. Cloud contamination is a hindrance to long-term environmental assessment using information derived from satellite imagery that retrieve data from visible and infrared spectral ranges. Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Landsat has an ongoing high resolution NDVI record starting from 1984. Unfortunately, the time series NDVI data suffers from the cloud contamination issue. Though simple to complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques are subject to many limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum with the aid of a machine learning tool, namely random forest (RF) trained and tested utilizing multi-parameter hydrologic data. The RF based OCPR model was compared with a simple linear regression (LR) based OCPR model to understand the potential of the model. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance for two specific dates. The RF based OCPR method achieves a root mean squared error of 0.0475 sr?1 between predicted and observed NDVI reflectance values. The LR based OCPR method achieves a root mean squared error of 0.1257 sr?1. Findings suggested that the RF based OCPR method is effective to repair cloudy values and provide continuous and quantitatively reliable imagery for further analysis in environmental applications

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

ANALYSIS OF AGRO-ECOSYSTEMS EXPLOITING OPTICAL SATELLITE DATA TIME SERIES: THE CASE STUDY OF CAMARGUE REGION, FRANCE

The research activities presented in this manuscript were conducted in the frame of the international project SCENARICE, whose aim is to demonstrate the contribution of different technical and scientific competences, to assess current characteristics of analyzed cropping systems and to define sustainable future agricultural scenarios. Dynamic simulation crop models are used to evaluate the efficiency of current cropping systems and to predict their performances as consequence of climate change scenarios. In this context, a lack of information regarding the intra- and inter-annual variability of crop practices was highlighted for crops such as winter wheat, for the study area of Camargue. Moreover, a description of possible future cropping systems adaptation strategies was needed to formulate short term scenario farming system assessment. To perform this analysis it is fundamental to identify the different farm typologies representing the study area. Since it was required to take into account inter-annual variability of crop practices and farm diversities to build farm typologies, representative data of the study region in both time and space were needed. To address this issue, in this work long term time series of satellite data (2003-2013) were exploited with the specific aims to: (i) provide winter wheat sowing dates estimations variability on a long term period (11 years) to contribute in base line scenario definition and (ii) reconstruct farms land use changes through the analysis of time series of satellite data to provide helpful information for farm typologies definition. Two main research activities were carried out to address the defined objectives. Firstly a rule-based methodology was developed to automatically identify winter wheat cultivated areas in order to retrieve crop sowing occurrences in the satellite time series. Detection criteria were derived on the basis of agronomic expert knowledge and by interpretation of high confidence temporal signature. The distinction of winter wheat from other crops was based on the individuation of the crop heading and establishment periods and considering the length of the crop cycle. The detection of winter wheat cultivated areas showed that 56% of the target in the study area was correctly detected with low commissions (11%). Once winter wheat area was detected, additional rules were designed to identify sowing dates. The method was able to capture the seasonal variability of sowing dates with errors of \ub18 and \ub116 days in 45% and 65% of cases respectively. Extending the analysis to the 11 years period it was observed that in Camargue the most frequent sowing period was about October 31th (\ub14 days of uncertainty). The 2004 and 2006 seasons showed early sowings (late September) the 2003 and 2008 seasons were slightly delayed at the beginning of November. Sowing dates were not correlated to the seasonal rainfall events; this led us to formulate the hypothesis that sowing dates could be much more influenced by the harvest date of the preceding crop and soil moisture, which are related to rains but also to the date of last irrigations and to the wind. The second activity was related to define farm typologies. Temporal trajectories of winter and summer crops cultivated areas were estimated at farm scale level based on satellite data time series in the 2003-2013 periods. The validation demonstrated that the method was able to produce maps with high overall accuracy (OA 92%) and very low commission errors (3% for summer crops and 7% for winter crops). Omission errors were very low for summer crops (3%) and higher but within an acceptable level for winter crops (31%). Temporal trajectories of annual winter and summer crop land use at farm level were assumed as indicators of farm management (e.g. intensive monoculture farm or diversified crop producer). Trajectories were analysed through a hierarchical clustering procedure to identify farm management typologies. We were able to identify six typologies out of 140 farm samples, covering 75% of the arable land in the study area. A semantic interpretation of the farm types, allowed formulating hypothesis to describe farming systems. The size of the farms seemed to be an explanatory variable of the intensive or extensive farm management. The two main activities presented in this thesis highlighted the importance of time series spatial and temporal resolution for crop monitoring purposes. Currently, only heterogeneous remotely sensed data in terms of spatial and temporal resolutions are available for agricultural monitoring. Forthcoming sensors (i.e. ESA Sentinel-II A/B) will offer the chance to exploit coexisting high spatial and temporal resolutions for the first time. A preliminary application of an innovative methodology for the fusion of heterogeneous spatio-temporal resolution remotely sensed datasets was provided in the final section of the thesis with the aim to (i) produce high spatio-temporal resolution time series and (ii) verify the quality and the usefulness of the generated time series for monitoring the main European cultivated crops. The experiment positively demonstrated the contribution of data fusion techniques for the production of time series at high space-time resolution for crop monitoring purposes. The application of data fusion techniques in the main methodologies presented in this work appears to be beneficial. To conclude this thesis framework, satellite remotely sensed data properly analyzed has shown to be a reliable tool to study large-scale crop cultivations and to retrieve spatially and temporally distributed information of cropping systems. Remote sensing time series analyses lead to highlight patterns of intra- and inter-annual dynamics of agro-practices and were also useful to define farm typologies based on multi-temporal land use trajectories. Results contribute in enriching the studies and the characterization of the Camargue study area, in particular providing information such as sowing dates that are not available at present for the considered study area and represent a step forward in respect to the actual (static) available crop calendar informations. Moreover, the achieved results provide supplementary information layers for summarize and classify the diversity of the farm in the study area and to characterize farming systems