New land-cover maps of Ghana for 2015 using Landsat 8 and three popular classifiers for biodiversity assessment

2017 ◽  
Vol 38 (14) ◽  
pp. 4008-4021 ◽  
Author(s):  
Kwame Oppong Hackman ◽  
Peng Gong ◽  
Jie Wang
Keyword(s):  
Land Cover ◽  
Landsat 8 ◽  
Biodiversity Assessment ◽  
Land Cover Maps

scholarly journals Land Cover Change Investigation in the Southern Syrian Coastal Basins During the Past 30-Years Using Landsat Remote Sensing Data

2018 ◽  
Vol 11 (1-2) ◽  
pp. 45-51 ◽  
Author(s):  
Muhannad Hammad ◽  
László Mucsi ◽  
Boudewijn van Leeuwen
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Coastal Basins ◽  
Major Increase ◽  
Temporal And Spatial ◽  
Land Cover Maps ◽  
Conflict Period

Abstract Land cover change and deforestation are important global ecosystem hazards. As for Syria, the current conflict and the subsequent absence of the forest preservation are main reasons for land cover change. This study aims to investigate the temporal and spatial aspects and trends of the land cover alterations in the southern Syrian coastal basins. In this study, land cover maps were made from surface reflectance images of Landsat-5(TM), Landsat-7(ETM+) and Landsat-8(OLI) during May (period of maximum vegetation cover) in 1987, 2002 and 2017. The images were classified into four different thematic classes using the maximum likelihood supervised classification method. The classification results were validated using 160 validation points in 2017, where overall accuracy was 83.75%. Spatial analysis was applied to investigate the land cover change during the period of 30 years for each basin and the whole study area. The results show 262.40 km2 reduction of forest and natural vegetation area during (1987-2017) period, and 72.5% of this reduction occurred during (2002-2017) period due to over-cutting of forest trees as a source of heating by local people, especially during the conflict period. This reduction was particularly high in the Alabrash and Hseen basins with 76.13 and 79.49 km2 respectively, and was accompanied by major increase of agriculture lands area which is attributed to dam construction in these basins which allowed people to cultivate rural lands for subsistence or to enhance their economic situation. The results of this study must draw the relevant authorities’ attention to preserve the remaining forest area.

scholarly journals SCS+C Topographic Correction to Enhance SVM Classification Accuracy

2020 ◽  
Vol 2 ◽  
pp. 32-37
Author(s):  
Jwan AL-Doski ◽  
Shattri B. Mansor ◽  
H'ng Paik San ◽  
Zailani Khuzaimah
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Main Step ◽  
Support Vector ◽  
Landsat 8 ◽  
Mountainous Terrain ◽  
Topographic Correction ◽  
Corrected Image ◽  
Svm Algorithm ◽  
Land Cover Maps

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4% which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification90.1%accuracy compared to the uncorrected image86.2%for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification

scholarly journals INTRODUCING AUTOMATIC SATELLITE IMAGE PROCESSING INTO LAND COVER MAPPING BY PHOTO-INTERPRETATION OF AIRBORNE DATA

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 29-34
Author(s):  
H. Costa ◽  
P. Benevides ◽  
F. Marcelino ◽  
M. Caetano
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Use Changes ◽  
Satellite Image ◽  
Landsat 8 ◽  
Photo Interpretation ◽  
Time Production ◽  
Mainland Portugal ◽  
Land Cover Maps

Abstract. A series of five land cover maps, widely known as COS (Carta de Uso e Ocupação do Solo), have been produced since 1990 for mainland Portugal. Previous to 2015, all maps were produced through photo-interpretation of orthophotos. Land cover and land use changes were detected through comparison of previous and recent orthophotos, which were used for map updating, thereby producing a new map. The remaining areas of no change were preserved across the maps for consistency. Despite the value of the maps produced, the method is very time-consuming and limited to the single-date reference of the orthophotos. From 2015 onwards, a new approach was adopted for map production. Photo-interpretation of orthophoto maps is still the basis of mapping, but assisted by products derived from satellite data. The goals are three-fold: (i) cut time production, (ii) increase map accuracy, and (iii) further detail the nomenclature. The last map published (COS 2015) benefited from change detection and classification analyses of Landsat data, namely for guiding the photo-interpretation in forest, shrublands, and mapping annual agriculture. Time production and map error have been reduced comparing to previous maps. The new 2018 map, currently in production, further explores this approach. Landsat 8 time series of 2015–2018 are used for change detection in vegetation based on NDVI differencing, thresholding and clustering. Sentinel-2 time series of 2017–2018 are used to classify Autumn/Winter crops and Spring/Summer crops based on NDVI temporal profiles and classification rules. Benefits and pitfalls of the new mapping approach are presented and discussed.

scholarly journals The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

2019 ◽  
Vol 11 (15) ◽  
pp. 4035 ◽  
Author(s):  
Kanat Samarkhanov ◽  
Jilili Abuduwaili ◽  
Alim Samat ◽  
Gulnura Issanova
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Aral Sea ◽  
Landsat 8 ◽  
Spatial And Temporal Patterns ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Cover Maps

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.

scholarly journals Developing Multi-Source Indices to Discriminate between Native Tropical Forests, Oil Palm and Rubber Plantations in Indonesia

2021 ◽  
Vol 14 (1) ◽  
pp. 3
Author(s):  
Inggit Lolita Sari ◽  
Christopher J. Weston ◽  
Glenn J. Newnham ◽  
Liubov Volkova
Keyword(s):  
Land Cover ◽  
Oil Palm ◽  
Forest Monitoring ◽  
Rubber Plantation ◽  
Native Forest ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Rubber Plantations ◽  
Radar Images ◽  
Land Cover Maps

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer’s accuracy for all classes was higher than 90%, except for rubber (65%), and user’s accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia’s national forest monitoring system and inform monitoring of plantation expansion.

scholarly journals Tingkat Kerawanan Kebakaran Hutan Dan Lahan Di Banjarbaru Provinsi Kalimantan Selatan

2021 ◽  
Vol 3 (2) ◽  
pp. 57-64
Author(s):  
Muhammad Riza Saputra ◽  
Deasy Arisanty ◽  
Sidharta Adyatma
Keyword(s):  
Land Cover ◽  
High Density ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
High Level ◽  
Land Cover Maps

One of the areas in South Kalimantan that is prone to land fires is the Banjarbaru area, especially on peatlands. The fire in Banjarbaru is important because of the vital object of Syamsudin Noor Airport. Mapping of fire vulnerability was important for the Banjarbaru area, which had repeated fires throughout the year. The objective of the study was to analyze the vulnerability of forest and land fires in Banjarbaru, South Kalimantan Province. This study used Landsat 8 Oli Tirs imagery to obtain NDVI data and land cover maps from INA-Geoportal. The analysis of data used the scoring and overlay of the two maps. The level of vulnerability was dominated by the high vulnerability. The high level of vulnerability in Cempaka District was 81.9 %, in Banjarbaru Selatan District was around 99.5 %, in Banjarbaru Utara District was around 95.3 %, in Landasan Ulin District was around 94.1 % and in Lianganggang District was around 88.9 %. Land cover in the form of agriculture, plantations, and shrubs with moderate-high density caused the land to be more prone to fires.

scholarly journals Trends of land cover change in a key biological corridor in Central Nepal

2017 ◽  
Vol 1 (2) ◽  
pp. 64-69
Author(s):  
KRIPA NEUPANE ◽  
AMBIKA P. GAUTAM ◽  
ARUN REGMI
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Forest Cover ◽  
Likelihood Method ◽  
Primary Data ◽  
Landsat 8 ◽  
Central Nepal ◽  
Biological Corridor ◽  
Conservation Awareness ◽  
Land Cover Maps

Neupane K, Gautam AP, Regmi A. 2017. Trends of land cover change in a key biological corridor in Central Nepal. Asian J For 1: 50-55. The study analyzed the changes in land cover in one of the key biological corridors in Central Nepal called the Barandabhar Corridor located in Chitwan District, during the last two decades (i.e. 1991 to 2013). The study is based on analysis of satellite imageries (Landsat 5 TM of 1991 and Landsat 8 OLI_TIRS of 2013) and primary data on drivers of land cover change, collected from the field. Supervised Maximum Likelihood method of image classification was used to produce the land cover maps for 1991 and 2013. The result showed that forest cover in the corridor increased by 7.03% while the coverage of shrubland, water and other land cover types decreased during the study period. Implementation of community based forest management programs, low dependency on forest resources, and increase in conservation awareness among the local people are found to be the main causes behind the increase in forest cover.

scholarly journals Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

2019 ◽  
Vol 11 (13) ◽  
pp. 1581 ◽  
Author(s):  
Uddin ◽  
Matin ◽  
Meyer
Keyword(s):  
Land Cover ◽  
Flood Frequency ◽  
Landsat 8 ◽  
Flood Inundation ◽  
Inundation Maps ◽  
Flood Inundation Mapping ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Hazard Response ◽  
Land Cover Maps

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

scholarly journals Assessment of land use change in the Thuma forest reserve region of Malawi, Africa

2021 ◽  
Author(s):  
Mihla Phiri ◽  
Harrington Nyirenda
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Sustainable Management ◽  
Landsat 8 ◽  
Forest Reserve ◽  
Future Studies ◽  
Open Forest ◽  
Closed Forest ◽  
Land Cover Maps

Abstract A study was conducted in Thuma area in central Malawi to quantify contemporary land cover and to explore the degree of land use change in the Thuma forest reserve area of Malawi by analysing and comparing satellite-derived land cover maps from 1997, 2007 and 2017. The study was carried out using Remote Sensing and Geographic Information System (GIS), focusing on analysis of Landsat 5 ETM and Landsat 8 ORI/TIRS satellite images. The classification was conducted for the following distinct classes; closed forest, open forest, shrubland, savanna grassland, agriculture fields, and water. The analysis revealed that closed forest diminished from 19% in 1997 to 10% in 2007 to 6% in 2017. Open forest reduced from 30% to 21% from 1997 to 2007 but increased to 22% in 2017. Agriculture area almost doubled from 37 % in 1997 to 64 % in 2017. Actual area from 1997 to 2017, shows that closed forest has reduced from 7,000 ha to 3,000 ha while open forest from 12,900 ha to 7800 ha. Savanna grassland has doubled from 5,900 ha to 13,000 ha. However, future studies should use modern satellites such as Sentinel and Landsat 9 for improved quantification of changes. The findings show that even the protected forest reserve (previously dominated by closed forest) is not fully protected from deforestation by local communities. Government and other stakeholders should devise measures to meet the needs of the surrounding communities and the ecological/biophysical needs of the reserves. Based on this study, issues of re-demarcation of the forest reserve and accessed area should also be explored. This study serves as a reference for the management of Thuma Forest Reserve as a refuge for natural tree species, rivers that harbour endemic fish species (Opsaridium microlepis and Opsaridium microcephalis) and the sustainable management of endangered elephants in the reserve.

Sign in / Sign up

Export Citation Format

Share Document