ANALISA KESESUAIAN UNSUR PENYAJIAN PETA KELURAHAN PANYURAN BERDASARKAN PERKA BIG NO. 3 TAHUN 2016

Availability and understanding about the importance of spatial data, especially Village Maps, forrural communities are still minimum. Ngargosari Village, a village in Samigaluh District, KulonProgo Regency, was almost never used spatial data or maps to support the development.Whereas, UU 6 of 2014 concerning Villages states that Village Maps are the basis of informationand support systems in policy making. Village Map is a basic thematic map that containselements and information such as regional boundaries, roads/ infrastructure, topography,waters, facilities, and land use, which were presented in image maps, maps of facilities andinfrastructure, as well as land cover maps and land use (Perka BIG No. 3 in 2016 concerningTechnical Specifications for Presentation of Village Maps). Therefore, this community serviceaims to emphasize an understanding of the importance of Geospatial Information. Thecommunity is involved in making Village Maps through participatory mapping. The methods thatwere used are a remote sensing approach, field survey, and Focus Group Discussion (FGD)involving the Village Head, Village Officials, Hamlet Heads and Community Leaders. The resultsof this service are in the form of a map of the image of the Ngargosari Village which containsinformations about the boundaries of the village administration and hamlets, and also regionalfacilities and infrastructure.

The objective of this research is to analyze the current status and process of Vietnam's rural development to draw lessons for improving the quality of rural life and achieving sustainable rural development in Vietnam. The research was carried out by the following methods. First, the paper reviews previous studies on concepts and theoretical perspectives on rural development and comes up with a concrete definition of the term ‘rural development’ for this research to build a Rural Development Index (RDI). Second, the RDI is developed as a tool to evaluate the current status and process of rural development. Third, the paper examines the current status and development process of Vietnam's rural development using the RDI. The scope of this study covered 63 regions (58 provinces and 5 municipalities) in Vietnam. This research can be used to establish mid- and long-term visions and strategies of rural development policies in Vietnam.

In recent years, land use and land cover plays a pivotal role in global environmental change. Under these circumstances,the need of a new dimension for detecting land use and cover is getting more imperative for conservation and effectivemanagement of land use and cover types. Importantly, the use of information technology to support decision making indetecting land use and cover is essential and recent. One of the technologies used is Airborne Remote Sensing. Theobjective of this study is to identify, quantify, classify and map land use and land cover mapping in Setiu, Terengganuusing UPM-APSB’s AISA airborne hyperspectral remote sensing. Detection of land use and cover was performed usingairborne hyperspectral imaging data taken on 20 April 2006 with the support of existing land use and cover maps. Thesize of the study area is 100 ha. The image was displayed in ENVI 4.0 Software using bands 202217 (RGB)combination. The data were then enhanced and classified for different land use and cover classes. From the dataanalysis, the image can be classified into eight classes. The classes are 2-3 years old oil palm plantation, 4-5 years oldoil palm plantation, young (3-4 years old) rubber plantation, matured (15-17 years old) rubber plantation, vegetationcrops, open area, road and river. The land use and land cover classes area distribution of the plots under study in Setiu,Terengganu were 4.18 ha, 8.58 ha, 6.26 ha, 70.43 ha, 2.98 ha, 2.31 ha, 2.78 ha, and 2.48 ha. Overall, the classificationaccuracy of interpretation of the airborne imagery for land use and cover in Setiu, Terengganu is 89.51 and kappacoefficient is 0.86. This study shows that, airborne hyperspectral remote sensing technique is capable in identifying,quantifying, classifying and mapping land use and cover in Setiu, Terengganu, hence a good decision support tool inland use and cover planning and management.

Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.

Information on land cover and its changes is essential for rational planning of agricultural land use, sustainable management of agricultural land, increase of crop production, and environmental protection. Yet, in most developing countries such information is non-existent or not reliable. This chapter describes how the Food and Agriculture Organization of the United Nations (FAO) assists developing countries with land cover mapping and establishment of land cover databases. FAO assistance to developing countries in the field of land cover mapping is based on a three-pronged approach: ● Development of standardized methodologies for land cover and land use classification and mapping appropriate for developing countries, ● Providing assistance with land cover and land use mapping and establishment of associated digital databases, ● Strengthening the institutional capacities for land cover and land use mapping and monitoring in developing countries.

Land cover and forest mapping supports decision makers in the course of making informed decisions for implementation of sustainable conservation and management plans of the forest resources and environmental monitoring. This research examines the value of integrating of ALOS PALSAR and Landsat data for improved forest and land cover mapping in Northern Tanzania. A separate and joint processing of surface reflectance, backscattering and derivatives (i.e., Normalized Different Vegetation Index (NDVI), Principal Component Analysis (PCA), Radar Forest Deforestation Index (RFDI), quotient bands, polarimetric features and Grey Level Co-Occurrence Matrix (GLCM) textures) were executed using Support Vector Machine (SVM) classifier. The classification accuracy was assessed using a confusion matrix, where Overall classification Accuracy (OA), Kappa Coefficient (KC), Producer’s Accuracy (PA), User’s Accuracy (UA) and F1 score index were computed. A two sample t-statistics was utilized to evaluate the influence of different data categories on the classification accuracy. Landsat surface reflectance and derivatives show an overall classification accuracy (OA = 86%). ALOS PALSAR backscattering could not differentiate the land cover classes efficiently (OA = 59%). However, combination of backscattering, and derivatives could differentiate the land cover classes properly (OA = 71%). The attained results suggest that integration of backscattering and derivative has potential of utilization for mapping of land cover in tropical environment. Integration of backscattering, surface reflectance and their derivative increase the accuracy (OA = 97%). Therefore it can be concluded that integration of ALOS PALSAR and optical data improve the accuracies of land cover and forest mapping and hence suitable for environmental monitoring.

Abstract. Land Cover (LC) maps are fundamental products for a wide variety of applications. The workflow for their production is composed of classification of satellite imagery and validation against a reference dataset. Different LC maps as well as multiple versions in time of the same LC map can be also compared with one another to assess LC changes. Since the current richness of both space and in-situ observations makes it quite easy to produce LC maps, it is fundamental to assess their accuracy before using them for real applications. This paper focuses on education and capacity building on the intercomparison and validation of global (i.e. covering the whole world) and high-resolution (i.e. with a spatial resolution of at least 30 m) LC maps. The availability of Free and Open Source for Geospatial (FOSS4G) technology capable to process LC maps, as well as the existence of ad hoc educational material, is carefully assessed. In parallel, an ad hoc survey has showed that users, especially in developing countries, often lack awareness about the need to validate them and are not aware about the existence of training material. With this premise, a new project presented in the paper aims to produce new, openly licensed and FOSS4G-based training material on the intercomparison and validation of global high-resolution LC maps.

Limitations of mapping land surface properties and their conversion into climate model boundary conditions are major sources of uncertainty in climate simulations. In this paper, the range of the largest possible uncertainty in satellite-derived land cover (LC) map is estimated and its impact on climate simulations is quantified with the Earth System Model of the Max-Planck Institute for Meteorology utilizing prescribed sea surface temperature and sea ice. Two types of uncertainty in the LC map are addressed: (i) uncertainty due to classification algorithm of spectral reflectance into LC classes, and (ii) uncertainty due to conversion of LC classes into the climate model vegetation distribution. For forest cover, each of them is about the same order of magnitude as the uncertainty range in recent observations (∼± 700 Mha). Superposing two sources of uncertainty results in LC maps that feature the range of vegetation deviation that is about the same order of magnitude as the recent (since year 1700) forest loss due to agriculture (forest cover uncertainty range ∼± 1700 Mha). These uncertainties in vegetation distribution lead to noticeable variations in near-surface climate variables, local, regional, and global climate forcing. Temperature does not show significant uncertainty in global mean, but rather exhibits regional deviations with an opposite response to LC uncertainty that compensate each other in the global mean (e.g., albedo feedback controls temperature in boreal North America resulting in cooling (warming) with decrease (increase) of vegetation while evaporative cooling controls temperature in South America and sub-Saharan Africa resulting in cooling (warming) with increase (decrease) of vegetation). Large-scale circulation is also affected by the LC uncertainty, and consequently precipitation pattern as well. It is demonstrated that precipitation uncertainty in the monsoonal regions are about the same order of magnitude as in previous studies with idealized perturbations of vegetation. These findings indicate that the range of uncertainty in satellite-derived vegetation maps for climate models is about the same order of magnitude as the uncertainty in recent observations of forest cover or as the forest lost due to agriculture. Consequently, climate simulations have a similar range of uncertainty in variables representing near-surface climate as the observed climate change due to land use. Hence, more accurate methods are needed for mapping and converting LC properties into model vegetation in order to increase reliability of climate model simulations.

This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping.

Precise global/regional land cover mapping is of fundamental importance in studies of land surface processes and modelling. Quantitative assessments of the map quality and classification accuracy for existing land cover maps will help to improve accuracy in future land cover mapping. We compare and evaluate four land cover datasets over China. The datasets include the Version 2 global land cover dataset of IGBP, MODIS land cover map 2001, a global land cover map produced by the University of Maryland, and the land cover map produced by the global land cover for the year 2000 (GLC 2000) project coordinated by the Global Vegetation Monitoring Unit of the European Commission Joint Research Centre. The four maps used different classification systems, which made the comparison difficult. So we first aggregated these maps by reclassifying them using a unified legend system. A large-scale, i.e. 1:100 000 land cover map of China was used as the reference data to validate the four maps. The results show that the GLC2000 land cover map represents the highest accuracy. However, it has obvious local labelling errors and a zero labelling accuracy for the wetland type. The MODIS land cover map ranks second for type area consistency and third for sub-fraction overall accuracy compared with reference data, which may be affected by the local labelling error. The IGBP land cover map has good labelling accuracy, although it has a local labelling error and third consistency for type area. The labelling accuracy and type area consistency for the reference data of UMd land cover map is low. We conclude that the accuracies of all the datasets cannot meet the requirements of land surface modelling. For the reference data, i.e. the 1:100 000 land cover map, the classification system needs to be transferred to a well recognized one that has been used commonly in land surface modelling. In addition, we propose an information fusion strategy to produce a more accurate land cover map of China whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

Problem Statement: Community-based management as incorporates both a top-down and bottom-up approach that involvement beneficiary sections such as local community, government states and non governmental organizations. It has also been applied to designate approaches where local communities play a central but not exclusive role in rural sustainable development process management. Approach: This study was survey method and is descriptive-correlation research, which was carried out to designee the pattern of community-based management and its application for sustainable rural development process in west Azarbaijan province. Study population were consisted 270 of, local community (rural councilors), offices experts in rural related office activities and agricultural and natural resources engineering organization NGO’s members. Results: Results of structural equation modeling of the accepted characteristics indicated that latent variable such as "Stakeholder’s Role" and "Affecting Factors" have positive effect and "Obstacles" latent variable has a negative role to design CBM. A structural equation indicated these variables altogether account 93% of variance (R2 = 0.93) in designing community-based management. Conclusion/Recommendations: On the basis of structural model, we can conclude that factors, stakholers and obstacles have important affect on community-based management. Overall community based management will have more impotent role in rural developments process planning, organizing, staffing, controlling and directing.

Landscape is heterogeneous part of the Earth surface, forming mosaic of various habitats organized at different scales and levels (Johnson et al. 1992). The landscape pattern has important impact on ecological processes; hence its analysis through quantitative measures is essential for environmental studies. There are many indicators characterizing spatial structure of landscape at different level of detail; they enable analysis of landscape fragmentation at patch level, through studies at habitat level up to complex analyses at landscape level. Seven indicators, which are related to various levels of detail, were selected at the presented work. The following indicators have been studied: Patch Density, Edge Density, Patch Richness, Simpson Diversity Index, Natural Patch Richness, Percentage of Natural Landscape, Mean Natural Patch Area (McGarigal & Marks 1995). First two indicators were used for analysis of landscape fragmentation at patch level, next two at land cover level, while the last three were applied for studies of natural and semi-natural classes at both levels. The studies were performed at six test areas located in different regions of Europe (France, Germany, Poland, Latvia, Spain and Italy), using two different land cover maps. First map was based on Very High Resolution (VHR) Kompsat satellite images (4 m spatial resolution); it included 8 land cover categories with 0.25 ha Minimum Mapping Unit (MMU). CORINE Land Cover (CLC) map 2006 (25 ha MMU) was the second map used for analyses. Number of land cover classes in case of CLC map varied from 9 for Poland till 14 for France. All above mentioned indicators were calculated for grids with 100, 200, 500 and 1000 meter cell size, corresponding to 1, 4, 25 and 100 ha, respectively. The obtained results reveal high usefulness of land cover maps based on VHR satellite images for analysis of landscape fragmentation, even for grids with 100 m cell size. It was found that at patch level these materials are superior to CLC classifications, irrespective of cell area. In case of land cover level VHR data are better while using 100 and 200 m grid cells, whereas for larger cell sizes – 500 and 1000 m – results are not so evident, depending on degree of landscape fragmentation and spatial structure characteristic for individual land cover classes.

Mapping land cover and forest density in Zagros forests of Khuzestan province in Iran: A study based on Sentinel-2, Google Earth and field data

Previous land cover maps covering northern Canada have been of insufficient spatial or thematic detail to address emerging northern issues such as wildlife habitat, land use planning, and fine-scale land cover dynamics. Mapping northern land cover requires medium-resolution (~30 m) remote sensing data to effectively characterize cover types that are spatially heterogeneous and cannot be consistently represented at coarser (250 m – 1 km) scales. In this paper, we present a land cover map of northern Canada at 30 m spatial resolution suitable for application in northern land use planning, wildlife habitat assessment, and climate change impact assessment and adaption. Orthorectified circa 2000 Landsat data were acquired from the Centre for Topographic Information, with coverage from the treeline to the northern tip of Ellesmere Island, and combined into 16 radiometrically balanced large-area mosaics. A stratified unsupervised cluster labelling approach was used for map generation. Literature on northern land cover and vegetation mapping and numerous northern vegetation surveys were examined to define a land cover legend containing 15 classes. Field data gathered during several campaigns were used in conjunction with other available medium-resolution land cover maps to develop a dataset for training and validation. Standardized accuracy assessment is limited due to the cost of field data acquisition and the small archive of reference data in northern regions. Comparison with field data used only to aid cluster labelling suggests 81.5% accuracy for 76 plots, and examination of subpixel land cover distribution within each 1 km Circumpolar Arctic Vegetation Map (CAVM) class shows good agreement. The map is publicly available through the Natural Resources Canada Geogratis portal in 1 : 250 000 scale National Topographic System (NTS) map sheets.