Analyse diachronique grâce aux images Landsat de la dynamique spatiale des forêts sacrées du haut bassin du Niger en république de Guinée

La présente étude porte sur l’analyse diachronique, entre 1986 et 2017, de la dynamique spatiale de 20 forêts sacrées du bassin du Haut Niger en république de Guinée. L’interprétation des images satellites Landsat TM (1986, Landsat OLI/TIRS (2017), ainsi que les relevés de terrain, ont été utilisés. L’étude a révélé que malgré leur caractère sacré, la surveillance des sites forestiers étudiés pose problème. L’analyse diachronique de l’évolution spatiale de chacune des 20 forêts étudiées montre, à l’exception d’un seul site, une tendance régressive de l’ensemble des superficies initiales qui, sont passées de 2 581,10 ha en 1986, à 1 206,6 ha en 2017. Durant les trois décennies, la superficie initiale totale des 20 forêts a diminué de 57,5 % en moyenne, avec un taux annuel moyen de 3,8 %. Cette réduction considérable de la superficie est principalement liée à l’agriculture (63%). La pression démographique, la proximité des villages, l’affaiblissement des systèmes traditionnels de gestion et l’influence de l’islam, sont à l’origine de cette déforestation. La gestion foncière des terres dans les terroirs villageois du bassin est nécessaire pour la sauvegarde de ce patrimoine écologique.

Analysis of Urban Surface Temperature Using Remote Sensing and Geographic Information System (GIS)

The study analysed variation in surface temperature (ST) in Makurdi Urban Area (MUA), Northcentral Nigeria. A total of 12 Landsat TM/ETM+ images were acquired in January, April and June of 1991, 1996, 2001 and 2006. The ST was estimated from the 12 Landsat TM/ETM+ images, grouped into seven classes, and the area of each ST class was determined using remote sensing and Geographic Information System (GIS). The ST magnitudes vary spatially from 27.5oC (water bodies) to 50.7oC (built-up land), representing an intensity of 23.2oC. The mean seasonal ST varies from 32.4oC-34.5oC (cool-dry season), 35.5oC-38.8oC (hot-dry season) and 30.8oC-31.4oC (hot-wet season). The mean annual ST has increased from 32.9oC (1991) to 35.9oC (2006) with ST intensity of 3.0oC. The ST classes of 27oC-29oC and 33oC-37oC recorded the highest loss and gain in area of -126.5km2 and 94.5km2 whereas ST classes of 29oC-33oC and 41oC-45oC recorded the least and highest per centage change in area of 22% and 768%. The result showed decreasing and increasing trends in the areas of cooler and warmer surfaces, which are attributed to increase in anthropogen surface materials, with higher heat storage capacities, due to urbanisation.

Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery

Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.

ISO CLUSTER CLASSIFIER BY ARCGIS FOR UNSUPERVISED CLASSIFICATION OF THE LANDSAT TM IMAGE OF REYKJAVÍK

The paper presents the use of the Landsat TM image processed by the ArcGIS Spatial Analyst Tool for environmental mapping of southwestern Iceland, region of Reykjavik. Iceland is one of the most special Arctic regions with unique flora and landscapes. Its environment is presented by vulnerable ecosystems of highlands where vegetation is affected by climate, human or geologic factors: overgrazing, volcanism, annual temperature change. Therefore, mapping land cover types in Iceland contribute to the nature conservation, sustainable development and environmental monitoring purposes. This paper starts by review of the current trends in remote sensing, the importance of Landsat TM imagery for environmental mapping in general and Iceland in particular, and the requirements of GIS specifically for satellite image analysis. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The data used in this research include Landsat TM image which was captured using GloVis and processed in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1) coordinate projecting, 2) panchromatic sharpening, 3) inspection of raster statistics, 4) spectral bands combination, 5) calculations, 6) unsupervised classification, 7) mapping. The classification was done by clustering technique using ISO Cluster algorithm and Maximum Likelihood Classification. This paper finally presents the results of the ISO Cluster application for Landsat TM image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat TM image processing in ArcGIS.The results of the classification present landscapes divided into eight distinct land cover classes: 1) bare soils; 2) shrubs and smaller trees in the river valleys, urban areas including green spaces; 3) water areas; 4) forests including the Reykjanesfólkvangur National reserve; 5) ice-covered areas, glaciers and cloudy regions; 6) ravine valleys with a sparse type of the vegetation: rowan, alder, heathland, wetland; 7) rocks; 8) mixed areas. The final remarks include the discussion on the development of machine learning methods and opportunities of their technical applications in GIS-based analysis and Earth Observation data processing in ArcGIS, including image analysis and classification, mapping and visualization, machine learning and environmental applications for decision making in forestry and sustainable development.

SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

Summary The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from -112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region.

Remote Sensing Monitoring of Advancing and Surging Glaciers in the Tien Shan, 1990–2019

The advancing of glaciers is a manifestation of dynamic glacial instability. Glaciers in the Tien Shan region, especially in the Central Tien Shan, show instability, and advancing glaciers have been recently detected. In this study, we used Landsat TM/ETM+/OLI remote sensing images to identify glaciers in the Tien Shan region from 1990 to 2019 and found that 48 glaciers advanced. Among them, thirty-four glaciers exhibited terminal advances, and 14 glaciers experienced advances on the tributary or trunk. Ten of the glaciers experiencing terminal advances have been identified as surging glaciers. These 48 glaciers are distributed in the western part of the Halik and Kungey Mountain Ranges in the Central Tien Shan, and Fergana Mountains in the Western Tien Shan, indicating that the Tien Shan is also one of the regions where advancing and surging glaciers are active. From 1990 to 2019, a total of 169 times advances occurred on 34 terminal advancing glaciers in the Tien Shan region; the highest number of advancing and surging of glaciers occurred in July (26 and 14 times, respectively). With reference to the existing literature and the present study, the surge cycle in the Tien Shan is longer than that in other regions at high latitudes in Asia, lasting about 35–60 years. Surging glaciers in the Tien Shan region may be affected by a combination of thermal and hydrological control. An increase in temperature and precipitation drives surging glaciers, but the change mechanism is still difficult to explain based on changes in a single climate variable, such as temperature or precipitation.