One-Class Classification for Mapping a Specific Land-Cover Class: SVDD Classification of Fenland

This study investigated the pattern of land use and land cover of forest reserve in Akure, Ondo State, Nigeria. Currently, deforestation constitutes one of the global development challenges. The broad objective of this study is to identify land use and land cover class within the study area using satellite imagery (ies) to determine the rate/trend of change of this Forest Reserve from 1988 to 2018. The research method includes the use of Geographical Positioning System, and processing of field data through GIS and Remote sensing tool (ILWIS). The research was able to identify various land use and land cover within the Akure forest reserve with the help of GIS and remote sensing tools, the boundary of Akure forest reserve and its environs was delineated, and further result of the classification of Landsat shows that as at 2018 the forest reserve is covered with majorly light vegetation with 51.79%. The study recommended that there Department of Forestry and Ministry of Physical Planning and Urban Development must ensure Policy that will encourage local people and institutional participation in forestry management and conservation along with safeguarding indigenous people’s traditional rights and tenure with rightful sharing of benefits.

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Perubahan Penutupan Lahan Kota Bogor

Talenta Conference Series Agricultural and Natural Resources (ANR) ◽

10.32734/anr.v2i1.572 ◽

2019 ◽

Vol 2 (1) ◽

pp. 44-51

Author(s):

Ina Lidiawati ◽

Ratna Sari Hasibuan ◽

Retno Wijayanti

Keyword(s):

Land Cover ◽

Rice Field ◽

Research Result ◽

Land Cover Changes ◽

Plantation Forest ◽

Geospatial Information ◽

Land Cover Map ◽

Cover Class ◽

The City

Pembangunan yang terjadi sangat pesat sehingga tutupan lahan di Kota Bogor berubah. Penelitian ini bertujuan untuk mengetahui tutupan lahan Kota Bogor yang berubah yaitu tahun 1996, 2006, 2016 dan faktor-faktor yang mempengaruhi tutupan lahan Kota Bogor yang berubah tersebut. Perubahan tutupan lahan Kota Bogor dianalisis menggunakan perangkat lunak Arc.GIS 10.2. Data yang digunakan sebagai bahan analisis adalah peta tutupan lahan Kota Bogor 1996, 2006 dan 2016 dari Kementerian Lingkungan Hidup dan Kehutanan (KLHK) dan peta Rupa Bumi Indonesia (RBI). Hasil dari penelitian ini adalah klasifikasi kelas tutupan lahan hutan tanaman kota Bogor, area terbuka, pelabuhan/bandara, pemukiman/lahan, pertanian kering, pertanian kering, semak, sawah, perkebunan, dan badan air. Pada tahun 1996 tutupan lahan didominasi oleh vegetasi, semak, dan semak-semak. Perubahan tutupan lahan yang paling masif terjadi pada kelas permukiman / tanah dengan luas 6.683 hektar pada tahun 2006 dan 7.532 ha pada tahun 2016. Diperkirakan bahwa luas lahan yang akan dibangun akan terus bertambah seiring dengan pertambahan populasi. Peningkatan populasi menyebabkan lebih banyak ruang untuk perumahan dan berbagai kegiatan, selain kondisi sosial ekonomi dan arah kebijakan pemerintah yang mempengaruhi tutupan lahan kota Bogor menjadi berubah. Development that occurred in the city of Bogor very rapidly causing land cover changes. This research purpose was to know the change of land cover of Bogor City in 1996, 2006, and 2016 and to know what factors influence the change of land cover. Changes in land cover in Bogor City were analyzed using Arc.GIS software 10.2. The data used as an analysis material were the land cover map of Bogor City 1996, 2006 and 2016 issued by the Ministry of Environment and Forestry and the map of Rupa Bumi Indonesia issued by the Geospatial Information Agency. This research result was the classification of a land cover class of Bogor city of plantation forest, open area, port/airport, settlement/land, dry farm, dry farm, shrub, rice field, plantation, and water body. In 1996 the land cover was dominated by vegetation, shrubs, and bushes. The most massive land cover change occurred in the class of settlements/land with an area of 6,683 hectares in 2006 and 7,532 ha in the year 2016. It is estimated that the area of land will be built will continue to grow as the population increases. The increase in population causes more space for housing and various activities, besides the socio-economic condition and the direction of government policy also affect the change of land cover in Bogor city.

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THE USE OF ONLINE WEB-BASED SOFTWARE I-TREE CANOPY AS AN ALTERNATIVE IN SUPPORTING COMMUNITY BASED ZERO DELTA Q PLANNING

Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana ◽

10.29122/alami.v2i2.3013 ◽

2018 ◽

Vol 2 (2) ◽

pp. 120

Author(s):

Akhmadi Puguh Raharjo

Keyword(s):

Land Cover ◽

Planning Process ◽

River System ◽

Tree Canopy ◽

Land Cover Class ◽

Community Based ◽

Web Based ◽

The Difference ◽

Cover Class ◽

Online Web

Zero Delta Q is a policy to ensure that any additional surface runoff due to development does not further burden the drainage or river system. In case of Zero Delta Q application planning at the community level, a software is needed that can help classify and quantify the existing land cover class in area where the community is located. The purpose of this study is to look at the time needed and reliability of the i-Tree Canopy web-based software online in classifying and quantifying land cover classes on one of the sub-catchments in the Pesanggrahan River Basin. The land cover class is divided into six: trees, grasses / undergrowth plants, open area, water bodies, pavement / road and roof of the building. For comparison, an RBI map is used from the same area to see the extent of each class of land cover. Observation of each point requires an average time of 5.2 ± 1.0 seconds. The difference between direct sub-basin measurements using i-Tree Canopy and detailed analysis results from the RBI map is within the range of 0.41% or 0.36 Ha for each individual class of land cover. For a relatively small study area (under 100 ha) and when supported with reliable internet access, this web-based online software is sufficiently reliable in assisting the application planning process to support Zero Delta Q policy.

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Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability

Land ◽

10.3390/land10010035 ◽

2021 ◽

Vol 10 (1) ◽

pp. 35

Author(s):

Dingfan Xing ◽

Stephen V. Stehman ◽

Giles M. Foody ◽

Bruce W. Pengra

Keyword(s):

Land Cover ◽

Latent Class ◽

Reference Data ◽

Land Cover Class ◽

Reference Class ◽

Study Region ◽

Latent Class Modeling ◽

Class Labels ◽

Cover Class ◽

Percent Area

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.

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