Mapping Wheat Growing Areas of Turkey by Integrating Multi-Temporal NDVI Data and Official Crop Statistics

Wheat is the most widely cultivated crop in the world providing critical food source of most countries. It exceeds most of the grain crops in acreage and production because of its ability to grow in wide range of climatic and geographic conditions. Timely and reliable information on wheat acreages is essential for government services in order to formulate their policies for planning of agricultural production and monitoring their food supply. Traditionally, agricultural statistics is considered as the main source of such information. Unfortunately, existing statistical data of wheat acreages of Turkey, mostly dependent on farmers’ declarations, does not provide spatial information of where this crop specifically is grown. Satellite remote sensing technology can enable the acquisition of such information indirectly with the use of ancillary data of crop statistics. This study aims to determine wheat cultivation areas of Turkey as percentage per unit area in a crop map by integrating time series satellite NDVI imagery with the official crop statistics through regression analysis. The regression results indicated that satellite data explained 95.8% of the variability in official wheat crop statistics and actual wheat cropping areas were significantly related to NDVI-based wheat classes. Validation of the produced wheat map showed that there was good agreement between actual wheat fractions and estimated NDVI-based wheat fractions explaining approximately 69% (Adj. R2) of the total variability between them. This study suggests use of the methodology employed here to governing bodies that need to identify and to map current wheat cropping areas

Rangelands Vegetation Mapping at Species Composition Level Using the \u3cb\u3eSPiCla\u3c/b\u3e Method: \u3cb\u3eS\u3c/b\u3eDM Based \u3cb\u3ePi\u3c/b\u3exel \u3cb\u3eCla\u3c/b\u3essification and Fuzzy Accuracy. A New Approach of Map Making

Vegetation maps have been made since centuries. The vegetation cover was represented as homogeneous mapping units (polygons), representing different vegetation types, where each type consists a combination of different plant species (floristic composition). More recent, with the use of satellite imagery, the polygons have been replaced by pixels with similar content as the polygon maps. In both approaches, field-observations were linked to the mapping units (polygons or pixels) often resulting in a complex of different vegetation types per mapping unit. In our new approach field data (sample points) on presence and abundance of individual grass species are spatially extrapolated based on a set of environmental layers, using the species distribution modelling approach (SDM). When combined, each pixel will contain its own set of information about the vegetation structure and its floristic composition. This new methodology (SPiCla) results in a very accurate and detailed vegetation map at pixel level, allowing extraction of very detailed, accurate and easy to update spatial information on e.g., forage production and quality (palatability) for rangelands management. As no exact boundaries exist, but only gradients, we introduced fuzzy accuracy. The resolution mainly depends on the resolution of (or one of) the environmental layers used, scale of interest and workability. The methodology is generic and applicable to any other region in the world

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe

Estimating Sub-pixel to Regional Winter Crop Areas using Neural Nets

The current work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on sub-pixel to regional scales. The methodology is based on the assumption that within mixed pixels land cover variations are reflected by changes in the related hyper-temporal profiles of the Normalised Difference Vegetation Index (NDVI). We evaluated if changes in the fractional winter crop coverage are reflected in changing shapes of annual NDVI profiles and can be detected by using neural networks. The neural nets were trained on reference data obtained from high resolution Landsat TM/ETM images and additional ancillary data readily available (CORINE land cover). The proposed methodology was applied in a study region in central Italy to estimate winter crop areas between 1988 and 2002 from 1 km resolution NOAA-AVHRR profiles. The accuracy of the estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on sub-pixel (cross-validated R2 between 0.7 and 0.8) to regional scales (normalized RMSE: 10%) and proved to have a significantly higher forecast capability than other methods used previously for the same study area.JRC.DG.G.3-Monitoring agricultural resource

Natural and anthropogenic changes to mangrove distributions in the Pioneer River Estuary (QLD, Australia)

We analyzed a time series of aerial photographs and Landsat satellite imagery of the Pioneer River Estuary (near Mackay, Queensland, Australia) to document both natural and anthropogenic changes in the area of mangroves available to filter river runoff between 1948 and 2002. Over 54 years, there was a net loss of 137 ha (22%) of tidal mangroves during four successive periods that were characterized by different driving mechanisms: (1) little net change (1948– 1962); (2) net gain from rapid mangrove expansion (1962–1972); (3) net loss from clearing and tidal isolation (1972–1991); and (4) net loss from a severe species-specific dieback affecting over 50% of remaining mangrove cover (1991–2002). Manual digitization of aerial photographs was accurate for mapping changes in the boundaries of mangrove distributions, but this technique underestimated the total loss due to dieback. Regions of mangrove dieback were identified and mapped more accurately and efficiently after applying the Normalized Difference Vegetation Index (NDVI) to Landsat Thematic Mapper satellite imagery, and then monitoring changes to the index over time. These remote sensing techniques to map and monitor mangrove changes are important for identifying habitat degradation, both spatially and temporally, in order to prioritize restoration for management of estuarine and adjacent marine ecosystems

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks

Utilizing Satellite Fusion Methods to Assess Vegetation Phenology in a Semi-Arid Ecosystem

Dryland ecosystems cover over 40% of the Earth’s surface, and are highly heterogeneous systems dependent upon rainfall and temperature. Climate change and anthropogenic activities have caused considerable shifts in vegetation and fire regimes, leading to desertification, habitat loss, and the spread of invasive species. Modern public satellite imagery is unable to detect fine temporal and spatial changes that occur in drylands. These ecosystems can have rapid phenological changes, and the heterogeneity of the ground cover is unable to be identified at course pixel sizes (e.g. 250 m). We develop a system that uses data from multiple satellites to model finer data to detect phenology in a semi-arid ecosystem, a dryland ecosystem type. The first study in this thesis uses recent developments in readily available satellite imagery, coupled with new systems for large-scale data analysis. Google Earth Engine is used with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to create high resolution imagery from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). The 250 m daily MODIS data are downscaled using the 16-day, 30 m Landsat imagery resulting in daily, 30 m data. The downscaled images are used to observe vegetation phenology over the semi-arid region of the Morley Nelson Snake River Birds of Prey National Conservation Area in Southwestern Idaho, USA. We found the fused satellite imagery has a high accuracy, with R2 ranging from 0.73 to 0.99, when comparing fusion products to the true Landsat imagery. From these data, we observed the phenology of native and invasive vegetation, which can help scientists develop models and classifications of this ecosystem. The second study in this thesis builds upon the fused satellite imagery to understand pre-and post-fire vegetation response in the same ecosystem. We investigate the phenology of five areas that burned in 2012 by using the fusion imagery (daily) to derive the normalized difference vegetation index (NDVI, a measure of vegetation greenness) in areas dominated by grass (n=4) and shrub (n=1). The five areas also had a range of historical burns before 2012, and overall we investigated the phenology of these areas over a decade. This proof of concept resulted in observations of the relationship between the timing of fire and the vegetation greenness recovery. For example, we found that early and late season fires take the longest amount of time for vegetation greenness to recover, and that the number of historical fires has little impact in the vegetation greenness response if it has already burned once, and is a grass-dominated region. The greenness dynamics of the shrub-dominated study site provides insight into the potential to monitor post-fire invasion by nonnative grasses. Ultimately the systems developed in this thesis can be used to monitor semi-arid ecosystems over long-time periods at high spatial and temporal resolution