Landscape Influences on Stream Habitats and Biological Assemblages

<em>Abstract.</em>—The influence of land use and instream physical habitat on biotic condition of fish assemblages was investigated for 48 stream reaches in the Huron and Raisin rivers. The amount of agriculture and wetland in the catchment and 100-m stream buffers had the strongest relationships with instream physical habitat, and these two categories of land use/cover were negatively correlated with each other (<em>r </em>= –0.70, <em>p </em>= <0.01). Agriculture was associated with high levels of sedimentation and reduced flow stability, while wetland was associated with low sedimentation and stable flows. The index of biotic integrity (IBI) was positively related to low sedimentation, stable flows, and the presence of fine gravel (2–8 mm). It was not significantly correlated with agricultural land use, but was positively related to natural land cover (forest + wetland combined) in the buffer. The best linear regression model using physical habitat and land-use variables from all sites adequately predicted IBI scores (adjusted <EM>R</EM><SUP>2</SUP> = 0.52). However, when the Huron and Raisin basins were treated separately, some of the included variables differed, and model fit increased (Huron adjusted <EM>R</EM><SUP>2</SUP> = 0.76, Raisin adj. <EM>R</EM><SUP>2</SUP> = 0.79), indicating that relations of fish assemblages to physical habitat and land use differed between basins. The Raisin model included land cover variables, while the Huron model included only variables related to physical habitat. Thus instream habitat and land cover may play different roles in these basins, suggesting the benefit of forming separate models for individual basins when sufficient data are available.

2022 ◽  
Vol 964 (1) ◽  
pp. 012005
Author(s):  
P K Diem ◽  
N K Diem ◽  
N T Can ◽  
V Q Minh ◽  
H T T Huong ◽  
...  

Abstract This study aimed to evaluate the applicability of using time-series data of spatiotemporal fusion Landsat-MODIS imagery for mapping agricultural land use in An Giang province, Vietnam. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was adopted for fusion techniques to integrate the relatively high spatial resolution of Landsat (30 meters) and frequently revisit time of MODIS (MOD09Q1, 8-days). The Maximum Likelihood Classifier (MLC) was then used to classify the land cover categories based on variations of NDVI (Normalized Difference Vegetation Index) time-series over the observation period. The overall accuracy is about 84.9%, and a kappa coefficient of K=0.7, which revealed the effectiveness of using Fusion Landsat-MODIS NDVI data in land cover classification at the provincial scale. The current of the agricultural land use was finally mapped, including seven categories, namely built-up areas (10.49%), double rice crops (4.8%), triple rice crops (68.24%), perennial tree/orchards (4.08%), annual crops (7%), water surfaces (3.07%), and forest (2.32%). The results indicate that the agricultural land use cover can be detected in detail using Fusion Landsat-MODIS imagery. The classification is dramatically higher compared to the map classified by a conventional method of solely Landsat 8 image analysis (overall accuracy of 67.3% and Kappa coefficient K=0.35). The research outcomes will support the detailed information for managers in evaluating the impact of climate change on the rice cropping system toward sustainable agriculture development.


Land ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 24
Author(s):  
Mariana Vallejo ◽  
M. Isabel Ramírez ◽  
Alejandro Reyes-González ◽  
Jairo López-Sánchez ◽  
Alejandro Casas

The Tehuacán-Cuicatlán Valley, Mexico, is the semiarid region with the richest biodiversity of North America and was recently recognized as a UNESCO's World Heritage site. Original agricultural practices remain to this day in agroforestry systems (AFS), which are expressions of high biocultural diversity. However, local people and researchers perceive a progressive decline both in natural ecosystems and AFS. To assess changes in location and extent of agricultural land use, we carried out a visual interpretation of very-high resolution imagery and field work, through which we identified AFS and conventional agricultural systems (CAS) from 1995 to 2003 and 2012. We analyzed five communities, representative of three main ecological and agricultural zones of the region. We assessed agricultural land use changes in relation to conspicuous landscape features (relief, rivers, roads, and human settlements). We found that natural ecosystems cover more than 85% of the territory in each community, and AFS represent 51% of all agricultural land. Establishment and permanence of agricultural lands were strongly influenced by gentle slopes and the existence of roads. Contrary to what we expected, we recorded agricultural areas being abandoned, thus favoring the regeneration of natural ecosystems, as well as a 9% increase of AFS over CAS. Agriculture is concentrated near human settlements. Most of the studied territories are meant to preserve natural ecosystems, and traditional AFS practices are being recovered for biocultural conservation.


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