Kyung-Do Lee

Predicting and mapping soil available water capacity in Korea

The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at -10 and -1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at -10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively). Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

Estimation of Paddy Field Area in North Korea Using RapidEye Images

Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields () were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).

Division of Soil Properties in Reclaimed Land of the Mangyeong and Dongjin River Basin and Their Agricultural Engineering Management

The physical and chemical properties of soil in the Mangyeong and Dongjin river basin had been investigated in order to establish the most optimum soil improvement plan on the reclaimed land. The total soil area by reclamation in Saemangeum basin is 113,971 ha. The classification by the distribution of soil series and soil texture is as following. 13 soil series including Chonnam, Buyong and Chonbuk series are period-unknown areas. Regarding the soil texture, they are fine silty ~ clayey very fine. From 1920s to 1960s, Mangyeong, Gwanghwal and Chonbuk series had coarse silty textured soil. After the 1970s, Mangyeong, Gwanghwal, Munpo, Yeompo, Poseung, Gapo and Hasa series have more sandy soil ~ moderately coarse loamy textured soil. Regarding the chemical properties, the concentrations of EC, Exch. , , and pH are high regardless of the time of reclamation. On the other hand, organic matter (OM) of top soil were 3.3~16.1 g . The organic matter contents were very low though the soil had been farmed for a long time. Furthermore, the deep soil had almost no organic matter with 5.6~1.1 g . The reason is believed that there had not been any movement of OM and clay because pressure or induced pans had been formed by large agricultural machineries and poor vertical drain. Regarding the forming of illuvial horizon (B layer) which tells the development extent of soil, only in the Hwapo reclaimed area where rice had been cultivated for past 90 years, Fe and Mn from top soil are deposited at underground 20~30 cm with 7~8 cm thickness by the movement of clay. It is believed that it had been possible because the earthiness is silty clay loam soil with relatively high content of clay. The soils are soil with concern of damage from sea water, soil on flimsy ground and sandy soil. Therefore, soil improvement for stable crop production can be expected; if the water table would be lowered by subsurface drainage, the water permeability would be enhanced by gypsum and organic matter, and the sandy soil would be replaced by red soil with high content of clay.

Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, NDVIUAV and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVIUAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVIUAV and other agro-meteorological factors were well reflected in the model.

Estimating the Amount of Nitrogen in Hairy Vetch on Paddy Fields using Unmaned Aerial Vehicle Imagery

Remote sensing can be used to provide information about the monitoring of crop situation. This study was conducted to estimate the amount of nitrogen present in paddy fields by measuring the amount of nitrogen in hairy vetch using an UAV (Unmaned Aerial Vehicle). NDVIs (Normalized Difference Vegetation Index) were calculated using UAV images obtained from paddy fields in Seocheon on May 14 th 2015. There was strong relationship between UAV NDVI and the amount of nitrogen in hairy vetch (R 2 =0.79). Spatial distribution maps of green manure nitrogen were generated on each paddy field using the nitrogen-vegetation index relations to help farmers determine the amount of N fertilizers added to their rice fields after the application of green manure such as hairy vetch.

Assessment of Energy Efficiency and Nutrient Balance in Organic Rice Farming Area

Energy efficiency and nutrient balance are good methods for environmental assessment of the environmentally-friendly agriculture. The objectives of this study were 1) to estimate the energy efficiency and nutrient balance of the organic rice farming, and 2) to suggest a solution to improvement the energy efficiency and nutrient balance. The set of estimation was performed at the organic rice farming area (8.9 ha) in Wanju-gun during the paddy cultivation period from 2006 to 2007. The organic farming complex consists of four weeding methods using 1) duck, 2) apple snail, 3) duck and apple snail and 4) hands and machinery. Results from this estimation should that the organic rice farming area was less efficient than conventional rice farming. The efficiency of organic farming area in 2006 was higher than in 2007. For the calculation of the nutrient balance, the N, P and K contents of input materials (cattle manure, milk vetch, mixed oilcake, rice bran, rice straw and barley straw) and output (farm products) were analysed. Annual environmental loads of N, P2O5 and K2O were estimated at 4.4 kg/10a, 13.8 kg/10a and 14.5 kg/10a, respectively. Cattle manure had the largest portion among the inputs items and nutrient concentration of cattle manure was high. Thus energy efficiency and nutrient balance depend on cattle manure input. Therefore it is necessary to control the manure input to improve the efficiency of organic rice farming.

Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels.

Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK

To understand the impact of 2015 spring drought on crop production of DPRK (Democratic People’s Republic of Korea), we analyzed satellite and weather data to produce 2015 spring outlook of rice paddy field and rice growth in relation to weather anomaly. We defined anomaly of 2015 for weather and NDVI in comparison to past 5 year-average data. Weather anomaly layers for rainfall and mean temperature were calculated based on 27 weather station data. Rainfall in late April, early May, and late May in 2015 was much lower than those in average years. NDVI values as an indicator of rice growth in early June of 2015 was much lower than in 2014 and the average years. RapidEye and Radarsat-2 images were used to monitor status of rice paddy irrigation and transplanting. Due to rainfall shortage from late April to May, rice paddy irrigation was not favorable and rice planting was not progressed in large portion of paddy fields until early June near Pyongyang. Satellite images taken in late June showed rice paddy fields which were not irrigated until early June were flooded, assuming that rice was transplanted after rainfall in June. Weather and NDVI anomaly data in regular basis and timely acquired satellite data can be useful for grasping the crop and land status of DPRK, which is in high demand.

The Study of Applicability to Fixed-field Sensor for Normalized Difference Vegetation Index (NDVI) Monitoring in Cultivation Area

The NDVI (Normalized difference vegetation index) is used as indicators of crop growth situation in remote sensing. To measure or validate the NDVI, reliable NDVI sensors have been needed. We tested new fixed-field NDVI sensor, “SRS (Spectral Reflectance Sensor)” developed by Decagon Devices, during Kimchi cabbage growing season at the cultivation area located in Gochang, Gangneung and Taebaek in Korea from 2014 to 2015. The diurnal variation of NDVI measured by SRS (SRS NDVI) showed a slight ∩-profile shape and was affected by water on the sensor surface. This means that SRS NDVI around noontime is resonable, except rainy day. Comparisons were made between the SRS NDVI and NDVI of used widely mobile sensor (Cropcircle NDVI). The comparisons indicate that SRS NDVI are close to Cropcircle NDVI (R=0.99). SRS NDVI time series displayed change of the plant height and leaf width of Kimchi cabbage. An obvious exponential relationship is found between SRS NDVI and the plant height (R 2 ≥0.92) and leaf width(R 2 ≥0.92) of Kimchi cabbage. Thus, SRS NDVI will be used as indicator of crop growth situation and a very powerful tool for evaluation of remote sensing NDVI estimates and associated corrections.