A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

The Tharsis ridge system is roughly circumferential to the regional topographic high of northern Syria Planum and the major Tharsis volcanoes. However, many of the ridges have orientations that deviate from the regional trends. Normals to vector means of ridge orientations, calculated in 10° boxes of latitude and longitude, show that the Tharsis ridges are not purely circumferential. Intersections of vector mean orientations plotted as great circles on a stereographic net show that the ridges are not concentric to a single point but to three broad zones. These data indicate either that the Tharsis ridge system did not form in response to a single regional compressional event with a single stress center or that the majority of the ridge system did form in a single event but with locally controlled deflections in the generally radially oriented stress field. Superposition relationships and relative ages suggest that the compressional stresses that produced the ridges occurred after the emplacement of the ridged plains volcanic units (early in the Hesperian period) but did not extend beyond the time of emplacement of the Syria Planum Formation units or the basal units of the Tharsis Montes Formation (during the latter half of the Hesperian period). Comparison of topographic data with the locations of ridges demonstrates a good correlation between the topographic edge of the Tharsis rise and outer extent of ridge occurrence. Compressional deformation related to Tharsis is present out to about 5100 km from the regional topographic center located near northern Syria Planum. The innermost extent cannot be determined because the ridged plains units have been buried by more recent volcanic units, although ridge formation appears to have extended farther inward than is presently observed. Models involving a combination of isostatic stresses and stresses resulting from flexural loading appear to explain best the observed tensional features, topography, gravity, and, to a first approximation, the locations and orientations of the compressional ridges.

Orientation, relative age, and extent of the Tharsis Plateau ridge system. [volcanic and tectonic regions on Mars]

A review of forest fire and policy response for resilient adaptation under changing climate in the Eastern Himalayan region

Developing a predictive model for forest fires occurrence is an important activity in a fire prevention program The model describes characteristics of areas where fires occur based on past fires data It is essential as an early warning system for preventing forest fires, thus major damages because of fires can be avoided This study describes the application of data mining technique namely decision tree on forest fires data We improved the ID3 decision tree algorithm such that it can be utilized on spatial data in order to develop a classification model for hotspots occurrence The ID3 algorithm which is originally designed for a non-spatial dataset has been improved to construct a spatial decision tree from a spatial dataset containing discrete features (points, lines and polygons) As the ID3 algorithm that uses information gain in the attribute selection, the proposed algorithm uses spatial information gain to choose the best splitting layer from a set of explanatory layers The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features The proposed algorithm has been applied on the forest fire dataset for Rokan Hilir district in Riau Province in Indonesia The dataset contains physical data, socio-economic, weather data as well as hotspots and non-hotspots occurrence as target objects The result is a spatial decision tree with 276 leaves with distance from target objects to the nearest river as the first test layer and the accuracy on the training set of 8769% Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing a spatial decision tree from a spatial dataset The algorithm results a predictive model for hotspots occurrence from the real dataset on forest fires with high accuracy on the training set

Classification model for hotspot occurrences using spatial decision tree algorithm

The likelihood model so developed, envisaging various factors and flammability, accounted for different scenarios as a result of pair-wise comparison on an ordinal scale in a knowledge matrix. The inferential statistics computed indicated the robustness of the model and its insensitivity to moderate changes. It makes it possible for this forest fire likelihood model to predict and prevent a forest fire in an effective and scientific manner because it can assume forest fire likelihood in real time and present in proper time.

A geo-information system approach for forest fire likelihood based on causative and anti-causative factors

Object-based image analysis (OBIA) is an image classification that is oriented to object patterns that use image objects as the basis for processing, calculates characteristics per object, and extracts land cover information from remotely sensed images. This study aims to detect salt ponds using Sentinel 2 satellite data with an object-based classification model. The center of salt production, which is also an experimental area for the development of industrial salt from the ministry of maritime affairs and fisheries on the north coast of the island of Java was selected as the study area. The unit of analysis for this classification is the segmented object of sentinel image. The classification scheme built to detect salt ponds using OBIA consists of level 1, level 2, and level 3. Level 1 is to separate land and water using a Near Infrared canal. Level 2 is to separate land use from object segmentation results in land class at level 1 using NDVI transformation, and level 3 is to separate salt and non-salt ponds from the segmentation results of land use at level 2 using sentinel image transformation algorithm for the distribution of chlorophyll-a. The result shows chlorophyll-a estimation image transformation from sentinel useful to separate salt and non-salt ponds. Many researchers have been reported that chlorophyll-a does not live in the salinity range of salt ponds greater than 50 ppt, meanwhile, in non-salt ponds, chlorophyll-a is used as natural feed for cultivated animals. Furthermore, the research shows a classification scheme of salt ponds and non-salt ponds can be derived from sentinel 2 imagery with OBIA approach

https://iopscience.iop.org/article/10.1088/1755-1315/1109/1/012058/pdf

Salt pond detection on Sentinel 2 Satellite Imagery using Object-Based Image Analysis (OBIA) Approach

rlhe stucly was originally designed to assess vegetation diversity in Borneo's tropical rain forest with ecological consequence that can be retrieved from Landsat erM+. \"le were particularly interested in recognitien of spectral pattern of land cover for developing land cover and forest degradation mapping by visual analysis of satellite imagery in Sabah, Malaysia. In addition unsupervised, supervised classification, band ratio of TM4, TM41TM5, and TM2!TM7, and pattern decernposition analysis were tested to evaluate the interpretation. iVe proposed a reliable method of forest degradation mapping in Sabah from land cover map incorporated with their spectral pattern. Dense forest was found clearly discriminated from water and soil by using visual interpretation a d pattern deeomposition analysis, particularly in channel 3, 4, and 5. Hewever, it was difficult to discriminate spectral pattern of non-forest through decomposition analysis. Logged-over forest was found dominant in the study area (36%), followed by plantatien (21%), and only about 17% covered with natural dense forest, It is estimated that approximately 76% of the forest in Sabah, have been threatened with forest degradation. Iteywortts: spectral pattern, land coverfuse, Landsat Ei'M+, forest degradation. INTRODUCTION Borneo Island, next to the Arriazon of Brazil, is the secend largest ropical rain forest ecosystem in the world. It has the mest species-rich communities known anywhere else on earth and perferms important envirenment and landscape functien ovHTTEMoRE, 1975). However, the vegetation biodiversity oi Borneo tropical forest has been peorly studied and understood although it is subject to be intensive converting for timber 'iGraduate School of Agricultural and life Sciences, the University of lbkyo, 1-1-1 Yayoi Bunkyo-ku, Tbkyo 113-8657, Japan. e-mail/ darmawan@affrc.go.jp '\" Forestrv and Forest Produets Research Institute, 1 Matsunosato, Tsukuba-si, Ibaraki 305-8687, Japan.e-mail:rslsaite@ffpri.aifrc.go.jp '3 Centre for Ecological Research, }fyoto University, 509-3 Ohtsuka, Kamitanakami Hirano-cho, Ohtsu, Shiga 520-2113, Japan. e-mail: kitayama@ecology. kyoto-u.ac.jp extraction, agriculture, mining and settlemenL lherefore, study of characteristic ef land cover is important source for understanding the vegetation biodiversity Availability of ]and cover information is a prerequisite for understanding the global change process and will enhance our capability to predict land use and vegetation pattern and global ferest environment (forest fire, degradation forest etc,) for forest management strategles. Recently several image classification techniques have been developed to map land cover and forest degradation (I2xNGFoRD and BELL, 1997; Sm.MABuKuRo et al., 1998). In general, rnethods to extract land cover/use from remotely sensed data can be summarized as: Visual Analysis CXUX) (LILLEsANT) and lhEFER, 1987; HoMmo, 1991), Multivariate Spectral Analysis (MSA) (pt(A/rllER, 1987; SMITH, 1999), Spectral Mixture 1inalysis (SMA) (ADAM et al,, 1986; rllioMpKINs et al., 1997), and Data Integration (e.g, CoNiww, 1997) , The rnost famous technique to extract information from satellite irnagery that still preserves the highest information on vegetation is the Normalized Difference Vegetation Index

Recognition of Spectral Pattern Characteristic of Land Cover for Assisting Visual Interpretation of Landsat ETM^+ :A Forest Degradation Mapping in Tropical Rain Forest of Sabah, Malaysia

Watershed performance can be seen from various aspects, including the ability of the land to absorb water, the amount of erosion, water quality, and water regime index. This study is an effort to manage critical watersheds in managing water absorption based on biophysical parameters such as soil type, slope, geology, rainfall, and land cover. This research was conducted in 2021 in the Citarum watershed, limited to shallow aquifers. It is important to be mapped because it is part of 15 critical watersheds in Indonesia. The method used is to integrate RS, GIS, and Analytical Hierarchy Process (AHP). The results showed that there was a distribution of water absorption in the Citarum watershed, including very low 1.04%, low 21.33%, Medium 25.20%, high 38.80%, and very high 13.63%, with a total area of 638,511.57 ha. The dominant absorption rate is relatively high, 38.8%, and very high, 13.63%, influencing water availability in the Citarum watershed. From this study, AHP shows that the most influential in water infiltration into shallow aquifers is soil, with a score of 0.5, followed by Rainfall Criteria with a score of 0.26, followed by the land cover at 0.13, then slope and geology.

Assessment of rainwater absorption zone in Citarum Watershed using GIS and AHP

A heavy scale of forest fire occurred during the year of 1997/1998 in Indonesia, which burnt millions hectares of the tropical rain forest. The Indonesian East Kalimantan province was the most severely affected by this forest fire, which have destructed a natural forest and induced accelerated soil erosion. A repetitive forest fires in this province lead to bring a land and forest degradation process. This study develops a forest fire hazard model using remote sensing technique integrated with Geographic Information Systems (GIS). It also attempts to establish a method for land and forest degradation hazard mapping in lowland area of East Kalimantan based on analyzing of forest fire hazard with respect to soil erosion hazard. The following variables were derived for a study area: vegetation fuel type, terrain, road, and bare soil. Those variables were weighted according to their impact on the forest fire hazard or accelerating soil erosion. Supervised classification and NDVI analysis are applied to Landsat TM to derive a fuel type. Tasseled cap transformation, the simplified end member technique and the GIS neighborhood analysis was carried out to identify bare soil areas, which consist of maximum approximating of a pure bare soil on 3 by 3 pixel size. The final result offers a model of forest fire and soil erosion hazards. The models of forest fire hazard show reliable from perspective of the proportion of areas affected by high burnt areas of forest fire in 1997/1998. A derived land and forest degradation map has been offered. Based on this map it is possible to identify and monitor areas affected by forest fire or to evaluate the pattern of land and forest degradation.

Forest Fire Hazard Model Using Remote Sensing and Geographic Information Systems: Toward understanding of Land and Forest Degradation in Lowland areas of East Kalimantan, Indonesia

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Mulyanto Darmawan

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Papers

Salt pond detection on Sentinel 2 Satellite Imagery using Object-Based Image Analysis (OBIA) Approach

Recognition of Spectral Pattern Characteristic of Land Cover for Assisting Visual Interpretation of Landsat ETM^+ :A Forest Degradation Mapping in Tropical Rain Forest of Sabah, Malaysia

Assessment of rainwater absorption zone in Citarum Watershed using GIS and AHP

Forest Fire Hazard Model Using Remote Sensing and Geographic Information Systems: Toward understanding of Land and Forest Degradation in Lowland areas of East Kalimantan, Indonesia