scholarly journals Identifikasi dan Pencegahan Daerah Rawan Bencana Kebakaran Hutan dan Lahan Gambut Di Kalimantan Barat

2021 ◽  
Vol 20 (1) ◽  
pp. 115-126
Author(s):  
Tamas Faiz Dicelebica ◽  
Aji Ali Akbar ◽  
Dian Rahayu Jati
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Disaster Vulnerability ◽  
Fire Disaster ◽  
Forested Areas ◽  
Groundwater Basin ◽  
Groundwater Basins ◽  
Overlap Method ◽  
Land Cover Maps ◽  
Basin Area

Kalimantan Barat memiliki potensi bencana kebakaran hutan dan lahan gambut yang tinggi karena banyaknya titik api dan jenis lahan gambut yang mudah terbakar pada musim kemarau. Tujuan dari penelitian ini adalah untuk memetakan dan menentukan kecenderungan titik pamas dan mengidentifikasi dan mencegah kawasan rawan kebakaran hutan dan lahan gambut dengan data hotspot, peta curah hujan, peta tutupan lahan, peta kesatuan hidrologis gambut, dan peta cekungan air tanah menggunakan Sistem Informasi Geografis atau SIG. Metode overlap digunakan untuk menganalisis kecenderungan titik panas sedangkan Overlay dan Scoring digunakan untuk mengidentifikasi kawasan rawan kebakaran hutan dan lahan. Setelah dilakukan analisis titik panas, terdapat kecenderungan curah hujan pada kelas curah hujan 1.500-3.000 mm/tahun dengan 2.192 kejadian. Perubahan tutupan lahan di kawasan hutan mengalami penurunan sebesar 7,96%. Peningkatan tutupan lahan di kawasan non-hutan sebesar 11,26%, mempengaruhi potensi dan kecenderungan titik api dan bencana kebakaran hutan dan lahan. Kubu Raya memiliki tingkat kerawanan bencana kebakaran pada kelas sangat rawan dengan luasan 0,26%, dan Kapuas Hulu memiliki tingkat kerawanan bencana kebakaran pada kelas tidak rawan dengan luas 0,19%. Kabupaten Ketapang merupakan daerah dengan tingkat pencegahan tertinggi, dengan luas cekungan airtanah sebesar 26,46%.ABSTRACTWest Kalimantan has a high potential for forest and peatland fire disasters due to the high number of hotspots and the type of peatland which burns easily during the dry season. The purpose of this research is to map and determine the trend of hotspots and areas prone to forest and peatland fires and prevent them with hotspot data, rainfall maps, land cover maps, maps of peat hydrological units, and maps of groundwater basins using Geographic Information Systems or GIS. The overlap method is used to analyze the trend of hotspots; meanwhile, Overlay and Scoring are used to identify areas prone to forest and land fires in this research. After analyzing the hotspots, there is a tendency for rainfall with a class of 1,500-3,000mm/year with 2,192 events. Land cover change in forested areas decreased by 7.96%. It increased land cover in non-forest areas by 11.26%, affecting the potential and tendency of hotspots and forest and land fire disasters. Kubu Raya has a fire disaster vulnerability level in the very vulnerable class with an area of 0.26%, and Kapuas Hulu has a fire disaster vulnerability level in the non-prone class with an area of 0.19%. Ketapang Regency is the area with the highest prevention rate, with a groundwater basin area of 26.46%.

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 Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing

2020 ◽  
Vol 12 (18) ◽  
pp. 2883
Author(s):  
Theodomir Mugiraneza ◽  
Andrea Nascetti ◽  
Yifang Ban
Keyword(s):  
Growth Rate ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Land ◽  
Google Earth ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
Urban Land Cover ◽  
Urban Land Cover Change ◽  
Land Cover Maps

Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

scholarly journals Climate and Land Cover Trends Affecting Freshwater Inputs to a Fjord in Northwestern Patagonia

2021 ◽  
Vol 8 ◽  
Author(s):  
Jorge León-Muñoz ◽  
Rodrigo Aguayo ◽  
Rafael Marcé ◽  
Núria Catalán ◽  
Stefan Woelfl ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Burned Area ◽  
Freshwater Input ◽  
Dissolved Carbon ◽  
Dissolved Iron ◽  
Cross Wavelet Transform ◽  
Freshwater Inputs ◽  
Land Cover Maps ◽  
Basin Area

Freshwater inputs strongly influence oceanographic conditions in coastal systems of northwestern Patagonia (41–45°S). Nevertheless, the influence of freshwater on these systems has weakened in recent decades due to a marked decrease in precipitation. Here we evaluate potential influences of climate and land cover trends on the Puelo River (640 m3s–1), the main source of freshwater input of the Reloncaví Fjord (41.5°S). Water quality was analyzed along the Puelo River basin (six sampling points) and at the discharge site in the Reloncaví Fjord (1, 8, and 25 m depth), through six field campaigns carried out under contrasting streamflow scenarios. We also used several indicators of hydrological alteration, and cross-wavelet transform and coherence analyses to evaluate the association between the Puelo River streamflow and precipitation (1950–2019). Lastly, using the WEAP hydrological model, land cover maps (2001–2016) and burned area reconstructions (1985–2019), we simulated future land cover impacts (2030) on the hydrological processes of the Puelo River. Total Nitrogen and total phosphorus, dissolved carbon, and dissolved iron concentrations measured in the river were 3–15 times lower than those in the fjord. Multivariate analyses showed that streamflow drives the carbon composition in the river. High streamflow conditions contribute with humic and colored materials, while low streamflow conditions corresponded to higher arrival of protein-like materials from the basin. The Puelo River streamflow showed significant trends in magnitude (lower streamflow in summer and autumn), duration (minimum annual streamflow), timing (more floods in spring), and frequency (fewer prolonged floods). The land cover change (LCC) analysis indicated that more than 90% of the basin area maintained its land cover, and that the main changes were attributed to recent large wildfires. Considering these land cover trends, the hydrological simulations project a slight increase in the Puelo River streamflow mainly due to a decrease in evapotranspiration. According to previous simulations, these projections present a direction opposite to the trends forced by climate change. The combined effect of reduction in freshwater input to fiords and potential decline in water quality highlights the need for more robust data and robust analysis of the influence of climate and LCC on this river-fjord complex of northwestern Patagonia.

scholarly journals Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps

2017 ◽  
Author(s):  
Wei Li ◽  
Natasha MacBean ◽  
Philippe Ciais ◽  
Pierre Defourny ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Climate Models ◽  
Plant Functional Types ◽  
Forest Loss ◽  
Land Cover Changes ◽  
Cropland Area ◽  
Functional Types ◽  
Land Cover Maps

Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes at global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas, gross and net changes of different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) and Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992–2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes of forest, cropland and grassland PFTs in ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA while after 2007 in HYDE3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long time-series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps – v2.0.7; see details in Section 2.5).

scholarly journals Land-cover change threatens tropical forests and biodiversity in the Littoral Region, Cameroon

Oryx ◽  
2019 ◽  
Vol 54 (6) ◽  
pp. 882-891 ◽  
Author(s):  
Mahmoud I. Mahmoud ◽  
Mason J. Campbell ◽  
Sean Sloan ◽  
Mohammed Alamgir ◽  
William F. Laurance
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Urban Areas ◽  
Landsat Imagery ◽  
Biodiversity Loss ◽  
Littoral Region ◽  
Equatorial Africa ◽  
Forest Regions ◽  
Forested Areas ◽  
Major Factors

AbstractTropical forest regions in equatorial Africa are threatened with degradation, deforestation and biodiversity loss as a result of land-cover change. We investigated historical land-cover dynamics in unprotected forested areas of the Littoral Region in south-western Cameroon during 1975–2017, to detect changes that may influence this important biodiversity and wildlife area. Processed Landsat imagery was used to map and monitor changes in land use and land cover. From 1975 to 2017 the area of high-value forest landscapes decreased by c. 420,000 ha, and increasing forest fragmentation caused a decline of c. 12% in the largest patch index. Conversely, disturbed vegetation, cleared areas and urban areas all expanded in extent, by 32% (c. 400,000 ha), 5.6% (c. 26,800 ha) and 6.6% (c. 78,631 ha), respectively. The greatest increase was in the area converted to oil palm plantations (c. 26,893 ha), followed by logging and land clearing (c. 34,838 ha), all of which were the major factors driving deforestation in the study area. Our findings highlight the increasing threats facing the wider Littoral Region, which includes Mount Nlonako and Ebo Forest, both of which are critical areas for regional conservation and the latter a proposed National Park and the only sizable area of intact forest in the region. Intact forest in the Littoral Region, and in particular at Ebo, merits urgent protection.

scholarly journals Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

2021 ◽  
Vol 8 (3) ◽  
pp. 2731-2741
Author(s):  
Gatot Nugroho ◽  
Galdita Aruba Chulafak ◽  
Fajar Yulianto
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Remote Sensing Data ◽  
Vector Analysis ◽  
Tropical Savanna ◽  
Change Vector Analysis ◽  
Change Vector ◽  
Basin Area

In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation. This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year. From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area. From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.

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 Assessment of Land Cover Change and Its Impact on Changes in Soil Erosion Risk in Nepal

2018 ◽  
Vol 10 (12) ◽  
pp. 4715 ◽  
Author(s):  
Kabir Uddin ◽  
Mir Abdul Matin ◽  
Sajana Maharjan
Keyword(s):  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Soil Conservation ◽  
Forest Cover ◽  
Human Life ◽  
Landsat Images ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Land Cover Maps

Land cover change is a critical driver for enhancing the soil erosion risk in Nepal. Loss of the topsoil has a direct and indirect effect on human life and livelihoods. The present study provides an assessment of the decadal land use and land cover (LULC) change and consequent changes in the distribution of soil erosion risk for the years, 1990, 2000, and 2010, for the entire country of Nepal. The study attempted to understand how different land cover types change over the three decades and how it has changed the distribution of soil erosion risks in Nepal that would help in the development of soil conservation priority. The land cover maps were produced using geographic object-based image analysis (GEOBIA) using Landsat images. Soil erosion patterns were assessed using the revised universal soil loss equation (RUSLE) with the land cover as the input. The study shows that the forest cover is the most dominant land cover in Nepal that comprises about 6,200,000 ha forest cover. The estimated annual erosion was 129.30 million tons in 1990 and 110.53 million tons in 2010. The assessment of soil erosion dynamics was presented at the national, provincial, and district level. District wise analysis revealed that Gulmi, Parbat, Syangja, and the Tanahu district require priority for soil conservation.

scholarly journals Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

2018 ◽  
Vol 10 (1) ◽  
pp. 219-234 ◽  
Author(s):  
Wei Li ◽  
Natasha MacBean ◽  
Philippe Ciais ◽  
Pierre Defourny ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Climate Models ◽  
Plant Functional Types ◽  
Forest Loss ◽  
Land Cover Changes ◽  
Cropland Area ◽  
Functional Types ◽  
Land Cover Maps

Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992–2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps – v2.0.7; see details in Sect. 5). The PFT map translation protocol and an example in 2000 can be downloaded from https://doi.org/10.5281/zenodo.834229. The annual ESA CCI PFT maps from 1992 to 2015 at 0.5∘×0.5∘ resolution can also be downloaded from https://doi.org/10.5281/zenodo.1048163.

scholarly journals Assessing land cover change in Namibia's Kavango East region: a multi-date object approach

2020 ◽  
Vol 344 ◽  
pp. 17-32
Author(s):  
Edward MUHOKO ◽  
Carlos De WASSEIGE ◽  
Vera DE CAUWER
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Forest Cover ◽  
The Novel ◽  
East Region ◽  
Novel Approach ◽  
Object Based ◽  
Global Issue ◽  
Dry Tropical Forests ◽  
Land Cover Maps

Land cover change is a global issue but its effects can be particularly severe in developing countries such as Namibia, by affecting the ecological functions of ecosystems and hence the sustainability of its development. Namibia’s arid conditions, due to low rainfall and high evapotranspiration rates, coupled with annual savannah fires, have resulted in a heterogenous landscape composed of a mixture of trees, shrubs and herbaceous plants. As a result, land cover maps are often inaccurate at the pixel level. Despite their relatively high accuracy, object-based image analyses are yet to be exhaustively applied to the dry tropical forests of Southern Africa.  The purpose of this study was to apply a multi-date object-based approach to land cover change, in order to determine its extent and dynamics in the heterogenous landscape of Kavango East, one of the regions with the highest forest cover in Namibia. Multi-date segmentation, mean band values and image differentiation were used to detect land cover changes in four periods (1990, 2000, 2009 and 2016). The most common land conversion for all the periods was from forest to cropland. In 1990, forests covered 58% of the land but by 2016, this had dropped to 55%. Meanwhile, cropland covered 3% of the study area in 1990 and had doubled to 6% by 2016. The novel approach used in this study has produced promising results compared to traditional methods, which are prone to errors in detecting post-classification changes. The method can therefore be recommended for long term monitoring of land cover and land use change in areas with similar environmental and biophysical conditions.

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