Priority determination of some sub watersheds experienced difficulties based on the fact that data collection of the related sub watersheds takes time and quite costly. Whereas to comprehensively manage sub watershed, some prioritizet sub watersheds have to be chosen to manage holistically and integrally with good coordination between some related agencies. The study was carried out in India in two Nawagaon Maskara Raoi watersheds, Saharanpur city, located 250 km to the east of New Delhi. The study appointed a sub watershed to be the prioritu among the other 10 available using quantitative calculation method (MMF: Morgan, Morgan, and Finney method). The research aimed to measure the quantitative erosion based on MMF model and calculate the value index to determine the priority in sub watershed.The erosion calculation by MMF model produced five erosion levels i.e. very low (vl=0-5t/ha/yr), low (l=5-10 t/ha/yr), medium (m=10-25 t/ha/yr), high (h=25-50 t/ha/yr), and very high (vh 50 t/ha/yr). At the highest erosion level (vh) location with the most extensive land damage to the narrowest respectively was Sarbar Rao (SB) = 116.84 ha, Galr Rao (GR), Sahansra Thakur (ST), Shakumbari Rao (SH), Khawonwala Rao (KH) Kahan Rao (KR), Nawagaon Rao (NW), Chamarla Rao (CH), Track Fallows (TF), Barkala Rao (BR), and Maskara Rao (MR) = 0.34 ha. Of 11 sub watershed, priority value index was calculated, and the highest value (main priority) to the lowest one (least priority) is respectively as follows: GR (Galr Rao) = 33,5, KR (Kahan Rao), ST (Sahansra Thakur), TF (Track Fallows), BR (Barkala Rao), SB (Sarbar Rao), SH (Shakumbari Rao), CH (Chamarla Rao), KH (Kharonwala Rao), MR (Maskara Rao), and NW (Nawagaon Rao) = 18,2. Therefore the main priority fell for sub watershed Galr Rao (997.32) and the least priority for watershed Nawagaon Rao (7646.78 ha).Â Keywords: Land damage, quantitative erosion, MMF, watershed prioritization, RS & GIS
Kejadian tanah longsor di Indonesia belakangan ini terus meningkat intensitas dan sebarannya. Tanah longsor terjadi jika tahanan geser massa tanah atau batuan lebih kecil dari tekanan geser pada sepanjang bidang longsoran yang disebabkan oleh adanya peningkatan kejenuhan air tanah saat musim penghujan. Tujuan penelitian adalah untuk mendapatkan teknik identifikasi daerah yang berpotensi longsor, agar masyarakat mudah mengenali dan tidak terjadi korban yang tidak perlu. Lokasi penelitian adalah pada lahan yang berada pada wilayah berpotensi longsor di Kabupaten Purworejo, Banjarnegara, dan Karanganyar di Provinsi Jawa Tengah. Metode pengamatan longsor dengan mencatat beberapa parameter penyebab longsor, antara lain: kemiringan lereng, curah hujan, tekstur tanah, regolith tanah, sesar, kepadatan penduduk.Â Hasil pengamatan daerah yang berpotensi longsor berurutan dari sub Daerah Aliran Sungai (DAS) terberat: Banjarnegara di sub DAS Merawu (12 cm), Purworejo di sub DAS Gesing (8 cm), dan Karanganyar di sub DAS Mungkung-Grompol (0 cm). Semakin tinggi kandungan liat maka semakin berpotensi longsor, selain faktor kemiringan lereng, kedalaman regolit, adanya sesar, dan tingginya curah hujan.Â Dampak atau manfaat penelitian ini adalah: a) mengantisipasi/meminimalisir terjadinya korban jika terjadi longsor pada daerah yang berpotensi longsor, b) memberi informasi kepada masyarakat untuk mengenal daerah berpotensi longsor dan beradaptasi dengan bencana longsor, c) memberi peringatan dini dengan memasang berbagai alat, antara lain: extensometer, penakar hujan ombrometer, dan mengenalkan berbagai macam tanaman yang tahan longsor.
The technical Remote Sensing and Geographic Information System (GIS) had been used to detet vegetation changing of natural forest due to management differences. The target of the study were to determine classification method of satellite imagery in operational scale so that the method can be transfered and applicable for the user. The studied was done at forest concession of PT SLJ-IV (Sumalindo Lestari Jaya-IV), Yanjung Redeb, east Kalimantan Province. Landsat TM with 7 bands and high resolution of 1994 and 1996 were used to differentiate the condition before and after cutting in 1995. The analysis of the Landsats were only in the area where the TPTI (Indonesian Selective Logging System) and TPTI (Indonesian Strip Logging System) system applied. Based on the analysis and field check, it was found that the impact of logging caused decreasing dense forest about 21,3% and inreasing secondary forest around 6,3%. The highest dynamic of each band was band first and the lowest was band second. The differences in the mean of 2 band will give a more clear appearance of theÂ imagery.
In 2000, the area of DAS critical land in Indonesia is approximately 23,242,881 ha which consists of forest area 8,136,646 ha (35%) and non forest area 15,106,234 ha (65%). In the contrary, the fact shows that in 1989/ 1990 (the beginning of âPelitaâ/ the five years development planning owned by the government), the area of DAS critical land in Indonesia was 13,180,000 ha only that consists of forest area 5,910,000 ha and non forest area 7,270,000 ha. The cause and its location of negative improvement of the above DAS has not been predited yet. The one of the causes is the weakness of information system on very DAS management system in the aspect of biophysical, soial, eonomical, and cultural. Therefore, it needs the improvement of DAS management which is supported by the result of research and development. The purpose of this research is to get the potency information and the possibility of sensitivity of the land resources in the frame of DAS management with biophisical land as the parameter. Sub DAS of Merawu (21,860 Ha) isas one of the parts of âbuluâ DAS Serayu with stream flow minimum 0,81 m3/second and maximum 108 m3/second. The sub DAS of Merawu as the part of âbuluâ Serayu has the type of climate A and B with annual rainfall approximatelly >2,000 mm and it can support everything in the stream flow of in order to prevent the flood. This ondition is caused by the permanent vegetation such as forest, underbrsuh or srub, tea garden, as well as multi â plantgarden that has around 40% happen in the ineptisol land, although precipitous slope and very precipitous (>25%). The technique of land conversation is good enough in its development, mainly in the dry section of the field by using âteras gundulâ and âteras bangkuâ the society near Sub DAS of Merawu is densely populated, its is around 517 up to 827 persons/ square with their main profession as farmer and their income is around Rp 2.000.000 per year. Bya analysing the above DAS management, it an show that sub DAS of Merawu has the potency of water both for internal and external DAS consuming. The potency of using the land for farimng one season in length (class II, III, and IV) consists of around 50,8%. The development multy plant garden (25% area of DAS) is as the type of potential farming effort because of the diversity of both the result and time; besides it is also as the form of protection toward the effetive land. The possibility of sensitivity is too wide land which is susceptible toward the slide (land slide), mainly in the middle part of the DAS. The live dependee of land which strong enough is as the threat toward the future resoures conservation.
Short-term Planning of Watershed Using Calculation of Quantitative Erosion Method Based on Remote Sensing and Geographic Information SystemPriority determination of some sub watersheds experienced difficulties based on the fact that data collection of the related sub watersheds takes time and quite costly. Whereas to comprehensively manage sub watershed, some prioritizet sub watersheds have to be chosen to manage holistically and integrally with good coordination between some related agencies. The study was carried out in India in two Nawagaon Maskara Raoi watersheds, Saharanpur city, located 250 km to the east of New Delhi. The study appointed a sub watershed to be the prioritu among the other 10 available using quantitative calculation method (MMF: Morgan, Morgan, and Finney method). The research aimed to measure the quantitative erosion based on MMF model and calculate the value index to determine the priority in sub watershed.The erosion calculation by MMF model produced five erosion levels i.e. very low (vl=0-5t/ha/yr), low (l=5-10 t/ha/yr), medium (m=10-25 t/ha/yr), high (h=25-50 t/ha/yr), and very high (vh 50 t/ha/yr). At the highest erosion level (vh) location with the most extensive land damage to the narrowest respectively was Sarbar Rao (SB) = 116.84 ha, Galr Rao (GR), Sahansra Thakur (ST), Shakumbari Rao (SH), Khawonwala Rao (KH) Kahan Rao (KR), Nawagaon Rao (NW), Chamarla Rao (CH), Track Fallows (TF), Barkala Rao (BR), and Maskara Rao (MR) = 0.34 ha. Of 11 sub watershed, priority value index was calculated, and the highest value (main priority) to the lowest one (least priority) is respectively as follows: GR (Galr Rao) = 33,5, KR (Kahan Rao), ST (Sahansra Thakur), TF (Track Fallows), BR (Barkala Rao), SB (Sarbar Rao), SH (Shakumbari Rao), CH (Chamarla Rao), KH (Kharonwala Rao), MR (Maskara Rao), and NW (Nawagaon Rao) = 18,2. Therefore the main priority fell for sub watershed Galr Rao (997.32) and the least priority for watershed Nawagaon Rao (7646.78 ha).Â
The land that was increasingly crowded resulting from the inhabitantsâs speeding-up pressure, required the utilisation of the land to be as efficient and as effectively as possible. For this matter must be known by the LUC (Land Use Capability) class respectively the unit of the land management, so as to be known as early as possible the obstacle factor from the land and could be done by the utilisation of the land as optimally as possible. The implementation of the LUC determination must be carried out a stage for the sake of a stage by counting LUC respectively the main factor, so as to be received by LUC-Soil, LUC-Erosion, and LUC-Slope. The next one of the three of this LUC were just counted by the value of the maximum to appoint LUC Final. LUC-Slope by being based on the Wischmeier and Smith (1978), LUC-Erosion was counted by using the quantitative MMF erosion formula (Morgan, Morgan, and Finney), and LUC-Soil by gathering the physical data the field took the form of the texture data of the land, drainage, solum and the percentage of the rock in the surface. LUC-Erosion and LUC-Soil were received by 5 LUC classes (I, II, III, IV, IV, and VI), whereas LUC-Slope was received by 7 LUC classes all of them except the V. LUC I class until IV were recommended for the agricultural crop and LUC V until VIII for the forestry crop. From 11 of Sub Watershed LUC VIII was expanded 107.54 ha to Sub Watershed Sarbar Rao and narrowest to Sub Watershed Maskara Rao (0.12 ha). On the other hand for LUC II was expanded to Sub Watershed Nawagaon Rao (1136.8 ha) and narrowest (1.51 ha) to Sub Watershed Shakumbari Rao. The location of the research in Sub Watershed Nawagaon Rao Mascara the Saharanpur city, India, with the location goegrafis from 30 o 09â 00" N - 30o 21â 00" N and longitude 77 o 34â 00" E - 77 o 51â 00" E, widely the Watershed whole 205.94 km2 or 20594.49 ha. The analysis of the image satelit with IRS (Indian Remote Sensing) LISS IV in January 2005, the analysis of three dimensions with DEM SRTM, and the map of the topography of the sheet 53 F/11, 53 F/12, 53 F/15 and 53 F/16. The aim of the research of determining the LUC class by counting each one of LUC-Soil, LUC-Erosion, and LUC-Slope. The use of the land in the Nawagaon Maskara Rao Watershed in part: Wheat super (969,26), normal Wheat (2753.7 ha), the Orchard (2103.2 ha), the Forest was rather close (3930.5 ha), the Forest was open (3352.1 ha), Scrub (168.62 ha), Brush rocky (658.56 ha), and Open land (1814.8 ha). Was based on results of this research recomendation for LUC VIII was only for the protected forest that might not be touched or produced.
Catchments area can be analyzed as management system. Catchments area acquire input and it processed by the system to produce output. Land covers in catchments area are closely related to land use pattern and to management system. Land use changes to building area, agriculture and another activity are related to anthropological characters effected by change in function from vegetated land to unvegetated land. This condition have negative influences to the condition of carchment area. The damaged level of catchment area can be reflected by flood susceptibility, droughness, erosion and sedimentation, related impact onsite and offsite, so it is need a comprehensive management system from up land to low land river. To give information of land use in catchments area it needs accurate data about land cover in wide range. Remote sensing and Geographic Information System (GIS) are applicable to monitor land coverage of management catchments area. The aim of this paper is to analyze land cover using remote sensing and GIS to catchments area monitoring and evaluation. Land use in watershed connection with the pattern of nature resources by the community and the management of watershed. Total area of land use Grindulu watershed was 65.539 ha. From the map of land use could be seen that the spreading of the equitable meeting forest from the upstream to lower, and most property of the people. Land use became 8 classes, that is: Agroforestry (20%), Open Land (12%), Rare Forest (1%), Dense Forest (29%), Village (34%), Paddy (0.4%), River (0.2%), and Field (3%).
Indonesia is one of the mega-biodiversity countries that have a great responsibility in maintaining the balance of the global climate and forest ecosystems. Drought causes shifting of ecosystems causing disturbances on animal life leading to death of species. Alongside fires in the savanna, drought is a recurrent problem in the park, which occurs every year. This study aims to detect the abundance of water by using satellite imagery in Baluran National Park (BNP). The research analyzed using Landsat satellite imagery ETM7 + in 1999 and 2010 and three (3) main factors that have great potential abundance of water, are: (1) plant density (GI = Greenness Index), (2) soil moisture (WI = Wetness Index), and (3) soil conditions (SBI = Soil Brightness Index). Three factors are summed and divided by three to get 5 levels of water abundance: 1) Very abundant, 2) Abundant, 3) Medium, 4) Few, and 5) Very little. The results showed that the abundance of water decreased between 1999 and 2010 for moderate conditions from 85% to 38%, if the abundance of low water (slightly) increased from 15% to 60%. The level of accuracy of the abundance of water in the field of more than 80% is exactly 91%. The extreme drought conditions will be very dangerous for the survival of flora and fauna in Baluran National Park that are in desperate need of water and potentially in danger of a fire. Construction of water reservoirs and water supply continuously using a water tank in the dry season is very necessary in the Baluran National Park.
Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.