scholarly journals Optimal Land Cover Mapping and Change Analysis in Northeastern Oregon Using Landsat Imagery

2015 ◽  
Vol 81 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Michael Campbell ◽  
Russell G. Congalton ◽  
Joel Hartter ◽  
Mark Ducey
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Land Cover Mapping ◽  
Change Analysis

A Novel Unsupervised Sample Collection Method for Urban Land-Cover Mapping Using Landsat Imagery

2019 ◽  
Vol 57 (6) ◽  
pp. 3933-3951 ◽  
Author(s):  
Jiayi Li ◽  
Xin Huang ◽  
Ting Hu ◽  
Xiuping Jia ◽  
Jon Atli Benediktsson
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Sample Collection ◽  
Collection Method ◽  
Urban Land Cover

scholarly journals Perubahan lahan vegetasi berbasis citra satelit di DAS Citarum, Bandung, Jawa Barat

2021 ◽  
Vol 35 (1) ◽  
pp. 54
Author(s):  
Teguh Husodo ◽  
Yazid Ali ◽  
Siti Rodiatan Mardiyah ◽  
Sya Sya Shanida ◽  
Oekan S Abdoellah ◽  
...  
Keyword(s):  
Image Processing ◽  
Land Cover ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Land Cover Changes ◽  
Surrounding Area ◽  
Land Area ◽  
Change Analysis ◽  
Small Areas ◽  
Study Results

Abstrak DAS Citarum mengalami perubahan struktur lingkungan yang tinggi yang berakibat pada penurunan kualitas lingkungan, sehingga diperlukan gambaran mendetail mengenai kondisi perubahan lahan vegetasi di DAS Citarum. Tujuan dari penelitian ini untuk mengetahui proses perubahan luasan lahan vegetasi di DAS Citarum, Bandung, Jawa Barat. Penelitian ini menggunakan data penginderaan jauh dengan pendekatan kuantitatif. Pemetaan perubahan penutupan vegetasi di DAS Citarum menggunakan data citra Landsat multitemporal dengan perangkat lunak QGIS. Pada pelaksanaan penelitian ini, beberapa tahapan yang dilakukan, diantaranya pengolahan awal citra satelit (pre-processing), pengolahan citra digital (image processing), verifikasi data citra (NDVI), dan analisis perubahan penutupan lahan. Hasil studi menunjukkan bahwa terjadi penurunan luasan lahan vegetasi seluas 35% pada tahun 1989 – 2019 dengan rata-rata penyusutan luas lahan sebesar 0.64% setiap tahunnya dan penyusutan terbesar pada tahun 2006. Penyusutan lahan cenderung terjadi pada wilayah yang berbatasan dengan Kota Bandung, yang diperkirakan sebagai bagian dari pengembangan wilayah kota kedaerah sekitarnya dan hasil menunjukkan wilayah yang mengalami penyusutan terbesar merupakan kecamatan dengan luas wilayah relatif kecil dibandingkan dengan wilayah kecamatan lainnya seperti Cipatat (74%) dan Batujajar (83%). Meski demikian, selama periode tahun 1989 – 2019, beberapa kecamatan menunjukkan peningkatan luas lahan bervegetasi seperti Kecamatan Bojongsoang, Slawi, dan Tanjungsari. Kata kunci: Citra Satelit, Landsat, Penyusutan Lahan. Abstract The Citarum watershed undergoes a significant change in environmental structure, which results in a decrease in environmental quality, so a detailed description of the conditions of land change in vegetation in the Citarum watershed is needed. The main objective of this study: the process of changing the area of vegetation in the Citarum watershed, Bandung, West Java. This study uses remote sensing data with a quantitative approach. Mapping of land cover changes in the Citarum watershed uses multitemporal Landsat imagery with QGIS software. Several steps were carried out, including pre-processing, image processing, NDVI, and land cover change analysis. The study results show a decrease in the area of vegetation area of 35% in 1989 - 2019, with an average shrinkage of the land area of 0.64% annually and the most extensive shrinkage in 2006. Land shrinkage tends to occur in areas bordering Bandung City, which is estimated as part of the city's development to the surrounding area. The most extensive shrinkage areas are the districts with relatively small areas compared to other sub-districts such as Cipatat (74%) and Batujajar (83%). However, during the period 1989 - 2019, several sub-districts showed an increase in vegetated land areas, such as Bojongsoang, Slawi, and Tanjungsari Districts. 

scholarly journals Using Geospatial Analysis to Map Forest Change in New Hampshire: 1996–Present

2020 ◽  
Vol 118 (6) ◽  
pp. 598-612
Author(s):  
Heather Grybas ◽  
Russell G Congalton ◽  
Andrew F Howard
Keyword(s):  
Land Cover ◽  
New Hampshire ◽  
Forest Cover ◽  
Landsat Imagery ◽  
The State ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Land Cover Map ◽  
Forest Use

Abstract New Hampshire’s forests are vitally important to the state’s economy; however, there are indications that the state is experiencing a continuous loss in forest cover. We sought to investigate forest cover trends in New Hampshire. A baseline trend in forest cover between 1996 and 2010 was established using National Oceanic and Atmospheric Administration Coastal Change Analysis Program land cover data. A land cover map was then generated from Landsat imagery to extend the baseline trend to 2018. Results show that the state has experienced a continual decline in forest cover with the annual net loss steadily increasing from 0.14% between 1996 and 2001 to 0.27% between 2010 and 2018. Additionally, the more urbanized counties in southern New Hampshire are experiencing some of the greatest rates of net forest loss, most likely because of urbanization and agricultural expansion. This study demonstrated an effective methodology for tracking forest cover change and will hopefully inform future forest use policies.

scholarly journals Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

2019 ◽  
Vol 11 (9) ◽  
pp. 1056 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Xidong Chen ◽  
Shuai Xie ◽  
Yuan Gao
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Earth Science ◽  
Landsat Imagery ◽  
European Space Agency ◽  
Accurate Method ◽  
Land Cover Mapping ◽  
Land Cover Types ◽  
Multi Temporal ◽  
Land Cover Map

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

Land cover classification from satellite imagery, and its applications in cellular network planning

2005 ◽  
Author(s):  
◽  
Heng Huang
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Cellular Network ◽  
Landsat Imagery ◽  
Land Cover Classification ◽  
Landsat Data ◽  
Change Analysis ◽  
Accuracy Improvement ◽  
Processing Techniques

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] To further increase the classification accuracies, radar image processing techniques were investigated to preprocess the Radarsat data before classification. Eight processing techniques were applied to Radarsat data at various windows from 3 x 3 to 25 x 25 pixels. For a single radar feature, the Entropy processing at window size 13 x 13 provides the best overall land cover classification accuracy improvement when fused with the Landsat imagery. For multiple radar features, a higher accuracy improvement was found when combining the features (i.e., 13 x 13 Entropy, 9 x 9 data range, 19 x 19 mean) with the Landsat data. This study introduces an approach of fusing Landsat data with multiple Radarsat features to the land cover classification practice. Post-classification techniques were studied for land cover classification maps. Several weighted kernels were developed for the majority filtering process. The method evaluates the correlation between neighbor pixels according to the distance and further improves the classification accuracy. For the St. Louis study area, the Gaussian weighted kernel increases the overall land classification accuracy compared to the Landsat images. Post-classification smoothing of the sensor fusion result (Landsat and radar feature combination) further increases the accuracy. A decadal change analysis was also conducted for the St. Louis, Missouri area using Landsat imagery and census population data. This study proposes a methodology to integrate remotely sensed and census data in urban change analysis. The assessment can provide information that can highlight priority urban growth regions. The analysis shows strong correlation between population and land cover changes, which indicates the potential of satellite imagery to generate the physical feature input for tele-traffic forecasting of a cellular network.

scholarly journals Land Use and Land Cover Change Analysis Using GIS and Remote Sensing in The Case of Kersa District, Jimma Zone, Oromia Region, Ethiopia

2021 ◽  
Author(s):  
Nigus Tekleselassie Tsegaye
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Forest Cover ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Natural Forest ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based

Abstract Background: Land use and land cover change is driven by human actions and also drives changes that limit availability of products and services for human and livestock, and it can undermine environmental health as well. Therefore, this study was aimed at understanding land use and land cover change in Kersa district over the last 30 years. Time-series satellite images that included Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, which covered the time frame between 1990-2020, were used to determine the change in land use and land cover using object based classification.Results: The object based classification result revealed that in 1990 TM Landsat imagery, natural forest (16.07%), agroforestry (9.21%), village (12.03%), urban (1.93%), and agriculture (60.76%) were identified. The change result showed a rapid reduction in natural forest cover of 25.04%, 9.15%, and 23.11% occurred between (1990-2000), (2000-2010), and (2010-2020) study periods, respectively. Similarly agroforestry decreased by 0.88% and 63.9% (2000-2010) and (2010-2020), respectively. The finding indicates the increment of agricultural land, village, and urban, while the natural forest and agroforestry cover shows a declining trend.Conclusions: The finding implies that there was a rapid land use and land cover change in the study area. This resulted in loss of natural resource and biodiversity. Overall, proper and integrated approach in implementing policies and strategies related to land use and land cover management should be required in kersa district.

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