Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

Abstract

Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time.

Assessing Riparian Vegetation Condition and Function in Disturbed Sites of the Arid Northwestern Mexico

“…Previous land use and vegetation classification studies in the area have found that agriculture and cattle ranching are the activities that contribute the most to land cover dynamics [8], which is consistent with the present study given that those activities were present in all of the study sites and are determinant to the disturbance degree of riparian habitats. Different indices that evaluate ecological parameters in riparian habitats have been used in many places in Mexico [15,[58][59][60], all of them use vegetation parameters such as composition, cover and species density to estimate riparian habitat condition.…”

“…Our class scheme (Table 4) was developed using observations from the field (regarding community composition and stand physiognomy) and previous classification studies conducted in the area [8]. In order to simplify the classification scheme, all desert vegetation types adjacent to the river is referred to as "Desert Scrub", even though the formal designation for the Plains of Sonora region (as described by Shreve and Wiggins 1964 classification) is Arbosuffrutescent Desert.…”

“…On top of this, it has been reported that the SMR aquifer shows signs of overexploitation based on recharge-extraction analyses; official data reported an annual deficit of 1,500,000 m 3 [7], although the most recent update reports an availability of 17,508,107 m 3 [5], which arises some confusion and concern due to the fact that this last number was based on a 2008 hydrogeological study elaborated for a mining project. Given the above, riparian habitats show different scenarios where water availability has become an economical [22], social [21,23] and ecological [8,37] issue that needs to be addressed by managers and land owners.…”

“…Moreover, previous work suggests that water management practices could potentially modify the structure and function of key ecosystems in arid environments [3,[8][9][10]. In this sense, recent studies describe how groundwater depth restricts the establishment of riparian vegetation in watersheds located in arid environments of central Sonora (northwestern Mexico), particularly in vegetation on different sites along the river by field sampling.…”

Historical Analysis of Riparian Vegetation Change in Response to Shifting Management Objectives on the Middle Rio Grande

“…CART models have proven useful for creating general vegetation classifications from spectral data [46] and have been successfully used to model changes in arid land riparian vegetation [47,48]. Additionally, they are rule-based and use independent variables and datasets to determine the correct class membership for each pixel [46].…”