Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets.

Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements

Agricultural crop abandonment negatively impacts local economy and environment since land,as a resource for agriculture,is not optimally utilized.To take necessary actions to rehabilitate abandoned agricultural lands,the identification of the spatial distribution of these lands must be acknowledged.While optical images had previously illustrated potentials in the identification of agricultural land abandonment,tropical areas often suffer cloud coverage problem that limits the availability of the imageries.Therefore,this study was conducted to investigate the potential of ALOS-1 and 2(Advanced Land Observing Satellite-1 and 2)PALSAR(Phased Array L-band Synthetic Aperture Radar)images for the identification and classification of abandoned agricultural crop areas,namely paddy,rubber and oil palm fields.Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR;nonetheless,oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes.The accuracy obtained for these abandoned lands of paddy,rubber and oil palm was 93.33%±0.06%,78%±2.32%and 63.33%±1.88%,respectively.This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops.The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.

Full polarimetric PALSAR-based land cover monitoring in Cambodia for implementation of REDD policies

Forest cover monitoring plays an important role in the implementation of climate change mitigation policies such as Kyoto protocol and Reducing Emissions from Deforestation and Forest Degradation(REDD).In this study,we have monitored land cover using the PALSAR(Phased Array type L-band Synthetic Aperture Radar)full polarimetric data based on incoherent target decomposition.Supervised classification technique has been applied on Cloude
Pottier decomposition,Freeman
Durden three component,and Yamaguchi four component decomposition for accurate mapping of different types of land cover classes.Based on confusion matrix derived from the predicted and defined pixels,the evergreen and sparsely deciduous forests have shown high producer’s accuracy by Freeman
Durden three component and Yamaguchi four component classifications.The overall accuracy of Maximum Likelihood Classification by Yamaguchi four component is 94.1%with 0.93 kappa coefficient as compared to the 90.3%with 0.88 kappa coefficient by Freeman
Durden three component and 89.7%with 0.88 kappa coefficient by Cloude
Pottier decomposition.High accuracy of classification in a forested area using full polarimetric PALSAR data may have been because of high penetration of L-band SAR.The content of this study could be useful for the forest cover mapping during cloudy days needed for proper implementation of REDD policies in Cambodia.