Chris J. Johannsen

Assessment of regional biomass-soil relationships using vegetation indexes

The development of photosynthetic active biomass in different ecological conditions, as indicated by normalized difference vegetation indices (NDVIs) is compared by performing a stratified sampling (based on soil associations) on data acquired over Indiana. Data from the NOAA-10 Advanced Very High Resolution Radiometer (AVHRR) were collected for the 1987 and 1988 growing seasons. An NDVI transformation was performed using the two optical bands of the sensor (0.58-0.68 mu m and 0.72-1.10 mu m). The NDVI is related to the amount of active photosynthetic biomass present on the ground. Statistical analysis of results indicate that land-cover types (forest, forest/pasture, and crops), soil texture, and soil water-holding capacity have an important effect on vegetation biomass changes as measured by AVHRR data. >

Optimization of training data required for neuro-classification

Classification of remotely sensed data with artificial neural networks is called neuro-classification, and this technique has shown great potential. The amount of data used for training a neural ne...

Identifying agricultural flood damage using Landsat imagery

During the first two weeks of July 2003, heavy precipitation occurred across the northern and central portions of Indiana, resulting in flooding and ponded water that damaged crops. Landsat 5 Thematic Mapper images were used to identify the level of damage in fields. A supervised classification and temporal change detection were performed with the help of ERDAS Imagine. To examine the recovery rate of crops over time, two methods were used: a change detection matrix and Delta Normalized Difference Vegetation Index. Both methods indicated an improvement in the conditions of the crops two weeks after the end of the heavy precipitation. Correlations between precipitation, crop damage, yield and unharvested area were weak. At the end of the season, the damage caused by flooding and excess precipitation did not greatly affect the yield of crops, especially corn. Soybeans suffered slightly from these rainfall events, and their yield was smaller than in previous years.

Using remote sensing to assess stand loss and defoliation in maize

Assessing hail and wind damage to crops is a difficult, laborintensive task. A quick and accurate method of determining losses could lead to better crop management decisions, more accurate insurance claim adjustment, and reduced expenses for the crop hail insurance industry. Radiometric data were collected in 1997, 1998, and 1999 in Indiana and Nebraska from field plots of maize, Zea mays L., subjected to varying levels of damage. Incremental differences in plant damage resulted in incremental differences in spectral responses. The red and near-infrared spectral bands provided the most discrimination among levels of damage. Classification of remotely sensed images by damage level was performed by extrapolating spectral information from areas where damage levels were known to adjacent unknown areas of damage. Depending on location, sensor, and date of data collection, it was possible to classify the degree of early-season stand loss at accuracies of 48 to 100 percent. For leaf loss during the late vegetative stages, it was possible to classify the degree of leaf loss at accuracies of 81 to 100 percent and, for leaf loss during the early reproductive stages, it was possible to classify damage at accuracies of 71 to 98 percent. These results indicate that remote sensing could be used to improve the accuracy of estimating crop damage as long as adequate ground reference for different levels of crop damage exists.

Analysis of seasonal variation in the Niger River interior delta using satellite data

Abstract The Niger River is one of the most important sources of water supply for human consumption and agriculture in Western Africa. Two Landsat‐5 Multispectral Scanner (MSS) images, corresponding to the dry and wet seasons, over a selected area of the Niger River interior delta were classified to produce a land cover/land use map that reflects the geo‐hydrological units of this area. To classify the satellite data, training statistics were generated using a clustering algorithm with parameter values that maximize the separability among spectral classes. Both dry and wet season images are required to obtain an accurate classification for evaluation of hydrological parameters. The spatial resolution of the MSS proved to be adequate for this kind of work, since all the major cover types and geographic features were correctly recognized.

Evaluation and accuracy assessment of high‐resolution IFSAR DEMs in low‐relief areas

The use of digital elevation models from remotely sensing systems has been restricted in the past to high‐relief areas due to the lack of appropriate resolution and accuracy to map micro‐relief variability in low relief areas. Interferometric synthetic aperture radar, a new technology that provides detailed elevation models from remotely sensed data, is evaluated. Main characteristics of this data are highlighted. Accuracy assessment is tested in detail for two high‐resolution acquisition modes using higher accuracy sources of data. The accuracy results using the root mean square (rms) error were better than expected according to mission specifications. However, at the checkpoint locations where the signal backscatter generates an elevation measure, the accuracy depends considerably upon the site‐specific surface characteristics, such as the land use, above ground biomass, adjacent forest areas and infrastructure features located within surrounding pixels.

Contributions of automatic crop surveys to agricultural development.

Automatic crop surveys contribution to agricultural technology in developed and underdeveloped countries

SITE-SPECIFIC SOIL MANAGEMENT

Farming practices are changing with the development of high technology tools, software, and hardware. Many of these developments are presented here to help explain the systems and some of the benefits of adoption. Remote sensing has developed to include unmanned aerial platforms of various sizes from less than 25 kilograms to larger and more sophisticated systems. Additional satellites with high spatial and spectral resolution now image the ground. Managing the soil system is a key to a successful site-specific cropping system; requiring the input of the farmers knowledge to properly implement a high tech program. The future of the technology is being driven by industry developments while the adoption of the technology lags behind. As farmers see economic benefits, adoptions will occur.

Information requirements for agriculture: The next decade

International agriculture needs improved capabilities for crop production monitoring and management data. Many countries, using an area frame sample, have begun to integrate GIS and remote sensing in their national crop inventory statistics programs and as the basis for famine early warning systems. The demand for accurate digital data has been heightened by the boom in precision farming which requires analysis of data collected at 1–5 meter spatial intervals. Manipulation and interaction of such data as digital soils maps, field boundary maps, drainage maps, yield monitor images, fertilizer, seed and chemical rate applications are primary to precision farming. Interest is building in the use of remotely sensed data to compare with yield image maps to assist in management decisions. The demand for digital data at all levels will increase dramatically as data are collected for local, regional and national statistics, the management of crop production, transportation to markets, crop insurance decisions, ma...

Assessment Of Regional Biomass-soil Relationships Using Vegetation Indexes

The development of photosynthetic active biomass in different ecological conditions, as indicated by normalized difference vegetation indices (NDVIs) is compared by performing a stratified sampling (based on soil assocations) on data acquired over Indiana. Data from the NOAA-10 AVHRR were collected for the 1987 and 1988 growing seasons. An NDVI transformation was performed using the two optical bands of the sensor (0.58-0.68 microns and 0.72-1.10 microns). The NDVI is related to the amount of active photosynthetic biomass present on the ground. Samples of NDVI values over 45 fields representing eight soil associations throughout Indiana were collected to assess the effect of soil conditions and acquisition date on the spectral response of the vegetation, as shown by the NDVIs. Statistical analysis of results indicate that land-cover types (forest, forest/pasture, and crops), soil texture, and soil water-holding capacity have an important effect on vegetation biomass changes as measured by AVHRR data. Acquisition dates should be selected with condideration of the phenological stages of vegetation. Sampling of AVHRR data over extended areas should be stratified according to physiographic units rather than man-made boundaries. This will provide more homogeneous samples for statistical analysis.