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Use of Spatial Remote Sensing to Study the Temporal Evolution of the Water Retention of Al Massira Dam in Morocco

Bounif Mohammed, Rahimi Abdelmejid, Boutafoust Rachid, El Mjiri Ikram

Journal of Ecological Engineering

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2023

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Vol. 24, nr 2

340--349

EN

In Morocco, irrigated agriculture is still very much linked to the climate and the water retention of dams. With climate change, this country is experiencing recurrent drought, which has led to deficits in water inflow from the rivers to the various dams. The Al Massira dam, the area of study, does not escape this trend. This dam is the only surface water source for the irrigated area of Doukkala. Therefore, special attention must be paid to monitoring this resource at this dam. Thus, the proposed study examined the possibilities offered by spatial remote sensing to improve the current information system. It aims to evaluate this dam’s reservoir by exploiting the data generated by using satellite images. The Landsat satellite images were used to assess the area of this dam by adopting an approach combining spectral indices with thresholding. Then, the existing relationship between the area of the dam lake were examined, determined by spatial remote sensing and its water retention measured in situ. The results obtained revealed a strong correlation between the two parameters. Therefore, a study was conducted to find the best model for predicting the dam’s impoundment based on its lake. The second-degree polynomial model showed a better performance. Given the results obtained, it is recommended to use geospatial methods in the current and prospective monitoring and steering system of water resources.

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Features of Creating a System of Space Monitoring of Water-Supplied Territories for Irrigation in the South of Kazakhstan

Malakhov Dmitry V., Tskhay Mikhail, Kalashnikov Alexander A., Bekmukhamedov Nurlan E., Kalashnikov Pavel A., Baizakova Aigul

Journal of Ecological Engineering

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2022

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Vol. 23, nr 11

202--216

EN

The location of a significant part of the agricultural territories of Kazakhstan in the risk agriculture zone implies the development and further application of an objective monitoring system for irrigated territories. The purpose of the study was to develop methods for on-the-spot and long-term recognition of irrigated massifs and verification of methods in the conditions of the territories of southern Kazakhstan. The paper describes the methods of on-the-spot recognition of irrigated fields, the general assessment of irrigated areas for the growing season, as well as the method of recognizing promising areas for irrigation. The on-the-spot recognition of the fields is based on the use of such spectral indices as the Global Vegetation Moisture Index, Green Normalized Difference Vegetation Index, Normalized Difference Vegetation Index, and the xanthophyll index, combined into a single system by the Decision Tree algorithm. The assessment of irrigated areas is based on differences in the physiological state of plants in conditions of normal water supply and plants experiencing a lack of moisture. The evaluation system includes the calculation of the temperature difference according to the corresponding satellite data and the calculation of the difference in vegetation indices for the same period. The difference in vegetation indices in irrigated fields has positive values due to a steady increase in green biomass, and the temperature difference, on the contrary, is negative or zero, since healthy plants, with normal water supply, actively evaporate moisture to maintain optimal temperatures of biochemical processes. To develop these methods, ground data from 2017–2021 were used. Verification of the methods with ground data demonstrated acceptable accuracy (87% in the on-the-spot assessment of irrigated fields; 60–90% in the general assessment of irrigated areas), while the methods have significant potential for further improvement.

3

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

Sobura Szymon, Widłak Małgorzata, Hejmanowska Beata, Muszyńska Joanna

Geomatics and Environmental Engineering

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2022

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Vol. 16, no. 4

187--213

EN

The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland. The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research.

4

High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

Sierra-Soler A., Adamowski J., Qi Z., Saadat H., Pingale S.

Journal of Water and Land Development

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2015

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no. 26

19--35

EN

Satellite remote sensing provides a synoptic view of the land and a spatial context for measuring drought impacts, which have proved to be a valuable source of spatially continuous data with improved information for monitoring vegetation dynamics. Many studies have focused on detecting drought effects over large areas, given the wide availability of low-resolution images. In this study, however, the objective was to focus on a smaller area (1085 km2) using Landsat ETM+ images (multispectral resolution of 30 m and 15 m panchromatic), and to process very accurate Land Use Land Cover (LULC) classification to determine with great precision the effects of drought in specific classes. The study area was the Tortugas-Tepezata sub watershed (Moctezuma River), located in the state of Hidalgo in central Mexico. The LULC classification was processed using a new method based on available ancillary information plus analysis of three single date satellite images. The newly developed LULC methodology developed produced overall accuracies ranging from 87.88% to 92.42%. Spectral indices for vegetation and soil/vegetation moisture were used to detect anomalies in vegetation development caused by drought; furthermore, the area of water bodies was measured and compared to detect changes in water availability for irrigated crops. The proposed methodology has the potential to be used as a tool to identify, in detail, the effects of drought in rainfed agricultural lands in developing regions, and it can also be used as a mechanism to prevent and provide relief in the event of droughts.

PL

Teledetekcja zapewnia synoptyczny ogląd Ziemi i kontekst przestrzenny do pomiarów efektów susz, co okazało się cennym źródłem ciągłych danych dla monitorowania dynamiki roślinności. Wiele badań koncentrowało się na śledzeniu skutków suszy na rozległych obszarach ze względu na łatwą dostępność obrazów o małej rozdzielczości. Celem przedstawionej pracy była jednak analiza mniejszego obszaru (1085 km2) z użyciem zdjęć Landsat ETM+ (wielospektralna rozdzielczość 30 m, panchromatyczna – 15 m) oraz przeprowadzenie dokładnej klasyfikacji użytkowania i pokrycia powierzchni terenu (ang. Land Use Land Cover – LULC) z zamiarem określenia z dużą dokładnością skutków suszy w poszczególnych klasach. Terenem badań była Tortugas-Tepezata zlewnia II rzędu rzeki Moctezuma, zlokalizowana w stanie Hidalgo w środkowym Meksyku. Klasyfikację LULC przeprowadzono z użyciem nowej metody bazującej na dostępnych dodatkowych informacjach i analizie trzech zdjęć satelitarnych wykonanych w różnym czasie. Opracowana na nowo metodyka LULC zapewniła dokładność w granicach od 87,88 do 92,42%. Spektralne wskaźniki dla roślinności i wilgotności gleby oraz roślin wykorzystano do wykrycia anomalii w rozwoju roślinności spowodowanych suszą. Ponadto zmierzono i porównano powierzchnię zbiorników wodnych w celu sprawdzenia zmian w dostępności wody do nawadniania upraw. Proponowana metodyka może służyć jako narzędzie szczegółowej identyfikacji skutków suszy w zasilanych opadowo obszarach rolniczych oraz jako mechanizm zapobiegania i łagodzenia skutków suszy.

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Improving unsupervised classification of forest types throught the use of spectral indices

Stępniewski P., Będkowski K.

Roczniki Geomatyki

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2009

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T. 7, z. 2

127-132

EN

The natural land cover class of Poland is almost entirely forest, but nowadays it is replaced by man-made ecosystems like arable, meadows, pastures and urban areas. The remaining forest forms islands, which are connected each other by corridors located mainly along rivers. Satellite images are an effective tool for the recognition of land cover structures which forms the first step for attaining a good understanding of landscape function. One of the commonly used remote sensing methods is unsupervised land use classification derived from the ISODATA (Iterative Self-Organizing Data Analysis Technique) algorithm. The logic of this routine was described by (Ball, Hall, 1965). Although the ISODATA routine applied to spectral bands acquired from multispectral scanners usually leads to good classification results, new approaches continue to be developed. In this paper we describe how classification results can be improved through the use of spectral indices instead of the original spectral bands. This will be explained using an example of analysing the structure of a forested area. The main objective of this research is to analyse how the vertical and horizontal structure of mixed forests can be assessed from satellite images. In our study we've used the IDRISI32 ISOCLUST routine, which is a specific implementation of the ISODATA approach (Eastman, 1999). The study was carried out on a 100 km2 subset of the Landsat scene p188r24 acquired on 7th May 2000 (http://www.landsat.org). This 7-band multispectral image with panchromatic band covers a region located in central Poland. The main land cover classes here are agricultural areas (arable fields, meadows, pastures) and forests. This landscape can be considered as representative of this part of Poland. The entire study area was also recorded using aerial photographs taken with a DMC 2000 digital camera. The resolution (ground sample distance) of these images was about 0.15 m. For our study NC- (natural color) and CIR-composites were produced.

PL

Naturalną formą pokrycia terenu w Polsce są lasy, które zostały jednakże zastąpione w znacznym stopniu przez obszary użytkowane rolniczo - pola uprawne, łąki i pastwiska. Pozostałości lasów tworzą w krajobrazie wyspy, które są połączone siecią korytarzy. Rolę korytarzy pełnią najczęściej lasy i zarośla położone wzdłuż cieków wodnych. Obrazy satelitarne są uważane za efektywne narzę dzie służące do rozpoznawania struktury i funkcjonowania krajobrazu na dużych obszarach. Jedną z popularnych metod analizy jest procedura klasyfikacji nienadzorowanej ISODATA. W niniejszej pra cy przedstawiono wyniki klasyfikacji sceny Landsat p188r24 za pomocą algorytmu ISOCLUST, który jest implementacją procedury ISODATA w programie IDRISI32. Głównym celem badań było spraw dzenie, czy można poprawić wyniki klasyfikacji nienadzorowanej przez zastosowanie indeksów spek tralnych, zamiast oryginalnych kanałów obrazu satelitarnego. Specjalną uwagę zwrócono na odróżnianie drzewostanów mieszanych o złożonej strukturze poziomej i pionowej w sytuacji, gdy scena jest klasyfikowana do niedużej liczby klas. Wykazano, że zastosowanie wskaźników spektralnych popra wia wynik klasyfikacji. Rozpoznano drzewostany jednogatunkowe, jednak nie udało się utworzyć oddzielnej klasy drzewostanów mieszanych - piksele reprezentujące te obiekty były klasyfikowane do klasy drzewostanów iglastych bądź liściastych. Wynik taki otrzymano zarówno w przypadku drzewo stanów mieszanych jednopiętrowych, jak i dwupiętrowych - z sosną w piętrze górnym i dębem lub innymi gatunkami liściastymi w piętrze dolnym. Autorzy sugerują, że wynik klasyfikacji można popra wić przez zwiększenie liczby tworzonych klastrów-skupień (parametr procedury ISOCLUST), a także przez uwzględnienie sezonowej zmienności lasów, czyli prowadzenie analizy na obrazach wieloczasowych.