HOW DO RIPARIAN ZONES AND GEOMORPHIC HOLLOWS AFFECT STORMFLOW GENERATION IN HEADWATER CATCHMENTS?

2013 ◽  
Author(s):  
R.C. Sidle
Keyword(s):  
Riparian Zones ◽  
Headwater Catchments ◽  
Stormflow Generation

Progress Towards Understanding Stormflow Generation in Headwater Catchments

1998 ◽  
pp. 483-498 ◽  
Author(s):  
Roy C. Sidle ◽  
Yoshio Tsuboyama ◽  
Shoji Noguchi ◽  
Ikuhiro Hosoda ◽  
Motohisa Fujieda ◽  
...  
Keyword(s):  
Headwater Catchments ◽  
Stormflow Generation

Stormflow generation in two headwater catchments in eastern Amazonia, Brazil

2008 ◽  
Vol 22 (17) ◽  
pp. 3285-3293 ◽  
Author(s):  
A. J. Wickel ◽  
N. C. van de Giesen ◽  
T. D. d. A. Sá
Keyword(s):  
Headwater Catchments ◽  
Stormflow Generation

DIVERSITY OF MICROCHIROPTERAN BATS IN FOREST FRAGMENTS AND RIPARIAN ZONES IN AN OIL PALM PLANTATION ESTATE

SIMBIOSA ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Fauziah Syamsi
Keyword(s):  
Oil Palm ◽  
Riparian Zones ◽  
Habitat Patch ◽  
Forest Fragments ◽  
Oil Palm Plantation

Kelapa sawit merupakan salah satu tanaman meningkat paling pesat di dunia, dan mencakup lebih dari 13 juta ha di Asia Tenggara. Sumatera memiliki sejarah yang relatif panjang budidaya kelapa sawit komersial, dan banyak perkebunan telah menggantikan hutan hujan. Biasanya ini perkebunan monokultur mendukung spesies lebih sedikit daripada hutan, namun ada sangat sedikit informasi yang tersedia untuk kelelawar. Kami mencicipi kelelawar pemakan serangga di Sumatera Barat dalam perkebunan kelapa sawit matang di mana beberapa tutupan hutan dipertahankan di fragmen hutan di bukit-bukit dan di sepanjang sungai. Menggunakan total 180 kecapi perangkap malam kami dibandingkan dengan komunitas kelelawar dalam tiga jenis habitat: patch hutan, zona riparian dan perkebunan. Total kami ditangkap 1108 kelelawar yang mewakili 21 spesies dan 5 keluarga, dan mayoritas ini (dalam hal spesies dan kelimpahan) ditemukan di fragmen hutan. perkebunan kelapa sawit ditemukan menjadi habitat miskin untuk kelelawar - hanya empat orang dari dua spesies ditangkap. daerah pinggiran sungai didukung keanekaragaman menengah, dan mungkin penting sebagai koridor satwa liar antara fragmen hutan. Kata kunci : Biodiversitas, keleawar Microchiropteran

EVALUATING THE NATURAL TRANSPORT PATHWAYS OF IODINE IN HEADWATER CATCHMENTS

2017 ◽  
Author(s):  
Kristina M. Gutchess ◽  
◽  
Shannon Garvin ◽  
Li Jin ◽  
Wanyi Lu ◽  
...  
Keyword(s):  
Transport Pathways ◽  
Headwater Catchments

scholarly journals Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

2021 ◽  
Vol 13 (10) ◽  
pp. 1997
Author(s):  
Joan Grau ◽  
Kang Liang ◽  
Jae Ogilvie ◽  
Paul Arp ◽  
Sheng Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Riparian Zone ◽  
Agricultural Landscapes ◽  
Agricultural Watershed ◽  
Riparian Zones ◽  
Surface Model ◽  
Low Resolution ◽  
Channel Network ◽  
Aerial Vehicle

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

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