Use of Remote sensing in the field of Agriculture: - Agriculture plays a dominant role in economies of both developed and undeveloped countries. Whether agriculture represents a substantial trading industry for an economically strong country or simply sustenance for a hungry, overpopulated one, it plays a significant role in almost every nation. The production of food is important to everyone and producing food in a cost-effective manner is the goal of every farmer, large-scale farm manager and regional agricultural agency. A farmer needs to be informed to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. These tools will help him understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions. Commodity brokers are also very interested in how well farms are producing, as yield (both quantity and quality) estimates for all products control price and worldwide trading. Satellite and airborne images are used as mapping tools to classify crops, examine their health and viability, and monitor farming practices. Agricultural applications of remote sensing include the following:
1. Crop type classification
2. Crop condition assessment
3. Crop yield estimation
4. Mapping of soil characteristics
5. Mapping of soil management practices
6. Compliance monitoring (farming practices)
The latest Earth observation technology allows day-to-day satellite data application in various fields of agricultural activities. Thanks to our extensive experiences obtained in various research and commercial projects in the past the agricultural applications can be rated as one of the key activities of our company.
Crop type identification
Crop type identification and mapping has a number of important aspects. It can serve for production statistics together with yield prediction, mapping soil productivity, assessment of crop damage and monitoring of farming activities. These include identifying the crop types (winter and spring cereals, rapeseed, sugar beet, potato, maize, grass, etc.) and delineating their parcel extent. Crop type identification is often based on multitemporal, multispectral high resolution imagery, while parcel boundaries delineation is more often based on very-high resolution imagery.
Traditional methods of obtaining this information are statistical estimates or ground surveying. Remote sensing offers an efficient means of collecting the information, in order to map crop type and acreage. Remote sensing can also provide state information about the health of the vegetation. The spectral reflections vary with respect to changes in the phenology and crop health. This can be measured and monitored by multispectral sensors. Interpretations from remotely sensed data and geographic information system (GIS) combined with ancillary data provide information of ownership, management practices, etc. This approach can be used e.g. in agricultural subsidy controls.
Crop type identification and mapping is based on use of multitemporal imagery to enhance the classification by taking into account changes in reflectance as a function of plant phenology. This in turn requires calibrated sensors and frequent repeat imaging throughout the vegetation season.
- crop maps
- map of crop canopy health
- statistical figures of crop acreages
Crop growth development monitoring
Crop growth development monitoring by means of remote sensing and simulation models give main indicators for quantitative yield predictions. Remote sensing data (e.g. middle resolution) with daily acquisitions serve as a main source of vegetation indicators. Often NDVI (Normalized Differential Vegetation Index), DMP (Dry Matter Production) and other indicators are calculated in time series (daily or decadal composites) to monitor crop growth. Detailed spatial and time analysis results in yield predictions. Crop growth and soil water mechanistic simulation models can be employed in order to receive complementary information on crop growth development. These indicators provide information on potential and limited crop growth given a local meteorological and soil conditions calculated for different parameterized crop types. Assimilation of remote sensing parameters into simulation models enhances the analysed crop indicators. Prediction of drought can be based on both, purely remote sensing measurements but also on employing simulation models.
Final quantitative yield prediction can be also based on both sources of crop indicators. The scale of the output statistics depend upon the scale of input data (remote sensing images, meteorological data, soil maps, etc.) but also more notably on scale of collected reference measured yields. Yield prediction together with acreage estimates is used for production statistics.
- maps of vegetation indicators
- analysis of vegetation development
- drought monitoring
- crop yield prediction (national / regional)
Precision farming (or agriculture) is a new technology that allows farmers to adjust for within-field variability in a number of characteristics like soil fertility and weed populations. The use of global positioning system (GPS) to identify position within the field in the real time is one of the technological key elements. With the information of the position and on-board sensors, farm equipment can guide applications of crop inputs like fertilizers and herbicides or monitor crop yields. Precision farming has the potential to reduce costs through more efficient and effective applications of crop inputs. It can also reduce environmental impacts by allowing farmers to apply inputs only where they are needed at the appropriate rate.
Remote sensing is one of the main sources of the information on the crop and soil spatial variation with the fields. Several biophysical variables can be derived from RS images to assess degree of spatial variation as well as producing categorical maps separating parts of the fields according to the crop status.
- segmentation of vegetation cover (airborne and satellite images)
- segmentation of yield maps
- application maps preprocessing
- geostatistical analysis of spatial variation
- sampling optimisation
Abandoned Land Monitoring
The process of land abandonment has become widespread in the last decades in all CEEC reflecting substantial political and economical changes in this period. Nowadays, it represents a serious issue and if not solved in time, the abandonment of productive agricultural lands would further grow. Besides the major social and economical impact, the process of land abandonment has also serious ecological consequences. It endangers valuable habitats to get degraded. Particularly biodiversity of semi-natural grasslands seems to be mostly affected by land abandonment. Sustainable use and management of abandoned areas and prevention of abandonement land area enlargement is one of the policy goals both on European and national level. Assessment of the policy strategies require clear measures based on the figures on evolution of actual abandoned land area. Earth observation data can contribute to abandoned land monitoring, extent clarification and evolution dynamics.
- actual or retrospective maps of abandoned areas
- analysis of changes
- scenario development