Comparison of pixel-and object-based classification in land cover change mapping

Land use/land cover (LULC) change occurs when humans alter the landscape, and this leads to increasing loss, fragmentation and spatial simplification of habitat. Many fields of study require monitoring of LULC change at a variety of scales. LULC change assessment is dependent upon high-quality input data, most often from remote sensing-derived products such as thematic maps. This research compares pixel-and object-based classifications of Landsat Thematic Mapper (TM) data for mapping and analysis of LULC change in the mixed land use region of eastern Ontario for the period 1995-2005. For single date thematic maps of 10 LULC classes, quantitative and visual analyses showed no significant accuracy difference between the two methods. The object-based method produced thematic maps with more uniform and meaningful LULC objects, but it suffered from absorption of small rare classes into larger objects and the incapability of spatial parameters (e.g. object shape) to contribute to class discrimination. Despite the similar map accuracies produced by the two methods, temporal change maps produced using post-classification comparison (PCC) and analysed using intensive visual analysis of errors of omission and commission revealed that the object-based maps depicted change more accurately than maximum likelihood classification (MLC)-derived change maps

Combination of optical and SAR sensors for monitoring biomass over corn fields

In this study, a cross-calibration approach was applied to combine RADARSAT-2 and RapidEye sensors for biomass monitoring over corn fields. First, RapidEye and RADARSAT-2 sensors were compared in terms of biomass estimation. Then the estimated biomass from RADARSAT-2 was cross-calibrated with respect to the biomass estimated from RapidEye. Combination of the optical and cross-calibrated Synthetic Aperture Radar (SAR) derived biomass was proposed to have higher temporal resolution biomass maps. Vegetation indices including normalized difference vegetation index (NDVI), red-edge triangular vegetation index (RTVI), simple ratio (SR) and red-edge simple ratio (SRre) were used for modeling of biomass estimation from RapidEye. Water Cloud Model (WCM) was also used for biomass estimation from RADARSAT-2. Data collected during SMAP Validation Experiment 2012 (SMAPVEX12) field campaign was used for validation. The results demonstrate that the accuracies of biomass estimations from RapidEye and RADARSAT-2 are close. For RapidEye, the highest accuracies derived from RTVI index with correlation coefficient (R) of 0.92 and Root Mean Square of (RMSE) of 118.18 gr/m 2 . The R values derived from RADARSAT-2 is 0.83 and its RM

Compact Polarimetry for Agricultural Mapping and Inventory: Preparation for Radarsat Constellation Mission

Agriculture and Agri-Food Canada (AAFC) has combined RADARSAT-2 C-band dual polarization data with optical imagery to map crop types across the agricultural extent of Canada yearly since 2009. In preparation for the launch of the RADARSAT Constellation Mission (RCM) primary research has been focused on incorporating similar-mode dual polarization RCM data in the operational system. The availability of the compact polarimetry (CP) mode with continuous coverage has important implications for crop mapping and inventory. CP mode on RCM has a circular transmit and two orthogonal linear receive structure and maintains phase information. The addition of CP data to AAFC's operational crop type mapping will expand the information that the current dual polarization Synthetic Aperture Radar (SAR) component provides. This will increase the SAR contribution from the simple intensity of backscatter to capturing the scattering char

Assessment of Multi-Frequency SAR for Crop Type Classification and Mapping

Annual and within-season crop type monitoring and mapping is an important ongoing consideration for governments, global agricultural monitoring organizations and private interests worldwide. Successful country-wide operational remote sensing-based inventories are well-established utilizing optical-only and optical/single frequency Synthetic Aperture Radar (SAR) combinations of data. However, the drawbacks of these data combinations are the requirement of multiple sources of imagery throughout the entire growing season, which impedes within-season analysis, and cloud cover effects on the optical data. Currently, C-band SAR data are available with continuous global coverage from Sentinel-1A and B, RADARSAT-2 and from the expected launch of the RADARSAT Constellation Mission (RCM). With current and expected launches of several other frequency (L-, P-, etc.) SAR missions over the next few years (SAOCOM, NISAR, etc.) the opportunity for continuous, multi-frequency SAR coverage edges toward reality. The JECAM SAR Inter-Comparison Experiment is a multi-year

Integration of synthetic aperture radar and optical satellite data for corn biomass estimation

Efforts to use satellites to monitor the condition and productivity of crops, although extensive, can be challenging to operationalize at field scales in part due to low frequency revisit of higher resolution space-based sensors, in the context of an actively growing crop canopy. The presence of clouds and cloud shadows further impedes the exploitation of high resolution optical sensors for operational monitoring of crop development

SAR speckle filtering and agriculture field size: Development of sar data processing best practices for the JECAM SAR intercomparison experiment

Utilizing Synthetic Aperture Radar (SAR) sensors for crop inventory and condition monitoring offers many advantages, particularly the ability to collect data under cloudy conditions. The JECAM SAR Inter-Comparison Exper

Using Dense Time-Series of C-Band Sar Imagery for Classification of Diverse, Worldwide Agricultural Systems

Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited