10 Citations (Scopus)

Abstract

Since the 1970s, the Phoenix Active Management Area has experienced rapid urbanization, mostly through land conversions from agricultural lands to urban land use. Rapid urban expansion and population growth have placed unprecedented pressure on agricultural production in this region. Agricultural intensification, in particular double cropping, has been observed globally as an important response to the growing pressure on land. However, the intensification has a number of negative impacts on water quality, biodiversity, and biogeochemical cycles. Thus, quantifying the spatial pattern of cropping intensity is important for natural resource management. In this study, we developed an adaptive threshold approach to map cropping intensity using time series Landsat data and examined the spatiotemporal patterns of cropping intensity in the Phoenix Active Management Area from 1995 to 2010 at 5-year intervals. To map cropping intensity accurately, the adaptive threshold algorithm was designed specifically to address several issues caused by the complex cropping patterns in the study area. The adaptive threshold method has abilities to (1) distinguish true crop cycles from multiple false phenological peaks, (2) minimize errors caused by data noise and missing data, (3) identify alfalfa and interyear crops and to distinguish alfalfa from double crops, and (4) adapt to temporal profiles with different numbers of observations. The adaptive threshold algorithm is effective in characterizing cropping intensity with overall accuracies exceeding 97%. Results show that there is a dramatic decline in the area of total croplands (46.1%), single crops (46.3%), and double crops (43.4%) during the study period. There was a small conversion (1.9%) from single to double crop from 1995 to 2000, whereas a reverse conversion (1.3%) was observed from 2005 to 2010. Updated and accurate information on the spatial distribution of cropping intensity provide important implications on effective and sustainable cropping practices. In addition, joint investigation on cropping patterns and irrigation water use can shed light on future agricultural water demand, which is of paramount importance in this rapidly expanding arid region.

Original languageEnglish (US)
Pages (from-to)7263-7278
Number of pages16
JournalInternational Journal of Remote Sensing
Volume35
Issue number20
DOIs
StatePublished - Oct 7 2014

Fingerprint

Landsat
cropping practice
imagery
crop
alfalfa
double cropping
agricultural intensification
biogeochemical cycle
water demand
arid region
agricultural production
water use
population growth
resource management
urbanization
natural resource
agricultural land
irrigation
biodiversity
time series

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

@article{81b0bce2feb74e95aa5a12c19263b133,
title = "Characterizing changes in cropping patterns using sequential Landsat imagery: an adaptive threshold approach and application to Phoenix, Arizona",
abstract = "Since the 1970s, the Phoenix Active Management Area has experienced rapid urbanization, mostly through land conversions from agricultural lands to urban land use. Rapid urban expansion and population growth have placed unprecedented pressure on agricultural production in this region. Agricultural intensification, in particular double cropping, has been observed globally as an important response to the growing pressure on land. However, the intensification has a number of negative impacts on water quality, biodiversity, and biogeochemical cycles. Thus, quantifying the spatial pattern of cropping intensity is important for natural resource management. In this study, we developed an adaptive threshold approach to map cropping intensity using time series Landsat data and examined the spatiotemporal patterns of cropping intensity in the Phoenix Active Management Area from 1995 to 2010 at 5-year intervals. To map cropping intensity accurately, the adaptive threshold algorithm was designed specifically to address several issues caused by the complex cropping patterns in the study area. The adaptive threshold method has abilities to (1) distinguish true crop cycles from multiple false phenological peaks, (2) minimize errors caused by data noise and missing data, (3) identify alfalfa and interyear crops and to distinguish alfalfa from double crops, and (4) adapt to temporal profiles with different numbers of observations. The adaptive threshold algorithm is effective in characterizing cropping intensity with overall accuracies exceeding 97{\%}. Results show that there is a dramatic decline in the area of total croplands (46.1{\%}), single crops (46.3{\%}), and double crops (43.4{\%}) during the study period. There was a small conversion (1.9{\%}) from single to double crop from 1995 to 2000, whereas a reverse conversion (1.3{\%}) was observed from 2005 to 2010. Updated and accurate information on the spatial distribution of cropping intensity provide important implications on effective and sustainable cropping practices. In addition, joint investigation on cropping patterns and irrigation water use can shed light on future agricultural water demand, which is of paramount importance in this rapidly expanding arid region.",
author = "Chao Fan and Baojuan Zheng and Soe Myint and Rimjhim Aggarwal",
year = "2014",
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language = "English (US)",
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T1 - Characterizing changes in cropping patterns using sequential Landsat imagery

T2 - an adaptive threshold approach and application to Phoenix, Arizona

AU - Fan, Chao

AU - Zheng, Baojuan

AU - Myint, Soe

AU - Aggarwal, Rimjhim

PY - 2014/10/7

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