农业工程学报
農業工程學報
농업공정학보
2015年
16期
251-257
,共7页
李晶%Zipper Carl E.%李松%Donovan Patricia F.%Wynne Randolph H.%Oliphant Adam J.%夏清
李晶%Zipper Carl E.%李鬆%Donovan Patricia F.%Wynne Randolph H.%Oliphant Adam J.%夏清
리정%Zipper Carl E.%리송%Donovan Patricia F.%Wynne Randolph H.%Oliphant Adam J.%하청
土地复垦%煤矿%遥感%露天煤矿%归一化植被指数%时序分析
土地複墾%煤礦%遙感%露天煤礦%歸一化植被指數%時序分析
토지복은%매광%요감%로천매광%귀일화식피지수%시서분석
land reclamation%coal mines%remote sensing%surface coal-mine area%normalized difference vegetation index (NDVI)%time-series analysis
露天煤矿区是人类活动强扰动地区之一。该文以阿巴拉契亚煤田区韦恩县为研究区域,应用遥感时序分析法分析了像元尺度的土地损毁和复垦过程特征。得出结论:1984-2010年间,韦兹县露天开采扰动区域占采矿权范围的45.80%,其中植被恢复区域占开采范围的66.45%,开采时间越早,植被恢复像元比例越高;开采造成的地表无植被覆盖期时长中位数为6 a,均值为7 a;已充分复垦的区域,NDVI值恢复至采前水平的加权平均时长为12 a。基于像元变化轨迹的研究,除揭示土地损毁-复垦过程特征外,能较好地反映空间异质性,可以为土地复垦管理和相关政策决策提供科学依据。
露天煤礦區是人類活動彊擾動地區之一。該文以阿巴拉契亞煤田區韋恩縣為研究區域,應用遙感時序分析法分析瞭像元呎度的土地損燬和複墾過程特徵。得齣結論:1984-2010年間,韋玆縣露天開採擾動區域佔採礦權範圍的45.80%,其中植被恢複區域佔開採範圍的66.45%,開採時間越早,植被恢複像元比例越高;開採造成的地錶無植被覆蓋期時長中位數為6 a,均值為7 a;已充分複墾的區域,NDVI值恢複至採前水平的加權平均時長為12 a。基于像元變化軌跡的研究,除揭示土地損燬-複墾過程特徵外,能較好地反映空間異質性,可以為土地複墾管理和相關政策決策提供科學依據。
로천매광구시인류활동강우동지구지일。해문이아파랍계아매전구위은현위연구구역,응용요감시서분석법분석료상원척도적토지손훼화복은과정특정。득출결론:1984-2010년간,위자현로천개채우동구역점채광권범위적45.80%,기중식피회복구역점개채범위적66.45%,개채시간월조,식피회복상원비례월고;개채조성적지표무식피복개기시장중위수위6 a,균치위7 a;이충분복은적구역,NDVI치회복지채전수평적가권평균시장위12 a。기우상원변화궤적적연구,제게시토지손훼-복은과정특정외,능교호지반영공간이질성,가이위토지복은관리화상관정책결책제공과학의거。
Open-pit coal mines are among the most drastic anthropogenic land disturbances. Using Wise County in the USA’s Appalachian coal field as the study area, this paper evaluates the mine land disturbance and reclamation process over a 27-year period by conducting time-series analysis of multispectral remote-sensing data at the pixel scale. Twenty TM/ETM images obtained by the Landsat satellites, with 30 m spatial resolution, are treated as a multiple-year chronosequence. Polygonal vector files defining mining permitted areas and 6 high-resolution aerial images are used as auxiliary data;and the normalized difference vegetation index (NDVI) is used as a vegetative cover indicator. The methodology and study process include 3 steps. First, training data are prepared and multispectral image data are preprocessed. Data preprocessing includes band stacking, extracting the study area as image subsets, masking of cloud, cloud shadow and water, and computing NDVI based on each study-area pixel in each image. After the training data are generated, they are used to identify the NDVI thresholds for separating bare-ground from vegetated pixels, and ever-mined pixels from those un-mined. These separations are performed by visually inspecting each individual TM/ETM image displayed through a combination of the band of 2, 3 and 4 while referencing all available high-resolution aerial images and polygon vector files defining mining permitted area. Second, each pixel’s time-series NDVI trajectory is constructed and analyzed. Based on individual inspection of training points’ NDVI trajectory and qualitative trajectory classification, the characteristic parameters, including the NDVI maximum, the NDVI minimum, the difference of NDVI maxima before and after mining, the bare-ground threshold and ever-mined threshold, are designed, computed and used to identify whether the pixel is ever mined, revegetated or revegetated to the land cover level before mining. The relationship between NDVI minimal values and ever-mined thresholds for each study-area pixel over the full time series is computed and analyzed to determine if the pixel has been mined and, if so, the date of initial disturbance. For mined areas, the relationship between NDVI trajectory and bare-ground threshold for each point in time series after mining is analyzed to determinate if excavated land has been revegetated. For those revegetated pixels, the difference of NDVI maxima before and after mining is computed in order to find if it has been fully revegetated. Thirdly, by the data analysis function of database software and the spatial analysis function of ArcGIS software, the distribution of different mining-reclamation types is analyzed. Except for un-disturbed pixel, each mined pixel was classified for each year;such classes include the mined and un-vegetated during the whole observation period; the mined and un-revegetated after mining; the mined and revegetated without fully restored to the level before mining; and the mined and revegetated with vegetative cover fully restored to the level before mining. Time durations of non-vegetated status and revegetation after initial mining disturbance are also computed for fully reclaimed pixels. The conclusions are as follows:1) During 1984-2010, the mining disturbs 45.80%mining permitted areas;66.45%mined land is re-vegetated, which includes 38.42%mined land with fully restored vegetative cover and 28.03%mined land revegetated but without fully restored vegetative cover; 2) The fraction of mined lands with fully restored vegetative cover varies positively with mining-site age, which is found to be 85.54%, 13.92%, and 0.52% lands during the periods of 1984-1993, 1994-2003 and 2004-2010, respectively; 3) The average time for non-vegetated status and vegetated status prior to full cover restoration on the mined lands is 6 and 12 years, respectively; while the 25th, 50th, 75th and 90th percentiles for non-vegetated status are 4, 6, 10 and 16 years, respectively;4) For full restoration of NDVI-derived vegetative cover to the level before mining, the time is 12 years on average. Study results demonstrate that the analysis of pixel-scale NDVI trajectories can reconstruct mining disturbance and land reclamation history, and can disclose its spatial heterogeneity over the broad expanse of space and time. Hence, such analyses can provide a scientific basis for land reclamation management and policy decisions. We note, however, that restoration of the NDVI to the level before mining is not a reliable indicator of reclamation success when viewed alone, although it is easily measured with satellite remote-sensing data. In order to interpret NDVI-derived vegetative cover data as indicators of reclamation success, other factors such as vegetation type and land productivity should be also considered.