PE&RS September 2015 - page 750

stability of the local ecological system (Figure 2 forested areas
converted due to mining activities). The rapid development
of mining activities has other environmental and social effects
that are often negative (e.g., land degradation, biodiversity
loss, water and air pollution) and these factors often cause
conflicts between mining and non-mining land users. Local
governments must impose a more comprehensive reclamation
program targeted at reclaiming mine dumps and tailings and
recuperating mining areas.
A comparison between Plate 1d and 1e reveals an evident
increase in the extent of urbanized land cover in the study
area over the past decade. This may be attributable to the
fact that in mining regions, land used by mining companies
extended beyond their onsite operations, and the offsite
footprint (also referred as “spill-over effects”; Schueler
et al
.,
2011) of mining is also expanding. Our field studies show that
with the expansion of mining activities, workforce employ-
ment and housing, transportation infrastructure, stone prod-
uct processing, and sales factories have expanded in scope.
Similar findings have been reported for other mining regions
(Sonter
et al
., 2014), illustrating generality of this land change
trend across mining regions. Development of the offsite
footprint of mining is closely related to the direct footprint
of mining. Thus, when developing regional mining plans, it
is necessary to consider offsite land uses and their potential
effect on mining development.
Table 6 shows that the number of mine patches decreased
from 191 in 2001 to 158 in 2010, and that the average patch
area increased nearly five times from 2001 to 2010. This
denotes tendencies toward convergence in mining activities,
as evidenced by our field study results. In following its Tenth
Five-Year Plan, the government of Luoyuan County followed
its first general mineral resources plan, which was aimed
at adjusting mining industry structures, expanding mining
scales, emphasizing large-scale mining, and increasing the
number of medium and large-scale mines. The results of this
study confirm the effectiveness of this plan to some extent.
The study results may have been affected by the seasonal
characteristics of images used. Growing season images (from
June to August; ideal images are acquired in July) are essen-
tial when mapping mines and reclamation processes as such
images limit chances of confusion between bare soil areas and
mine or reclaimed mine areas. However, due to cloudy weath-
er patterns that characterize our study area in the summer, it
was difficult to acquire high-quality optical satellite imagery
for the growing season. On the other hand, change detection
analyses require the use of two images for the starting and
ending year for the same season. Two cloud-free satellite im-
ages for the growing seasons of 2001 and 2010 were not able
to be acquired. Therefore, images acquired for March 2001
and March 2010 were used for our land-cover change detec-
tion procedures, which may cause some error to the detection
results. Finally, it should be noted that the classification ac-
curacy assessment may have introduced errors due to our use
of imperfect 2001 validation datasets, which did not include
field-collected validation data. This constitutes another limi-
tation of this study.
Conclusions
High spatial resolution satellite remote sensing technologies
present unique strengths when used to examine land-cover
and change patterns in mining regions. In this study, image
segmentation, decision tree classifier, and retrospective ap-
proaches were used to detect land-cover changes in the mined
area of Luoyuan County based on pan-sharpened
ALOS
high-
resolution satellite imagery. The study results show that the
Luoyuan County mining area nearly quadrupled from 2001
to 2010, and that all newly expanded stone mine areas were
converted from forest. Reclaimed land in the mined region
was very limited. Digging areas only accounted for a smaller
portion of the overall stone mining area; however mine
dumps and tailings areas occupied the majority of the area, a
remarkable characteristic distinct from other mining regions.
It is clearly evident that urbanized land-cover in the study
area has expanded rapidly over the past decade, indicating
that in mining regions, land used by mining activities extend-
ed beyond their onsite operations, and the offsite footprint of
mining also expanded. It was also found that mining activi-
ties in Luoyuan County have undergone evident processes of
convergence over the past ten years.
Acknowledgments
The research has been supported by National Natural Sci-
ence Foundation of China (61401461), 135 Strategy Planning
of Institute of Remote Sensing and Digital Earth, the Chinese
Academy of Sciences, and National Ecological Environment
Changes decade remote sensing survey and assessment proj-
ect (STSN-10-03).
References
Baatz, M., and M. Schäpe, 2000. Multiresolution segmentation:
An optimization approach for high quality multi-scale image
segmentation,
Angewandte Geographische Informations-
verarbeitung XII, Wichmann Verlag, Karlsruhe
, pp. 12–23.
Benz, U.C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M.
Heynen, 2004. Multi-resolution, object-oriented fuzzy analysis of
remote sensing data for GIS-ready information,
ISPRS Journal of
Photogrammetry and Remote Sensing
, 58(3):239–258.
Bhaskaran, S., S. Paramananda, and M. Ramnarayan, 2010. Per-
pixel and object-oriented classification methods for mapping
urban features using Ikonos satellite data,
Applied Geography
,
30:650–665.
Breiman, L., J. Friedman, C. J. Stone, and R.A. Olshen, 1984.
Classification and Regression Trees
, CRC press.
Congalton, R.G., 1991. A review of assessing the accuracy of
classifications of remotely sensed data,
Remote Sensing of
Environment
, 37:35–46.
Congalton, R.G., and R.A. Mead, 1983. A quantitative method to
test for consistency and correctness in photointerpretation,
Photogrammetric Engineering & Remote Sensing
, 49:69−74.
Friedl, M.A., and C.E. Brodley, 1997. Decision tree classification
of land cover from remotely sensed data,
Remote Sensing of
Environment
, 61(3):399–409.
Hansen, M., R. Dubayah, and R. DeFries,1996. Classification trees:
An alternative to traditional land cover classifiers,
International
Journal of Remote Sensing
, 17(5):1075–1081.
Hodgson, M.E., J.R. Jensen, J.A. Tullis, K.D. Riordan, and C.M. Archer,
2003. Synergistic use of lidar and color aerial photography
for mapping urban parcel imperviousness,
Photogrammetric
Engineering & Remote Sensing
, 69(9):973–980.
Johnson, B.A., 2013. High-resolution urban land-cover classification
using a competitive multi-scale object-based approach,
Remote
Sensing Letters
, 4(2):131–140.
Kassouk, Z., J. Thouret, A. Gupta, A. Solikhin, and S.C. Liew, 2014.
Object-oriented classification of a high-spatial resolution SPOT
5 image for mapping geology and landforms of active volcanoes:
Semeru case study, Indonesia,
Geomorphology
, 221:18–33.
Laliberte, S.A., E.L. Fredrickson, and R. Albert, 2007. Combining
decision trees with hierarchical object-oriented image analysis
for mapping arid rangelands,
Photogrammetric Engineering &
Remote Sensing
, 73 (2):197-207.
Latifovic, R., K. Fytas, and J. Chen, 2005. Assessing land cover change
resulting from large surface mining development,
International
Journal of Applied Earth Observation and Geoinformation
,
7:29–48.
750
September 2015
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
679...,740,741,742,743,744,745,746,747,748,749 751,752,753,754
Powered by FlippingBook