scholarly journals ANALYSIS OF THE DEGREE OF GRASSLAND DEGRADATION USING REMOTE SENSING

2021 ◽  
Vol 22 (80) ◽  
pp. 201-219
Author(s):  
Jaiza Santos Motta ◽  
César Claudio Cáceres Encina ◽  
Eliane Guaraldo ◽  
Ariadne Brabosa Gonçalves ◽  
Roberto Macedo Gamarra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Grassland Degradation ◽  
Tropical Savanna ◽  
Degradation Index ◽  
Brazilian Savannah ◽  
Pasture Degradation ◽  
Multitemporal Remote Sensing

The objective of this study is to adapt the calculations of the Pasture Degradation Index (GDI) to the Brazilian savannah using medium spatial resolution satellite image for the dry season. Vegetation cover is the main evaluation parameter used to calculate the GDI. The extreme ranges of the grazing class were determined by the NDVI histogram of a single date. Pasture cover was distinguished into five classes called Vegetable Pasture Cover (GVC), derived from NDVI and compared with five other classes derived from field photographs, named Green Coverage Percentage (GCP). The similarity between GVC and GVP demonstrated that GVC can be used to classify pasture cover. As a product of GVC, GDI was obtained. The GDI showed that pasture degradation in Paraíso das Águas is very serious. Extremely severe and Severe degradation occupy 9.28% and 25.22% of the study area, moderate and light degradation of pasture occupy 8.29% and 4.50%, respectively, and the non-degradation area covers 1.43 % of pastures. The results suggest the possibility of applying the GDI, originally developed for natural fields and multitemporal remote sensing data, to evaluate the conditions of the tropical savanna planted fields by means of a unique image.

scholarly journals Remote-Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China

2019 ◽  
Vol 11 (24) ◽  
pp. 3030 ◽  
Author(s):  
Yanlin Yang ◽  
Jinliang Wang ◽  
Yun Chen ◽  
Feng Cheng ◽  
Guangjie Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Satellite Image ◽  
Animal Husbandry ◽  
Transformation Rate ◽  
Grassland Degradation ◽  
Degradation Index ◽  
Monitoring Period ◽  
Degraded Grassland

Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDIg) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDIg in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDIg based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDIg and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDIrs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km2 in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km2, with a loss of 66.53 km2. From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km2 and 49.93 km2, respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km2 from 91.58 km2 in 2008 to 132.74 km2 in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km2, accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals A 3D Reconstruction Framework of Buildings Using Single Off-Nadir Satellite Image

2021 ◽  
Vol 13 (21) ◽  
pp. 4434
Author(s):  
Chunhui Zhao ◽  
Chi Zhang ◽  
Yiming Yan ◽  
Nan Su
Keyword(s):  
Remote Sensing ◽  
3D Reconstruction ◽  
Spatial Resolution ◽  
Satellite Image ◽  
Geometric Constraints ◽  
Single Image ◽  
Multiple Images ◽  
Angle Size ◽  
Reconstruction Methods ◽  
Mesh Models

A novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed in this paper. Compared with the traditional methods of reconstruction using multiple images in remote sensing, recovering 3D information that utilizes the single image can reduce the demands of reconstruction tasks from the perspective of input data. It solves the problem that multiple images suitable for traditional reconstruction methods cannot be acquired in some regions, where remote sensing resources are scarce. However, it is difficult to reconstruct a 3D model containing a complete shape and accurate scale from a single image. The geometric constraints are not sufficient as the view-angle, size of buildings, and spatial resolution of images are different among remote sensing images. To solve this problem, the reconstruction framework proposed consists of two convolutional neural networks: Scale-Occupancy-Network (Scale-ONet) and model scale optimization network (Optim-Net). Through reconstruction using the single off-nadir satellite image, Scale-Onet can generate water-tight mesh models with the exact shape and rough scale of buildings. Meanwhile, the Optim-Net can reduce the error of scale for these mesh models. Finally, the complete reconstructed scene is recovered by Model-Image matching. Profiting from well-designed networks, our framework has good robustness for different input images, with different view-angle, size of buildings, and spatial resolution. Experimental results show that an ideal reconstruction accuracy can be obtained both on the model shape and scale of buildings.

scholarly journals REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017

2018 ◽  
Vol 5 (2) ◽  
pp. 215
Author(s):  
Md Arafat Hassan ◽  
Rakibul Islam ◽  
Rehnuma Mahjabin
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Remote Sensing And Gis ◽  
Vegetation Coverage ◽  
Accurate Information ◽  
Sal Forest ◽  
Shed Light

This paper has been developed to capture the land coverage change in Gazipur Sadar Upazila with the help of remote sensing data of 44 years from 1973 to 2017. After acquiring the study area image of 1973, 1991, 2006 and 2017 supervised classification method has been used to get the accurate information from the satellite image and the whole outcome has been transformed into measurable unit (sq km) and graphs. The accuracy of land coverage was ranged from 85% to 89%. The outcome says that the acceleration of economic growth and pressure of huge population took a heavy toll on the vegetation coverage which decreased -199.7%. People are destroying vegetation coverage for building up settlements and infrastructure. In the year 2017, the map shows that the built-up area increased 312.9% for industry, settlement and agricultural purpose. Moreover agricultural land also drops down from 42% to 32%.  The rapid rate of decreasing vegetation coverage and small amount of existing vegetation coverage only 57 sq km (in 2017) is a red alert for the region. The Sal forest and other special flora species of that region is valuable resource for environment. This paper shed light on the fact that it is urgent to protect vegetation coverage so it will help the authority to make good policies and use other techniques to save vegetation coverage.

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

2020 ◽  
Vol 40 (10) ◽  
pp. 1028001
Author(s):  
陈世涵 Chen Shihan ◽  
李玲 Li Ling ◽  
蒋弘凡 Jiang Hongfan ◽  
居伟杰 Ju Weijie ◽  
张曼玉 Zhang Manyu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Satellite Remote Sensing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Environment Change ◽  
Sensing Data

scholarly journals Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change

2020 ◽  
Vol 12 (4) ◽  
pp. 688 ◽  
Author(s):  
Jacky Lee ◽  
Jeffrey A. Cardille ◽  
Michael T. Coe
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Spatial Resolution ◽  
Processing Speed ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wide Audience ◽  
Data Set ◽  
Mato Grosso

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.

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