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"Soil erosion is a crucial environmental problem in the Manjunto watershed, Bengkulu Province, Indonesia. It has economic implications and environmental consequences. Assessment of potential soil erosion rate is useful in designing soil conservation strategies within the framework of integrated watershed management. Information obtained from Remote Sensing (RS) and Geographic Information System (GIS) framework supports decision makers in preparing more accurate spatial maps in less time and cost. The aim of this research is to assess the average annual rate of potential soil erosion in Manjunto watershed for each soil mapping unit using remote sensing data, namely Normalized Difference Vegetation Index (NDVI) and Slope. The NDVI value obtained from satellite imagery processing while slope value obtained from Digital Elevation Model-Shuttle Radar Topographic Mission (DEM-SRTM) processing. The results showed that the eroded catchment area increased significantly. The average annual rate of potential soil erosion in Manjunto watershed in the year 2000 amounted to 3.00 tons ha- 1 year-1, while in the year 2009 there was a significant increase to 27.03 ton ha-1year-1. The levels of erosion hazard in soil mapping unit numbers 41, 42 and 47 are classified in the very heavy category. Soil mapping unit numbers 41, 42 and 47 should be a first priority in soil and water conservation activities."

"DAS Cisadane termasuk dalam DAS prioritas dengan status untuk dipulihkan. Perkembangan penduduk dan perubahan penggunaan tanah di daerah tersebut menjadi salah satu permasalahan terhadap kebutuhan akan sumber daya air akibat berkurangnya wilayah resapan air, sehingga identifikasi wilayah resapan diperlukan untuk menjaga dan memperbaiki fungsi resapan air. Pemetaan potensi resapan air dilakukan dengan model RTkHL-DAS dan SCS-CN menggunakan data jenis tanah, curah hujan, lereng dan penutup lahan yang diperoleh dari peta analog dan data penginderaan jauh (CHIRPS, Landsat 8 dan DEMNAS). Model dari RTkHL-DAS dilakukan modifikasi untuk membuat model terbaru yaitu model I dengan perubahan data penutup lahan menjadi NDVI dan Model II dengan mengubah jenis tanah menjadi SMI dan penambahan kriteria densitas drainase pada model I, serta dilakukan pembobotan hasil normalisasi bobot penelitian terdahulu. Kedua model diproses dengan metode multi-kriteria dengan teknik weight overlay untuk menghasilkan kelas potensi resapan air. Hasil menunjukkan resapan air model Model RTkHL-DAS dengan menggunakan menghasilkan kelas baik (65,6%), Normal Alami (4,6%), mulai kritis (5,7%), agak kritis (22,1%) dan kritis (2%). Model SCS-CN dengan nilai CN dari 20-40 (23%), 60-80 (52,10%) dan 80-100 (24,90%). Berdasarkan hubungannya dengan fluktuasi muka air tanah, kedua model memiliki hubungan dengan perubahan fluktuasi muka air tanah dengan masing-masing kelas yang dihasilkan dari tiap model. Model I menghasilkan 4 kelas potensi resapan air yaitu sangat tinggi (1,9%), tinggi (40,8%), sedang (55,8%), dan rendah (1,5%), memiliki keunggulan dalam mendeteksi variasi kelas pada kondisi dominan hutan/vegetasi. Sedangkan Model II menghasilkan kelas sangat tinggi (0,3%), tinggi (53,4%), sedang (44,6%), dan rendah (1,7%). Memiliki keunggulan dalam mendeteksi variasi kelas pada kondisi dominan permukiman/sawah yang diperkuat dengan hubungan fluktuasi muka air tanah di wilayah tengah-hilir terhadap luas kelas potensi resapan air di mana Model II mengungguli Model I.

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The Cisadane watershed is included in the priority watershed with status for restoration. Population development and changes in land use in the area are one of the problems with the need for water resources due to reduced water catchment areas, so the identification of recharge areas is needed to maintain and improve the function of water catchments. The mapping of recharge potential was carried out using the RTkHL-DAS and SCS-CN models using data on soil types, rainfall, slopes, and land cover obtained from analog maps and remote sensing data (CHIRPS, Landsat 8, and DEMNAS). The model from the RTkHL-DAS was modified to create the latest model, namely model I by changing the land cover data to NDVI and Model II by changing the soil type to SMI and adding drainage density criteria from model I, as well as weighting the results of the normalization of the weights of previous studies. Both models were processed using a multi-criteria method with a weight overlay technique to produce a class of water infiltration potential. The results show that the water infiltration of the RTkHL-DAS model using the good class (65.6%), Natural Normal (4.6%), critical (5.7%), moderately critical (22.1%), and critical ( 2%). SCS-CN model with CN values from 20-40 (23%), 60-80 (52.10%) and 80-100 (24.90%). Based on its relationship with groundwater level fluctuations, both models have a relationship with changes in groundwater level fluctuations with each class resulting from each model. Model I produces 4 classes of water infiltration potential, namely very high (1.9%), high (40.8%), moderate (55.8%), and low (1.5%). Effective in detecting classes in dominant conditions of forest/plantation. While Model II produces very high (0.3%), high (53.4%), medium (44.6%), and low (1.7%). It has the advantage of detecting class variations in the dominant conditions of settlements/rice fields which is strengthened by the relationship between groundwater level fluctuations in the middle-downstream region to the class area of water infiltration potential where Model II outperforms Model I."

"This book puts great emphasis on the importance of Integrated Water Resources Management IWRM as the way forward towards food, water, and energy security. It offers better ways and means of managing the limited water resources, using water more efficiently, adopting new policies in order to cope with climate change and drought, increase water supply through the use of non conventional water resources, especially waste water, inducing stakeholders participation and information exchange and raise public awareness to the value of water. "

"Remote sensing provides information on how remote sensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. Remote sensing will discuss remotely sensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotely sensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed."