Spectral radiance characteristics and vegetative indices of crops -A ground based remote sensing technique

The spectral radiance characteristics and vegetation indices like simple difference, ratio vegetation, normalised vegetation perpendicular vegetation transformed vegetation and tasseled cap transformation of mung been sunflower and groundnut crops at different growth stages have been studied. The experiment was conducted in post rainy season during 1990-91 in the farm of Agricultural College. Pune using hand held multi-spectral radiometer. The significance of spectral variation of radiance and vegetative indices with respect to the phenological stages are discussed.

Mapping buried paleogeographical features of the Nile Delta (Egypt) using the Landsat archive

The contribution highlights the use of Landsat spectral-temporal metrics (STMs) for the detection of surface anomalies that are potentially related to buried near-surface paleogeomorphological deposits in the Nile Delta (Egypt), in particular for a buried river branch close to Buto. The processing was completed in the Google Earth Engine (GEE) for the entire Nile Delta and for selected seasons of the year (summer/winter) using Landsat data from 1985 to 2019. We derived the STMs of the tasseled cap transformation (TC), the Normalized Difference Wetness Index (NDWI), and the Normalized Difference Vegetation Index (NDVI). These features were compared to historical topographic maps of the Survey of Egypt, CORONA imagery, the digital elevation model of the TanDEM-X mission, and modern high-resolution satellite imagery. The results suggest that the extent of channels is best revealed when differencing the median NDWI between summer (July/August) and winter (January/February) seasons (ΔNDWI). The observed difference is likely due to lower soil/plant moisture during summer, which is potentially caused by coarser-grained deposits and the morphology of the former levee. Similar anomalies were found in the immediate surroundings of several Pleistocene sand hills (“geziras”) and settlement mounds (“tells”) of the eastern delta, which allowed some mapping of the potential near-surface continuation. Such anomalies were not observed for the surroundings of tells of the western Nile Delta. Additional linear and meandering ΔNDWI anomalies were found in the eastern Nile Delta in the immediate surroundings of the ancient site of Bubastis (Tell Basta), as well as several kilometers north of Zagazig. These anomalies might indicate former courses of Nile river branches. However, the ΔNDWI does not provide an unambiguous delineation.

Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy

This paper is focused on the use of satellite Sentinel-2 data for assessing their capability in the identification of archaeological buried remains. We selected the “Tavoliere delle Puglie” (Foggia, Italy) as a test area because it is characterized by a long human frequentation and is very rich in archaeological remains. The investigations were performed using multi-temporal Sentinel-2 data and spectral indices, commonly used in satellite-based archaeology, and herein analyzed in known archaeological areas to capture the spectral signatures of soil and crop marks and characterize their temporal behavior using Time Series Analysis and Spectral Un-mixing. Tasseled Cap Transformation and Principal Component Analysis have been also adopted to enhance archaeological features. Results from investigations were compared with independent data sources and enabled us to (i) characterize the spectral signatures of soil and crop marks, (ii) assess the performance of the diverse spectral channels and indices, and (iii) identify the best period of the year to capture the archaeological proxy indicators. Additional very important results of our investigations were (i) the discovery of unknown archaeological areas and (ii) the setup of a database of archaeological features devised ad hoc to characterize and categorize the diverse typologies of archaeological remains detected using Sentinel-2 Data.

ANALYSIS OF SOIL DEGRADATION AND DESERTIFICATION IN SOUTHERN BALKASH DESERT BASED ON GIS

Desertification and degradation provide a clear picture of global environmental and socio-economic issues. Most of Kazakhstan is located in a desert region, including the suburbs of South Balkhash. The reason is that desertification of the region has a strong influence on natural and anthropogenic factors. To consider the geomorphological state of the region and the problem of desertification of the territory, it is necessary to determine the importance of the process of relief of geological structure and relief of tectonics. In recent years, the environmental situation in Balkhash has deteriorated sharply not only as a result of river flow regulation, but also as a result of non-commercial economic activities. Therefore, it is very important to assess the situation of desertification and degradation in the Balkhash region. Desert vegetation has been identified, since information in the spectral range is often insufficient to describe the state of plants, plant indices often develop by combining two or more spectral bands. Land cover index is the percentage of vegetation over a given surface area. Remote sensing information was used to detect the entire land cover. Remote sensing with time and space limitations is widely used to classify vegetation cover. In this work, the proportion of vegetation was estimated by NDVI. The proportion of land cover is based on the relationship between NDVI (NDVIS) and NDVI (NDVIV) in the soil. Using the NDVI index, land cover zones were determined based on satellite images of 2006 and Landsat-5 from 2011. TCT (Tasseled Cap Transformation) coefficients are used in the widest range of problems solved using Earth remote sensing data: from recognition of the coastline of water bodies to determination of forest disturbances. Stressful vegetation may be an indirect sign of the presence of salt in soils. Saline soils are usually characterized by poorly planted areas. A normalized differential salinity index (NDSI) was also determined.

The method for water body information extraction in complex environment using GF-1 WFV images

Water body is one of the most active and important earth resources, and which has a profound impact on the natural system and human society. In order to acquire surface water body information quickly, accurately and efficiently, the method of water body information extraction using remote sensing imagery has attracted the attention of many searchers. On the basis of sorting out relevant research results of water body information extraction using remote sensing imagery, this paper proposed the method of water body information extraction based on the tasseled cap transformation for complex environments such as shadow and dense vegetation. First, radiometric calibration and atmospheric correction were carried out for remote sensing images. Then, the tasseled cap transformation was performed to obtain the greenness component and wetness component. Finally, the model of water body information extraction based on the tasseled cap transformation was constructed, and the water body information was extracted. In a region of Hunan province, China, the experiment using GF-1 WFV remote sensing image shows that the extracted water body information has a clear boundary and complete shape, and the Kappa coefficient, overall accuracy and user accuracy are 0.89, 92.72%, and 88.04%, respectively.

Drought area of agricultural land using Tasseled Cap Transformation (TCT) method in Ciampel Subdistrict Karawang Regency

Drought is water availability that is far below the water needs for life, agriculture, economic activities, and the environment. The impact of severe drought in Indonesia occurred in 2015 due to the strong El Nino phenomenon and positive IOD. The Regional Disaster Management Agency (BPBD) of Karawang Regency noted that drought in 14 villages spread across three subdistricts in Karawang Regency expanded in 2019. One of them is the Ciampel subdistrict. The purpose of this research is to analyze the drought of agricultural land based on green vegetation, soil organic content, and soil moisture using Tasseled Cap Transformation (TCT) method in Ciampel Subdistrict, Karawang Regency in 2015 and 2019. This research uses Landsat 8 OLI imagery in August 2015, September 2015, July 2019, and September 2019. Agricultural land drought includes three indices, namely the brightness index, wetness index and greenness index. Overlay and scoring three drought parameters to making the map drought of agriculture land with four classes such as normal, moderate, high, and very high drought classes. The results show that the drought occurred in 2015 and 2019, but the dry area is more expansive in 2015 than 2019.

Investigation of Urban Environment Using Tasseled Cap Transformation

Urban settlements have more complex environments, in unremitting fruition, where most of the world population lives. Most of the cities in developing countries have been developed without a rationale, and the life conditions are repeatedly insufferable. For this research work, NDVI is particularly used to assess the status of vegetation cover. Tasseled cap is another index that creates three band images for this study. Brightness, greenness, wetness are the three bands that represent the area under consideration. The present study aims particularly at comparing high NDVI area and greenness values given by tasseled cap and low NDVI values and high brightness values and status of urban environment. Based on the overlapping of tasseled cap image, an NDVI image is observed in which most of the area of healthy vegetation is located in the north west and south east part of Kharagpur city, which extended from south west to north east and north to south respectively.

Soil Moisture Mapping at Paddy Field in Indramayu Residence Using Landsat 8 OLI/TIRS

Drought monitoring is important for the paddy planting planning. Remote sensing is one tool can be used for it. Paddy field monitoring based on the soil moisture gives much knowledge related to the water content in the soil. Soil moisture analysis in this study is using Normalized Different Water Index (NDWI), Linear Soil Moisture (LSM), and Tasseled Cap. Soil moisture change could explain based on calculation results of NDWI, Linear Soil Moisture (LSM), and Tasseled Cap Transformation (TCT). Based on the results has explained that the driest year occurs in 2015 and June 2016 has a higher soil moisture. Comparison with the radar shows that the results of soil moisture analysis with Landsat was effective can be used with results relatively close to the radar results.

BEECH TREE DENSITY ESTIMATION USING SENTINEL-2 DATA (CASE STUDY: KHYROUD FOREST)

Obtaining information about forest attributes is essential for planning, monitoring, and management of forests. Due to the time and cost consuming of Tree Density (TD) using field measurements especially in the vast and remote areas, remote sensing techniques have gained more attention in scientific community. Khyroud forest, a part of Hyrcanian forest of Iran, with a high species biodiversity and growing volume stock plays an important role in carbon storage. The aim of this study was to assess the capability of Sentinel-2 data for estimating the tree density in the Khyroud forest. 65 square sample plots with an area of 2025 m2 were measured. In each sample plot, trees with diameter at the breast height (DBH) higher than 7.5-cm were recorded. The quality of Sentinel-2 data in terms of geometric correction and cloud effect were investigated. Different processing approaches such as vegetation indices and Tasseled Cap transformation on spectral bands in combination with an empirical approach were implemented. Also, some of biophysical variables were computed. To assess the model performance, the data were randomly divided into parts, 70% of sample plots were used for modelling and 30% for validation. The results showed that the SVR algorithm (linear kernel) with a relative RMSE of 23.09% and a R2 of 0.526 gained the highest performance for tree density estimation.